From a8eff4c683a0d8a81fa7cb8be2e1a683af053ce6 Mon Sep 17 00:00:00 2001
From: cephi <cephi_sui@protonmail.com>
Date: Wed, 18 Dec 2024 14:28:06 -0500
Subject: [PATCH] as-caida coo

---
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..6f81294
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..338089c
--- /dev/null
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..74888e6
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_020.json
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+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 6157, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.447131633758545, "TIME_S_1KI": 1.6967892859767004, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 273.603369216919, "W": 19.294003834671976, "J_1KI": 44.4377731390156, "W_1KI": 3.13366961745525, "W_D": 4.2190038346719785, "J_D": 59.82862208366404, "W_D_1KI": 0.685236939202855, "J_D_1KI": 0.1112939644636763}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..a4224cf
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_020.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_020.mtx -c 16']
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+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..1904469
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5667, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.373903036117554, "TIME_S_1KI": 1.8305810898389896, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 279.4291752243042, "W": 19.595129066747692, "J_1KI": 49.308130443674635, "W_1KI": 3.457760555275753, "W_D": 4.856129066747693, "J_D": 69.24905343985554, "W_D_1KI": 0.8569135462762825, "J_D_1KI": 0.15121114280506132}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..9349377
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_030.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_030.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.2259833812713623}
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+tensor(indices=tensor([[ 1809, 21783,   106,  ...,  7018,   160,   882],
+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=86850, layout=torch.sparse_coo)
+tensor([0.2384, 0.4578, 0.3485,  ..., 0.8138, 0.6026, 0.3518])
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+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
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+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..f2d2b41
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5490, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.40391230583191, "TIME_S_1KI": 1.8950659937763041, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 265.913395280838, "W": 18.629131570080315, "J_1KI": 48.43595542456066, "W_1KI": 3.3932844389945926, "W_D": 3.6391315700803144, "J_D": 51.9451928305626, "W_D_1KI": 0.6628654954608952, "J_D_1KI": 0.12074052740635614}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..e9d9bd3
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_040.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_040.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.22439122200012207}
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+tensor(indices=tensor([[  106,   329,  1040,  ..., 10144,   882, 16085],
+                       [    2,     2,     2,  ..., 31371, 31373, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=89658, layout=torch.sparse_coo)
+tensor([0.1454, 0.9169, 0.2081,  ..., 0.7984, 0.1283, 0.7510])
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+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 8.948421478271484}
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+                       [    2,     2,     2,  ..., 31371, 31373, 31378]]),
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+       size=(31379, 31379), nnz=89658, layout=torch.sparse_coo)
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..8abe402
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5417, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.343466758728027, "TIME_S_1KI": 1.9094455895750466, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 281.49025360107413, "W": 19.728120633755104, "J_1KI": 51.96423363505153, "W_1KI": 3.641890462203268, "W_D": 4.954120633755103, "J_D": 70.68776085948934, "W_D_1KI": 0.9145506061944071, "J_D_1KI": 0.16882972239143568}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..805c174
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_050.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_050.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.21228814125061035}
+
+tensor(indices=tensor([[ 5326,   106,   329,  ...,   882,  2232, 16085],
+                       [    0,     2,     2,  ..., 31373, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=90392, layout=torch.sparse_coo)
+tensor([0.3434, 0.0026, 0.8832,  ..., 0.4180, 0.8589, 0.1973])
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 4946 -m matrices/as-caida_pruned/as-caida_G_050.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 9.58672308921814}
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+                       [    0,     2,     2,  ..., 31373, 31378, 31378]]),
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+tensor([0.6652, 0.6138, 0.1566,  ..., 0.2394, 0.6281, 0.2308])
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+tensor(indices=tensor([[ 5326,   106,   329,  ...,   882,  2232, 16085],
+                       [    0,     2,     2,  ..., 31373, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
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+tensor([0.6679, 0.0443, 0.6559,  ..., 0.4078, 0.6922, 0.7735])
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..9abd535
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5164, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.403054475784302, "TIME_S_1KI": 2.0145341742417315, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 274.7938276672363, "W": 19.323381225072442, "J_1KI": 53.21336709280331, "W_1KI": 3.741940593546174, "W_D": 4.4473812250724425, "J_D": 63.24529313468935, "W_D_1KI": 0.8612279676747565, "J_D_1KI": 0.16677536167210621}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..12ba257
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_060.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_060.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.2032930850982666}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160,   882],
+                       [    2,     2,     2,  ..., 31355, 31360, 31373]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=94180, layout=torch.sparse_coo)
+tensor([0.6402, 0.6620, 0.2797,  ..., 0.7961, 0.8098, 0.7007])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_060
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+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
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+Density: 9.564901186217454e-05
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+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.403054475784302}
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+                       [    2,     2,     2,  ..., 31355, 31360, 31373]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=94180, layout=torch.sparse_coo)
+tensor([0.7965, 0.8169, 0.0055,  ..., 0.9599, 0.2086, 0.2807])
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+                       [    2,     2,     2,  ..., 31355, 31360, 31373]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=94180, layout=torch.sparse_coo)
+tensor([0.7965, 0.8169, 0.0055,  ..., 0.9599, 0.2086, 0.2807])
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..0cd8ade
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 6085, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.561926126480103, "TIME_S_1KI": 1.7357314916154647, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 269.13966796874996, "W": 18.871496391148096, "J_1KI": 44.230019386811826, "W_1KI": 3.1013141152256525, "W_D": 3.663496391148094, "J_D": 52.24769577789302, "W_D_1KI": 0.6020536386438938, "J_D_1KI": 0.09894061440326932}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..a8aa2b6
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_070.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_070.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.2066023349761963}
+
+tensor(indices=tensor([[  106,   329,  1040,  ..., 16263,  2242,  2242],
+                       [    2,     2,     2,  ..., 31240, 31283, 31284]]),
+       values=tensor([3., 3., 3.,  ..., 1., 3., 3.]),
+       size=(31379, 31379), nnz=78684, layout=torch.sparse_coo)
+tensor([0.8673, 0.7061, 0.4866,  ..., 0.0493, 0.1107, 0.0552])
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+Matrix: as-caida_G_070
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 5082 -m matrices/as-caida_pruned/as-caida_G_070.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 8.768118619918823}
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+                       [    2,     2,     2,  ..., 31240, 31283, 31284]]),
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+                       [    2,     2,     2,  ..., 31240, 31283, 31284]]),
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..40ffd8a
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5047, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.559667587280273, "TIME_S_1KI": 2.092266215034728, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 269.87574887275696, "W": 18.935720713656302, "J_1KI": 53.47250819749494, "W_1KI": 3.7518765035974444, "W_D": 3.7907207136563006, "J_D": 54.026123791933024, "W_D_1KI": 0.7510839535677235, "J_D_1KI": 0.14881790243069615}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..ad8dc08
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_080.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_080.mtx -c 16']
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+
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+                       [    0,     1,     2,  ..., 31373, 31373, 31378]]),
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+                       [    0,     1,     2,  ..., 31373, 31373, 31378]]),
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..99477b4
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4841, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.36881422996521, "TIME_S_1KI": 2.1418744536180974, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 286.9453951072693, "W": 20.105071747721635, "J_1KI": 59.27399196597176, "W_1KI": 4.1530823688745375, "W_D": 5.071071747721632, "J_D": 72.37580171442029, "W_D_1KI": 1.0475256657140326, "J_D_1KI": 0.2163862147725744}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..21ef95b
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_090.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_090.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.21688628196716309}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   882, 31211, 12170],
+                       [    2,     2,     2,  ..., 31373, 31376, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=100924, layout=torch.sparse_coo)
+tensor([0.6044, 0.9052, 0.9070,  ..., 0.7470, 0.2235, 0.3476])
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..b4939ce
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4694, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.377132654190063, "TIME_S_1KI": 2.2107227639944744, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 270.65173756599427, "W": 19.038925804006492, "J_1KI": 57.65908341840525, "W_1KI": 4.056013166597038, "W_D": 4.348925804006491, "J_D": 61.82304282903671, "W_D_1KI": 0.9264861107811017, "J_D_1KI": 0.19737667464446138}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..01bf8c3
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_100.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_100.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.2236778736114502}
+
+tensor(indices=tensor([[  106,   329,  1040,  ..., 25970,  5128, 12170],
+                       [    2,     2,     2,  ..., 31373, 31377, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=102888, layout=torch.sparse_coo)
+tensor([0.9725, 0.4397, 0.5107,  ..., 0.9228, 0.6104, 0.9148])
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diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..e0b4bcf
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4747, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.490605115890503, "TIME_S_1KI": 2.20994419968201, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 265.2582043838501, "W": 18.664908076245766, "J_1KI": 55.879124580545636, "W_1KI": 3.9319376608901977, "W_D": 3.8899080762457654, "J_D": 55.28181694269181, "W_D_1KI": 0.8194455606163399, "J_D_1KI": 0.17262388047531912}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..09882e7
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_110.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_110.mtx -c 16']
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   882,  2616, 12170],
+                       [    2,     2,     2,  ..., 31373, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=104846, layout=torch.sparse_coo)
+tensor([0.8491, 0.3135, 0.1444,  ..., 0.8000, 0.8444, 0.5839])
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+Matrix: as-caida_G_110
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   882,  2616, 12170],
+                       [    2,     2,     2,  ..., 31373, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=104846, layout=torch.sparse_coo)
+tensor([0.0201, 0.8500, 0.3074,  ..., 0.9892, 0.1776, 0.1910])
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+Matrix: as-caida_G_110
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+Rows: 31379
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+Density: 0.0001064813792493263
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+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.490605115890503}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   882,  2616, 12170],
+                       [    2,     2,     2,  ..., 31373, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=104846, layout=torch.sparse_coo)
+tensor([0.7081, 0.3802, 0.2336,  ..., 0.4048, 0.3605, 0.4195])
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+Density: 0.0001064813792493263
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   882,  2616, 12170],
+                       [    2,     2,     2,  ..., 31373, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=104846, layout=torch.sparse_coo)
+tensor([0.7081, 0.3802, 0.2336,  ..., 0.4048, 0.3605, 0.4195])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_110
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+Size: 984641641
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+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4747, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 10.490605115890503, 'TIME_S_1KI': 2.20994419968201, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 265.2582043838501, 'W': 18.664908076245766, 'J_1KI': 55.879124580545636, 'W_1KI': 3.9319376608901977, 'W_D': 3.8899080762457654, 'J_D': 55.28181694269181, 'W_D_1KI': 0.8194455606163399, 'J_D_1KI': 0.17262388047531912}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..e4a3160
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4652, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.588041067123413, "TIME_S_1KI": 2.2760191459852566, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 291.5600438308715, "W": 20.506065841615797, "J_1KI": 62.67412808058287, "W_1KI": 4.408010714018873, "W_D": 5.508065841615798, "J_D": 78.31496936607354, "W_D_1KI": 1.1840210321616076, "J_D_1KI": 0.25451870854720715}
diff --git a/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..fe53824
--- /dev/null
+++ b/pytorch/output_as-caida_16core/altra_16_coo_10_10_10_as-caida_G_120.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.2656247615814209}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.3456, 0.4102, 0.6456,  ..., 0.5106, 0.1829, 0.4538])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.2656247615814209 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 3952 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 8.919629573822021}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.7266, 0.0356, 0.3831,  ..., 0.4998, 0.1232, 0.2973])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 8.919629573822021 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 4652 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.588041067123413}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.1991, 0.7050, 0.2626,  ..., 0.8268, 0.9165, 0.3589])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.588041067123413 seconds
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.1991, 0.7050, 0.2626,  ..., 0.8268, 0.9165, 0.3589])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.588041067123413 seconds
+
+[16.32, 16.48, 16.52, 16.52, 16.52, 16.64, 16.84, 16.76, 16.8, 16.8]
+[16.64, 16.56, 19.76, 21.56, 23.76, 24.32, 24.32, 25.56, 23.2, 22.92, 21.76, 22.08, 21.84, 21.72]
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+299.96
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+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4652, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.588041067123413, 'TIME_S_1KI': 2.2760191459852566, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 291.5600438308715, 'W': 20.506065841615797, 'J_1KI': 62.67412808058287, 'W_1KI': 4.408010714018873, 'W_D': 5.508065841615798, 'J_D': 78.31496936607354, 'W_D_1KI': 1.1840210321616076, 'J_D_1KI': 0.25451870854720715}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.json
deleted file mode 100644
index cc64c57..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5806, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.171998977661133, "TIME_S_1KI": 1.7519805335275807, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 312.83616289138786, "W": 23.626422472883572, "J_1KI": 53.88152995029071, "W_1KI": 4.069311483445328, "W_D": 5.210422472883575, "J_D": 68.9909179153442, "W_D_1KI": 0.8974203363561101, "J_D_1KI": 0.15456774653050467}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.output
deleted file mode 100644
index 04c69bd..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_005.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.1808183193206787}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.7415, 0.8054, 0.6431,  ..., 0.7043, 0.2095, 0.2852])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 0.1808183193206787 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5806 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.171998977661133}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.0310, 0.3080, 0.7594,  ..., 0.0941, 0.5225, 0.9795])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.171998977661133 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.0310, 0.3080, 0.7594,  ..., 0.0941, 0.5225, 0.9795])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.171998977661133 seconds
-
-[20.56, 20.64, 20.44, 20.48, 20.48, 20.44, 20.6, 20.64, 20.64, 20.76]
-[20.8, 20.88, 21.04, 25.72, 26.8, 29.92, 30.76, 28.4, 26.88, 24.6, 24.56, 24.56, 24.72]
-13.240945100784302
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5806, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.171998977661133, 'TIME_S_1KI': 1.7519805335275807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.83616289138786, 'W': 23.626422472883572}
-[20.56, 20.64, 20.44, 20.48, 20.48, 20.44, 20.6, 20.64, 20.64, 20.76, 20.2, 20.36, 20.24, 20.2, 20.24, 20.36, 20.4, 20.56, 20.6, 20.48]
-368.31999999999994
-18.415999999999997
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5806, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.171998977661133, 'TIME_S_1KI': 1.7519805335275807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.83616289138786, 'W': 23.626422472883572, 'J_1KI': 53.88152995029071, 'W_1KI': 4.069311483445328, 'W_D': 5.210422472883575, 'J_D': 68.9909179153442, 'W_D_1KI': 0.8974203363561101, 'J_D_1KI': 0.15456774653050467}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.json
deleted file mode 100644
index 9fa31bf..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5449, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.42112112045288, "TIME_S_1KI": 1.9124832300335624, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.5276795768738, "W": 22.970903604955094, "J_1KI": 59.924330992269006, "W_1KI": 4.21561820608462, "W_D": 4.577903604955093, "J_D": 65.0741593434811, "W_D_1KI": 0.8401364663158548, "J_D_1KI": 0.15418177029103594}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.output
deleted file mode 100644
index e3feb4a..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_015.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.24444031715393066}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.0041, 0.0059, 0.8299,  ..., 0.3077, 0.8545, 0.8513])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 0.24444031715393066 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4295 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 8.275424003601074}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.5048, 0.0128, 0.9259,  ..., 0.5690, 0.7343, 0.9731])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 8.275424003601074 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5449 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.42112112045288}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.2882, 0.9342, 0.0969,  ..., 0.6573, 0.4161, 0.3369])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.42112112045288 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.2882, 0.9342, 0.0969,  ..., 0.6573, 0.4161, 0.3369])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.42112112045288 seconds
-
-[20.72, 20.48, 20.48, 20.32, 20.36, 20.36, 20.28, 20.36, 20.36, 20.24]
-[20.4, 20.36, 20.28, 25.0, 25.96, 28.32, 29.16, 26.92, 25.8, 24.32, 24.4, 24.24, 24.24, 24.24]
-14.21483826637268
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5449, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.42112112045288, 'TIME_S_1KI': 1.9124832300335624, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.5276795768738, 'W': 22.970903604955094}
-[20.72, 20.48, 20.48, 20.32, 20.36, 20.36, 20.28, 20.36, 20.36, 20.24, 20.48, 20.36, 20.32, 20.2, 20.16, 20.6, 20.76, 20.72, 20.8, 20.44]
-367.86
-18.393
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5449, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.42112112045288, 'TIME_S_1KI': 1.9124832300335624, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.5276795768738, 'W': 22.970903604955094, 'J_1KI': 59.924330992269006, 'W_1KI': 4.21561820608462, 'W_D': 4.577903604955093, 'J_D': 65.0741593434811, 'W_D_1KI': 0.8401364663158548, 'J_D_1KI': 0.15418177029103594}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.json
deleted file mode 100644
index 8393438..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 5009, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.436067342758179, "TIME_S_1KI": 2.0834632347291233, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 339.9102258396148, "W": 23.870320724339745, "J_1KI": 67.85989735268812, "W_1KI": 4.7654862695827, "W_D": 5.56032072433975, "J_D": 79.17823539018632, "W_D_1KI": 1.110066026021112, "J_D_1KI": 0.22161429946518504}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.output
deleted file mode 100644
index ee06650..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_025.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.2360525131225586}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.8761, 0.0368, 0.8631,  ..., 0.6340, 0.9685, 0.1396])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 0.2360525131225586 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4448 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 9.323699712753296}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.4095, 0.9128, 0.5370,  ..., 0.1298, 0.9549, 0.0765])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 9.323699712753296 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5009 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.436067342758179}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.6896, 0.4674, 0.9391,  ..., 0.8690, 0.1471, 0.0542])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.436067342758179 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.6896, 0.4674, 0.9391,  ..., 0.8690, 0.1471, 0.0542])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.436067342758179 seconds
-
-[20.2, 20.32, 20.52, 20.52, 20.64, 20.64, 20.64, 20.64, 20.64, 20.52]
-[20.44, 20.72, 20.72, 23.84, 26.4, 29.12, 30.32, 31.08, 27.36, 26.24, 24.52, 25.04, 25.36, 25.64]
-14.239868402481079
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5009, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.436067342758179, 'TIME_S_1KI': 2.0834632347291233, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.9102258396148, 'W': 23.870320724339745}
-[20.2, 20.32, 20.52, 20.52, 20.64, 20.64, 20.64, 20.64, 20.64, 20.52, 20.2, 20.16, 20.16, 20.16, 20.2, 20.24, 20.16, 20.2, 19.92, 19.96]
-366.19999999999993
-18.309999999999995
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 5009, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.436067342758179, 'TIME_S_1KI': 2.0834632347291233, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.9102258396148, 'W': 23.870320724339745, 'J_1KI': 67.85989735268812, 'W_1KI': 4.7654862695827, 'W_D': 5.56032072433975, 'J_D': 79.17823539018632, 'W_D_1KI': 1.110066026021112, 'J_D_1KI': 0.22161429946518504}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.json
deleted file mode 100644
index b64425d..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4906, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.369710445404053, "TIME_S_1KI": 2.113679259152885, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 320.3935369491577, "W": 22.512475730235575, "J_1KI": 65.30646900716626, "W_1KI": 4.5887639075082705, "W_D": 4.301475730235577, "J_D": 61.217834938526146, "W_D_1KI": 0.8767785834153234, "J_D_1KI": 0.17871556938755065}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.output
deleted file mode 100644
index 8c69e76..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_035.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.28984951972961426}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.2495, 0.4746, 0.6508,  ..., 0.6030, 0.3808, 0.6963])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 0.28984951972961426 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3622 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 7.750367879867554}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.1862, 0.1434, 0.8787,  ..., 0.7704, 0.8925, 0.8878])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 7.750367879867554 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4906 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.369710445404053}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.7972, 0.7657, 0.0835,  ..., 0.8008, 0.2416, 0.9619])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.369710445404053 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.7972, 0.7657, 0.0835,  ..., 0.8008, 0.2416, 0.9619])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.369710445404053 seconds
-
-[20.08, 19.84, 19.84, 19.92, 19.96, 20.04, 20.08, 20.2, 20.2, 20.2]
-[20.36, 20.32, 21.32, 22.76, 24.92, 24.92, 25.72, 26.36, 26.24, 25.64, 24.64, 24.68, 24.72, 24.56]
-14.231821537017822
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4906, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.369710445404053, 'TIME_S_1KI': 2.113679259152885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 320.3935369491577, 'W': 22.512475730235575}
-[20.08, 19.84, 19.84, 19.92, 19.96, 20.04, 20.08, 20.2, 20.2, 20.2, 20.36, 20.4, 20.56, 20.4, 20.6, 20.6, 20.6, 20.28, 20.12, 20.52]
-364.21999999999997
-18.211
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4906, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.369710445404053, 'TIME_S_1KI': 2.113679259152885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 320.3935369491577, 'W': 22.512475730235575, 'J_1KI': 65.30646900716626, 'W_1KI': 4.5887639075082705, 'W_D': 4.301475730235577, 'J_D': 61.217834938526146, 'W_D_1KI': 0.8767785834153234, 'J_D_1KI': 0.17871556938755065}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.json
deleted file mode 100644
index 332fb37..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4905, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.552767515182495, "TIME_S_1KI": 2.1514306860718646, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.58112309455873, "W": 23.14675123767438, "J_1KI": 66.9890159214187, "W_1KI": 4.719011465377039, "W_D": 4.7337512376743796, "J_D": 67.19825526070599, "W_D_1KI": 0.9650868986084362, "J_D_1KI": 0.196755738758091}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.output
deleted file mode 100644
index 88c6f0e..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_045.output
+++ /dev/null
@@ -1,105 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.2738626003265381}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.8777, 0.5124, 0.0822,  ..., 0.9706, 0.3708, 0.5874])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 0.2738626003265381 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3834 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 8.640145778656006}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.9169, 0.5590, 0.9513,  ..., 0.6480, 0.9706, 0.8048])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 8.640145778656006 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4659 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 9.972679376602173}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.6180, 0.9542, 0.9412,  ..., 0.9357, 0.2218, 0.1163])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 9.972679376602173 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4905 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.552767515182495}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.7047, 0.9313, 0.0358,  ..., 0.9576, 0.8194, 0.2072])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.552767515182495 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.7047, 0.9313, 0.0358,  ..., 0.9576, 0.8194, 0.2072])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.552767515182495 seconds
-
-[20.24, 20.28, 20.32, 20.28, 20.64, 20.72, 20.8, 20.96, 20.88, 20.6]
-[20.6, 20.52, 20.28, 22.6, 24.08, 26.32, 27.4, 28.32, 27.04, 26.4, 25.72, 25.72, 25.88, 25.88]
-14.195561170578003
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4905, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.552767515182495, 'TIME_S_1KI': 2.1514306860718646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.58112309455873, 'W': 23.14675123767438}
-[20.24, 20.28, 20.32, 20.28, 20.64, 20.72, 20.8, 20.96, 20.88, 20.6, 20.8, 20.4, 20.12, 19.96, 20.16, 20.24, 20.4, 20.6, 20.48, 20.4]
-368.26
-18.413
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4905, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.552767515182495, 'TIME_S_1KI': 2.1514306860718646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.58112309455873, 'W': 23.14675123767438, 'J_1KI': 66.9890159214187, 'W_1KI': 4.719011465377039, 'W_D': 4.7337512376743796, 'J_D': 67.19825526070599, 'W_D_1KI': 0.9650868986084362, 'J_D_1KI': 0.196755738758091}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.json
deleted file mode 100644
index ddd2c19..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4732, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.468129873275757, "TIME_S_1KI": 2.212199888688875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 340.41829671859733, "W": 23.891063458309905, "J_1KI": 71.93962314425134, "W_1KI": 5.048829978510124, "W_D": 5.390063458309907, "J_D": 76.80178092050545, "W_D_1KI": 1.1390666649006567, "J_D_1KI": 0.24071569418864255}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.output
deleted file mode 100644
index d8de122..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_055.output
+++ /dev/null
@@ -1,86 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.2837095260620117}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.1141, 0.9590, 0.8822,  ..., 0.4586, 0.3032, 0.3922])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 0.2837095260620117 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3700 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 8.209553480148315}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([1.3531e-01, 9.2471e-01, 3.7424e-01,  ..., 8.7339e-04, 4.3447e-01,
-        4.7205e-01])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 8.209553480148315 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4732 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.468129873275757}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.1672, 0.2165, 0.1528,  ..., 0.1782, 0.4621, 0.9393])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.468129873275757 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.1672, 0.2165, 0.1528,  ..., 0.1782, 0.4621, 0.9393])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.468129873275757 seconds
-
-[20.4, 20.44, 20.52, 20.76, 20.6, 20.84, 20.92, 20.84, 20.6, 20.6]
-[20.6, 20.72, 20.72, 24.32, 26.2, 29.28, 30.44, 28.2, 28.0, 25.56, 25.68, 25.84, 25.84, 25.8]
-14.24877119064331
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.468129873275757, 'TIME_S_1KI': 2.212199888688875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 340.41829671859733, 'W': 23.891063458309905}
-[20.4, 20.44, 20.52, 20.76, 20.6, 20.84, 20.92, 20.84, 20.6, 20.6, 20.64, 20.36, 20.52, 20.52, 20.48, 20.36, 20.36, 20.32, 20.44, 20.64]
-370.02
-18.500999999999998
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.468129873275757, 'TIME_S_1KI': 2.212199888688875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 340.41829671859733, 'W': 23.891063458309905, 'J_1KI': 71.93962314425134, 'W_1KI': 5.048829978510124, 'W_D': 5.390063458309907, 'J_D': 76.80178092050545, 'W_D_1KI': 1.1390666649006567, 'J_D_1KI': 0.24071569418864255}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.json
deleted file mode 100644
index 4edf738..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4558, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.40420150756836, "TIME_S_1KI": 2.2826242886284245, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.84002380371095, "W": 23.226044906428264, "J_1KI": 72.3650776225781, "W_1KI": 5.095665841691151, "W_D": 4.6110449064282655, "J_D": 65.48283049583436, "W_D_1KI": 1.0116377591988295, "J_D_1KI": 0.2219477312853948}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.output
deleted file mode 100644
index bfc13d8..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_065.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.2867097854614258}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.2765, 0.7404, 0.5834,  ..., 0.0305, 0.2184, 0.6277])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 0.2867097854614258 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3662 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 8.434634447097778}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.9579, 0.2280, 0.6543,  ..., 0.1974, 0.2729, 0.8108])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 8.434634447097778 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4558 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.40420150756836}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.9497, 0.5746, 0.8058,  ..., 0.6531, 0.4871, 0.2425])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.40420150756836 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.9497, 0.5746, 0.8058,  ..., 0.6531, 0.4871, 0.2425])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.40420150756836 seconds
-
-[20.64, 20.64, 20.6, 20.56, 20.4, 20.2, 20.16, 20.24, 20.44, 20.48]
-[20.4, 20.4, 20.32, 24.04, 25.44, 28.52, 29.28, 29.6, 26.24, 25.04, 24.36, 24.48, 24.64, 24.64]
-14.201299667358398
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4558, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.40420150756836, 'TIME_S_1KI': 2.2826242886284245, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.84002380371095, 'W': 23.226044906428264}
-[20.64, 20.64, 20.6, 20.56, 20.4, 20.2, 20.16, 20.24, 20.44, 20.48, 20.76, 20.76, 20.84, 20.72, 20.92, 21.16, 21.12, 20.96, 21.04, 21.2]
-372.29999999999995
-18.615
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4558, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.40420150756836, 'TIME_S_1KI': 2.2826242886284245, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.84002380371095, 'W': 23.226044906428264, 'J_1KI': 72.3650776225781, 'W_1KI': 5.095665841691151, 'W_D': 4.6110449064282655, 'J_D': 65.48283049583436, 'W_D_1KI': 1.0116377591988295, 'J_D_1KI': 0.2219477312853948}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.json
deleted file mode 100644
index dda358e..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4325, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.857763290405273, "TIME_S_1KI": 2.5104655006717396, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.7423639297486, "W": 23.58156237869744, "J_1KI": 77.62829223809216, "W_1KI": 5.45238436501675, "W_D": 5.256562378697442, "J_D": 74.84027779102334, "W_D_1KI": 1.2153901453635703, "J_D_1KI": 0.28101506251180813}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.output
deleted file mode 100644
index d65e9d8..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_075.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_075.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.24277067184448242}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.0081, 0.1025, 0.4852,  ..., 0.3080, 0.4481, 0.5761])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 0.24277067184448242 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4325 -m matrices/as-caida_pruned/as-caida_G_075.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.857763290405273}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.2760, 0.7522, 0.4563,  ..., 0.1844, 0.1938, 0.2151])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.857763290405273 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.2760, 0.7522, 0.4563,  ..., 0.1844, 0.1938, 0.2151])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.857763290405273 seconds
-
-[20.32, 20.4, 20.16, 20.16, 20.2, 20.32, 20.32, 20.52, 20.44, 20.48]
-[20.4, 20.44, 20.72, 24.96, 27.28, 28.88, 29.68, 27.12, 26.68, 26.68, 24.92, 25.16, 24.88, 24.6]
-14.237494468688965
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4325, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.857763290405273, 'TIME_S_1KI': 2.5104655006717396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.7423639297486, 'W': 23.58156237869744}
-[20.32, 20.4, 20.16, 20.16, 20.2, 20.32, 20.32, 20.52, 20.44, 20.48, 20.2, 20.44, 20.6, 20.68, 20.64, 20.44, 20.36, 20.08, 20.16, 20.16]
-366.5
-18.325
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4325, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.857763290405273, 'TIME_S_1KI': 2.5104655006717396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.7423639297486, 'W': 23.58156237869744, 'J_1KI': 77.62829223809216, 'W_1KI': 5.45238436501675, 'W_D': 5.256562378697442, 'J_D': 74.84027779102334, 'W_D_1KI': 1.2153901453635703, 'J_D_1KI': 0.28101506251180813}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.json
deleted file mode 100644
index 940e178..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4299, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.420058727264404, "TIME_S_1KI": 2.423833153585579, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.41223700523375, "W": 22.905972813666082, "J_1KI": 75.92748011287131, "W_1KI": 5.32820954028055, "W_D": 4.372972813666085, "J_D": 62.31526816534997, "W_D_1KI": 1.0172069815459606, "J_D_1KI": 0.23661478984553633}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.output
deleted file mode 100644
index e7f8144..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_085.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.2965834140777588}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.4492, 0.3348, 0.2820,  ..., 0.6380, 0.4778, 0.0633])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 0.2965834140777588 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3540 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 8.64511513710022}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.6270, 0.7806, 0.1009,  ..., 0.0616, 0.3576, 0.1481])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 8.64511513710022 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4299 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.420058727264404}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.3742, 0.9296, 0.9529,  ..., 0.5552, 0.6324, 0.8504])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.420058727264404 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.3742, 0.9296, 0.9529,  ..., 0.5552, 0.6324, 0.8504])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.420058727264404 seconds
-
-[21.0, 20.84, 20.84, 20.52, 20.56, 20.44, 20.52, 20.52, 20.6, 20.68]
-[20.72, 20.36, 20.68, 24.16, 25.6, 27.56, 28.48, 26.48, 26.4, 24.6, 24.6, 24.68, 24.36, 24.12]
-14.25009274482727
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.420058727264404, 'TIME_S_1KI': 2.423833153585579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.41223700523375, 'W': 22.905972813666082}
-[21.0, 20.84, 20.84, 20.52, 20.56, 20.44, 20.52, 20.52, 20.6, 20.68, 20.32, 20.56, 20.4, 20.44, 20.48, 20.4, 20.72, 20.68, 20.76, 20.76]
-370.65999999999997
-18.532999999999998
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.420058727264404, 'TIME_S_1KI': 2.423833153585579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.41223700523375, 'W': 22.905972813666082, 'J_1KI': 75.92748011287131, 'W_1KI': 5.32820954028055, 'W_D': 4.372972813666085, 'J_D': 62.31526816534997, 'W_D_1KI': 1.0172069815459606, 'J_D_1KI': 0.23661478984553633}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.json
deleted file mode 100644
index 7c8a651..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4276, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.67867112159729, "TIME_S_1KI": 2.4973505897093755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 339.92240665435787, "W": 23.947333156007627, "J_1KI": 79.49541783310521, "W_1KI": 5.600405321797855, "W_D": 5.373333156007625, "J_D": 76.27222314262384, "W_D_1KI": 1.2566260888698844, "J_D_1KI": 0.2938788795299075}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.output
deleted file mode 100644
index b48990a..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_095.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.31911325454711914}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.3207, 0.9700, 0.3262,  ..., 0.9362, 0.9941, 0.3912])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 0.31911325454711914 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3290 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 8.077399730682373}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.5291, 0.8021, 0.1066,  ..., 0.7881, 0.0805, 0.4870])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 8.077399730682373 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4276 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.67867112159729}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.1501, 0.9759, 0.0443,  ..., 0.6183, 0.1649, 0.2013])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.67867112159729 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.1501, 0.9759, 0.0443,  ..., 0.6183, 0.1649, 0.2013])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.67867112159729 seconds
-
-[20.72, 20.84, 20.68, 20.36, 20.24, 20.2, 20.4, 20.32, 20.32, 20.28]
-[20.32, 20.36, 20.52, 24.24, 26.84, 29.28, 30.8, 30.56, 26.92, 26.36, 25.44, 25.44, 25.36, 25.4]
-14.19458293914795
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4276, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.67867112159729, 'TIME_S_1KI': 2.4973505897093755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.92240665435787, 'W': 23.947333156007627}
-[20.72, 20.84, 20.68, 20.36, 20.24, 20.2, 20.4, 20.32, 20.32, 20.28, 20.76, 20.88, 20.68, 20.84, 20.88, 21.0, 20.76, 20.88, 20.84, 20.96]
-371.48
-18.574
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4276, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.67867112159729, 'TIME_S_1KI': 2.4973505897093755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 339.92240665435787, 'W': 23.947333156007627, 'J_1KI': 79.49541783310521, 'W_1KI': 5.600405321797855, 'W_D': 5.373333156007625, 'J_D': 76.27222314262384, 'W_D_1KI': 1.2566260888698844, 'J_D_1KI': 0.2938788795299075}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.json
deleted file mode 100644
index b1ad1ed..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4151, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.793859243392944, "TIME_S_1KI": 2.8412091648742335, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 356.85992715835573, "W": 25.058368416517443, "J_1KI": 85.96962832049041, "W_1KI": 6.036706436164163, "W_D": 5.961368416517441, "J_D": 84.89672845101359, "W_D_1KI": 1.4361282622301712, "J_D_1KI": 0.3459716362876828}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.output
deleted file mode 100644
index d09ad65..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_105.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.33879852294921875}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.1080, 0.7126, 0.2741,  ..., 0.0141, 0.6709, 0.0416])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 0.33879852294921875 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3099 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 7.838690519332886}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.5517, 0.4219, 0.5041,  ..., 0.2191, 0.6881, 0.0206])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 7.838690519332886 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4151 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.793859243392944}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.6544, 0.9539, 0.4979,  ..., 0.4670, 0.0344, 0.9767])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 11.793859243392944 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.6544, 0.9539, 0.4979,  ..., 0.4670, 0.0344, 0.9767])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 11.793859243392944 seconds
-
-[23.72, 23.12, 22.6, 21.2, 21.04, 21.04, 21.2, 21.6, 21.96, 22.48]
-[22.96, 22.84, 22.8, 26.76, 28.44, 29.48, 30.2, 28.12, 28.12, 28.04, 26.36, 27.0, 27.12, 27.12]
-14.241147756576538
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4151, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.793859243392944, 'TIME_S_1KI': 2.8412091648742335, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 356.85992715835573, 'W': 25.058368416517443}
-[23.72, 23.12, 22.6, 21.2, 21.04, 21.04, 21.2, 21.6, 21.96, 22.48, 21.08, 20.44, 20.4, 20.6, 20.6, 20.6, 20.64, 20.48, 20.48, 20.6]
-381.94
-19.097
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4151, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.793859243392944, 'TIME_S_1KI': 2.8412091648742335, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 356.85992715835573, 'W': 25.058368416517443, 'J_1KI': 85.96962832049041, 'W_1KI': 6.036706436164163, 'W_D': 5.961368416517441, 'J_D': 84.89672845101359, 'W_D_1KI': 1.4361282622301712, 'J_D_1KI': 0.3459716362876828}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.json
deleted file mode 100644
index cde0fa8..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3898, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.276582956314087, "TIME_S_1KI": 2.6363732571354768, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 299.2206564903259, "W": 22.616425322779282, "J_1KI": 76.76261069531193, "W_1KI": 5.802058830882319, "W_D": 4.17842532277928, "J_D": 55.28155534458152, "W_D_1KI": 1.0719408216468136, "J_D_1KI": 0.27499764536860277}
diff --git a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.output
deleted file mode 100644
index 78918ca..0000000
--- a/pytorch/output_as-caida_16core/altra_16_csr_10_10_10_as-caida_G_115.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.29033493995666504}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.0287, 0.9302, 0.4533,  ..., 0.1887, 0.8093, 0.0476])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 0.29033493995666504 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3616 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 9.739661455154419}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.1589, 0.7243, 0.3638,  ..., 0.5413, 0.9750, 0.3668])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 9.739661455154419 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3898 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.276582956314087}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.5554, 0.1424, 0.7572,  ..., 0.7612, 0.5304, 0.9292])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.276582956314087 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.5554, 0.1424, 0.7572,  ..., 0.7612, 0.5304, 0.9292])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.276582956314087 seconds
-
-[20.68, 20.84, 20.88, 20.88, 20.48, 20.32, 20.28, 20.04, 20.2, 20.24]
-[20.12, 20.44, 21.0, 22.68, 25.16, 26.56, 26.56, 27.28, 26.48, 26.6, 24.04, 24.28, 24.76]
-13.230236530303955
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3898, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.276582956314087, 'TIME_S_1KI': 2.6363732571354768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.2206564903259, 'W': 22.616425322779282}
-[20.68, 20.84, 20.88, 20.88, 20.48, 20.32, 20.28, 20.04, 20.2, 20.24, 20.4, 20.36, 20.36, 20.48, 20.52, 20.4, 20.52, 20.52, 20.68, 20.68]
-368.76000000000005
-18.438000000000002
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3898, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.276582956314087, 'TIME_S_1KI': 2.6363732571354768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.2206564903259, 'W': 22.616425322779282, 'J_1KI': 76.76261069531193, 'W_1KI': 5.802058830882319, 'W_D': 4.17842532277928, 'J_D': 55.28155534458152, 'W_D_1KI': 1.0719408216468136, 'J_D_1KI': 0.27499764536860277}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..d324571
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4769, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.119500637054443, "TIME_S_1KI": 2.1219334529365574, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 844.3043718338013, "W": 66.0, "J_1KI": 177.0401282939403, "W_1KI": 13.839379324806039, "W_D": 30.517749999999992, "J_D": 390.3980264171361, "W_D_1KI": 6.3991927028727185, "J_D_1KI": 1.3418311392058542}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..b73b062
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_010.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.22014713287353516}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.0446, 0.3085, 0.7701,  ..., 0.1983, 0.0942, 0.8281])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.22014713287353516 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4769', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.119500637054443}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.3597, 0.7528, 0.5477,  ..., 0.4164, 0.8221, 0.3719])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.119500637054443 seconds
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.3597, 0.7528, 0.5477,  ..., 0.4164, 0.8221, 0.3719])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.119500637054443 seconds
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+[66.0]
+12.792490482330322
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4769, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.119500637054443, 'TIME_S_1KI': 2.1219334529365574, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 844.3043718338013, 'W': 66.0}
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+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4769, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.119500637054443, 'TIME_S_1KI': 2.1219334529365574, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 844.3043718338013, 'W': 66.0, 'J_1KI': 177.0401282939403, 'W_1KI': 13.839379324806039, 'W_D': 30.517749999999992, 'J_D': 390.3980264171361, 'W_D_1KI': 6.3991927028727185, 'J_D_1KI': 1.3418311392058542}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..b346d50
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4385, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.059838056564331, "TIME_S_1KI": 2.2941477894103377, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 832.878444750309, "W": 65.69, "J_1KI": 189.9380717788618, "W_1KI": 14.980615735461802, "W_D": 29.879499999999993, "J_D": 378.8398765400647, "W_D_1KI": 6.814025085518813, "J_D_1KI": 1.5539395862072551}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..60c216c
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_020.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.23944783210754395}
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.8563, 0.4350, 0.3757,  ..., 0.6412, 0.9309, 0.3113])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.23944783210754395 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4385', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.059838056564331}
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.4982, 0.1677, 0.2557,  ..., 0.3629, 0.8799, 0.2576])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.059838056564331 seconds
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.4982, 0.1677, 0.2557,  ..., 0.3629, 0.8799, 0.2576])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.059838056564331 seconds
+
+[40.19, 39.19, 38.83, 38.95, 38.96, 39.61, 44.61, 39.86, 39.01, 38.95]
+[65.69]
+12.678922891616821
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4385, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.059838056564331, 'TIME_S_1KI': 2.2941477894103377, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 832.878444750309, 'W': 65.69}
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+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4385, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.059838056564331, 'TIME_S_1KI': 2.2941477894103377, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 832.878444750309, 'W': 65.69, 'J_1KI': 189.9380717788618, 'W_1KI': 14.980615735461802, 'W_D': 29.879499999999993, 'J_D': 378.8398765400647, 'W_D_1KI': 6.814025085518813, 'J_D_1KI': 1.5539395862072551}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..eaa390d
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4096, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.174330949783325, "TIME_S_1KI": 2.4839675170369446, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 834.314271697998, "W": 65.58, "J_1KI": 203.69000773876905, "W_1KI": 16.0107421875, "W_D": 30.34375, "J_D": 386.03573775291443, "W_D_1KI": 7.40814208984375, "J_D_1KI": 1.8086284399032593}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..fd9cd3d
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_030.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.25633716583251953}
+
+tensor(indices=tensor([[ 1809, 21783,   106,  ...,  7018,   160,   882],
+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=86850, layout=torch.sparse_coo)
+tensor([0.5364, 0.8049, 0.8196,  ..., 0.5812, 0.6014, 0.6461])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_030
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 86850
+Density: 8.820467912752026e-05
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+
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..85698b6
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..996b8a6
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_040.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '16']
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..91a3b98
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_050.json
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..bd72311
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_050.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '16']
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..8d9edd9
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..3ef304c
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_060.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.27417469024658203}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160,   882],
+                       [    2,     2,     2,  ..., 31355, 31360, 31373]]),
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..363955f
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4500, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.017730236053467, "TIME_S_1KI": 2.226162274678548, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 835.5015341281892, "W": 65.95, "J_1KI": 185.66700758404204, "W_1KI": 14.655555555555555, "W_D": 30.592999999999996, "J_D": 387.57389588451383, "W_D_1KI": 6.798444444444444, "J_D_1KI": 1.5107654320987653}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..fb1e3c6
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_070.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.23329615592956543}
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+                       [    2,     2,     2,  ..., 31240, 31283, 31284]]),
+       values=tensor([3., 3., 3.,  ..., 1., 3., 3.]),
+       size=(31379, 31379), nnz=78684, layout=torch.sparse_coo)
+tensor([0.1520, 0.4880, 0.9188,  ..., 0.4689, 0.3143, 0.0563])
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+                       [    2,     2,     2,  ..., 31240, 31283, 31284]]),
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..6859055
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_080.json
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+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3675, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.307059049606323, "TIME_S_1KI": 2.804641918260224, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 846.6635090303421, "W": 66.61, "J_1KI": 230.3846283075761, "W_1KI": 18.125170068027213, "W_D": 31.482750000000003, "J_D": 400.16957797515397, "W_D_1KI": 8.566734693877551, "J_D_1KI": 2.331084270442871}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..2b02f8b
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_080.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.285677433013916}
+
+tensor(indices=tensor([[22754, 22754,   106,  ...,  4133, 31329, 12170],
+                       [    0,     1,     2,  ..., 31373, 31373, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 1., 1., 3.]),
+       size=(31379, 31379), nnz=98112, layout=torch.sparse_coo)
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..39d235e
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3641, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.4994535446167, "TIME_S_1KI": 2.8836730416414995, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.6610073566436, "W": 65.96, "J_1KI": 236.9296916662026, "W_1KI": 18.11590222466355, "W_D": 30.651999999999987, "J_D": 400.883644595146, "W_D_1KI": 8.418566327931883, "J_D_1KI": 2.312157739063961}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..880f507
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_090.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.43505334854125977}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   882, 31211, 12170],
+                       [    2,     2,     2,  ..., 31373, 31376, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=100924, layout=torch.sparse_coo)
+tensor([0.3836, 0.2671, 0.6063,  ..., 0.6627, 0.4538, 0.8080])
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '2413', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 6.957933187484741}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   882, 31211, 12170],
+                       [    2,     2,     2,  ..., 31373, 31376, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
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+tensor([0.8754, 0.4075, 0.0830,  ..., 0.8566, 0.5369, 0.0410])
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..da12c4a
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_100.json
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+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3499, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.198312997817993, "TIME_S_1KI": 2.914636466938552, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 849.0300207567216, "W": 66.06, "J_1KI": 242.6493343117238, "W_1KI": 18.8796799085453, "W_D": 30.453500000000005, "J_D": 391.40078318369393, "W_D_1KI": 8.703486710488713, "J_D_1KI": 2.487421180476911}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..4532586
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_100.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '16']
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+                       [    2,     2,     2,  ..., 31373, 31377, 31378]]),
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..76c647d
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_110.json
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..ca9c4d0
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_110.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '16']
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diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..01b12e6
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_120.json
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+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3394, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.34037709236145, "TIME_S_1KI": 3.0466638457163966, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 844.1848246479035, "W": 66.18, "J_1KI": 248.72858710898748, "W_1KI": 19.49911608721273, "W_D": 30.526500000000013, "J_D": 389.3926873619558, "W_D_1KI": 8.99425456688274, "J_D_1KI": 2.6500455412147144}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..6c68976
--- /dev/null
+++ b/pytorch/output_as-caida_16core/epyc_7313p_16_coo_10_10_10_as-caida_G_120.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.309314489364624}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.5583, 0.2788, 0.6104,  ..., 0.7605, 0.2906, 0.0391])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.34037709236145 seconds
+
+[40.3, 44.55, 39.36, 39.43, 39.43, 39.38, 39.86, 38.9, 38.87, 39.02]
+[66.18]
+12.755890369415283
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3394, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.34037709236145, 'TIME_S_1KI': 3.0466638457163966, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 844.1848246479035, 'W': 66.18}
+[40.3, 44.55, 39.36, 39.43, 39.43, 39.38, 39.86, 38.9, 38.87, 39.02, 40.16, 39.0, 38.87, 38.79, 39.53, 39.93, 39.44, 39.04, 39.36, 39.18]
+713.0699999999999
+35.653499999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3394, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.34037709236145, 'TIME_S_1KI': 3.0466638457163966, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 844.1848246479035, 'W': 66.18, 'J_1KI': 248.72858710898748, 'W_1KI': 19.49911608721273, 'W_D': 30.526500000000013, 'J_D': 389.3926873619558, 'W_D_1KI': 8.99425456688274, 'J_D_1KI': 2.6500455412147144}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.json
deleted file mode 100644
index 4d89138..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 130629, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 11.046883583068848, "TIME_S_1KI": 0.08456685409111948, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1447.5023779034616, "W": 103.45, "J_1KI": 11.081018593906878, "W_1KI": 0.7919374717711994, "W_D": 67.28425, "J_D": 941.4607237356305, "W_D_1KI": 0.5150789640891379, "J_D_1KI": 0.003943067497180089}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.output
deleted file mode 100644
index 531c512..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_005.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.02265310287475586}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.1109, 0.3688, 0.8394,  ..., 0.5778, 0.7474, 0.0459])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 0.02265310287475586 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '46351', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 3.7256975173950195}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.7624, 0.1488, 0.7288,  ..., 0.4517, 0.7426, 0.4871])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 3.7256975173950195 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '130629', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 11.046883583068848}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.6772, 0.5623, 0.5296,  ..., 0.8301, 0.9620, 0.4995])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 11.046883583068848 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.6772, 0.5623, 0.5296,  ..., 0.8301, 0.9620, 0.4995])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 11.046883583068848 seconds
-
-[40.01, 39.79, 39.68, 39.98, 39.67, 39.17, 44.96, 39.73, 39.43, 39.12]
-[103.45]
-13.992289781570435
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130629, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 11.046883583068848, 'TIME_S_1KI': 0.08456685409111948, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1447.5023779034616, 'W': 103.45}
-[40.01, 39.79, 39.68, 39.98, 39.67, 39.17, 44.96, 39.73, 39.43, 39.12, 40.63, 39.49, 44.41, 39.16, 39.85, 39.65, 39.59, 39.3, 39.78, 39.59]
-723.315
-36.16575
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130629, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 11.046883583068848, 'TIME_S_1KI': 0.08456685409111948, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1447.5023779034616, 'W': 103.45, 'J_1KI': 11.081018593906878, 'W_1KI': 0.7919374717711994, 'W_D': 67.28425, 'J_D': 941.4607237356305, 'W_D_1KI': 0.5150789640891379, 'J_D_1KI': 0.003943067497180089}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.json
deleted file mode 100644
index 4246a76..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122232, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.012900829315186, "TIME_S_1KI": 0.08191718068357864, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1344.7570972156525, "W": 103.87, "J_1KI": 11.001677933893355, "W_1KI": 0.8497774723476668, "W_D": 67.6435, "J_D": 875.7492702946663, "W_D_1KI": 0.5534025459781399, "J_D_1KI": 0.004527476814403265}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.output
deleted file mode 100644
index 78932cf..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_015.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.02150440216064453}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.2547, 0.3736, 0.4755,  ..., 0.0068, 0.6237, 0.5320])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 0.02150440216064453 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '48827', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 4.194327354431152}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.7417, 0.4748, 0.0091,  ..., 0.5058, 0.6479, 0.7190])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 4.194327354431152 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122232', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.012900829315186}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.8581, 0.1137, 0.8207,  ..., 0.0910, 0.4048, 0.6394])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.012900829315186 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.8581, 0.1137, 0.8207,  ..., 0.0910, 0.4048, 0.6394])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.012900829315186 seconds
-
-[40.42, 39.32, 39.31, 39.25, 54.33, 39.25, 39.4, 39.12, 39.27, 40.74]
-[103.87]
-12.946539878845215
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122232, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.012900829315186, 'TIME_S_1KI': 0.08191718068357864, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.7570972156525, 'W': 103.87}
-[40.42, 39.32, 39.31, 39.25, 54.33, 39.25, 39.4, 39.12, 39.27, 40.74, 39.91, 39.57, 39.25, 39.28, 39.45, 39.43, 39.72, 39.1, 39.4, 39.09]
-724.53
-36.2265
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122232, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.012900829315186, 'TIME_S_1KI': 0.08191718068357864, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1344.7570972156525, 'W': 103.87, 'J_1KI': 11.001677933893355, 'W_1KI': 0.8497774723476668, 'W_D': 67.6435, 'J_D': 875.7492702946663, 'W_D_1KI': 0.5534025459781399, 'J_D_1KI': 0.004527476814403265}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.json
deleted file mode 100644
index 634c711..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120118, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 12.039484739303589, "TIME_S_1KI": 0.10023047952266595, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1377.8382610416413, "W": 104.04, "J_1KI": 11.47070598113223, "W_1KI": 0.8661482875172747, "W_D": 68.01325, "J_D": 900.7233574374318, "W_D_1KI": 0.5662203000382956, "J_D_1KI": 0.004713867197574848}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.output
deleted file mode 100644
index 41dc918..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_025.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.028760671615600586}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.0546, 0.6478, 0.5019,  ..., 0.1774, 0.5884, 0.7696])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 0.028760671615600586 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36508', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 3.191291332244873}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.7513, 0.6718, 0.2286,  ..., 0.8031, 0.6348, 0.1488])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 3.191291332244873 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120118', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 12.039484739303589}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.0727, 0.2026, 0.8265,  ..., 0.2293, 0.8547, 0.4127])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 12.039484739303589 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.0727, 0.2026, 0.8265,  ..., 0.2293, 0.8547, 0.4127])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 12.039484739303589 seconds
-
-[41.14, 39.71, 39.25, 39.89, 39.22, 39.19, 39.32, 39.24, 44.24, 39.23]
-[104.04]
-13.243351221084595
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 12.039484739303589, 'TIME_S_1KI': 0.10023047952266595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1377.8382610416413, 'W': 104.04}
-[41.14, 39.71, 39.25, 39.89, 39.22, 39.19, 39.32, 39.24, 44.24, 39.23, 40.57, 39.23, 39.41, 39.27, 39.7, 44.55, 39.47, 39.31, 39.26, 39.61]
-720.5350000000001
-36.02675000000001
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120118, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 12.039484739303589, 'TIME_S_1KI': 0.10023047952266595, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1377.8382610416413, 'W': 104.04, 'J_1KI': 11.47070598113223, 'W_1KI': 0.8661482875172747, 'W_D': 68.01325, 'J_D': 900.7233574374318, 'W_D_1KI': 0.5662203000382956, 'J_D_1KI': 0.004713867197574848}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.json
deleted file mode 100644
index 206e20b..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 117637, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.62736988067627, "TIME_S_1KI": 0.09034036808721975, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1348.652883746624, "W": 104.36999999999999, "J_1KI": 11.464529729138144, "W_1KI": 0.8872208573833061, "W_D": 68.05524999999999, "J_D": 879.3993404867051, "W_D_1KI": 0.5785190883820566, "J_D_1KI": 0.00491783272594555}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.output
deleted file mode 100644
index 2b9239c..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_035.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.029236316680908203}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.3402, 0.2499, 0.3172,  ..., 0.1488, 0.4072, 0.2122])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 0.029236316680908203 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35914', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 3.2055723667144775}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.5217, 0.6973, 0.2862,  ..., 0.9929, 0.5018, 0.4794])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 3.2055723667144775 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117637', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.62736988067627}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.6504, 0.2249, 0.2739,  ..., 0.8117, 0.7999, 0.0068])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.62736988067627 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.6504, 0.2249, 0.2739,  ..., 0.8117, 0.7999, 0.0068])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.62736988067627 seconds
-
-[39.98, 40.1, 39.44, 39.73, 39.36, 44.62, 40.72, 39.73, 40.41, 39.38]
-[104.37]
-12.921844244003296
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117637, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.62736988067627, 'TIME_S_1KI': 0.09034036808721975, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1348.652883746624, 'W': 104.36999999999999}
-[39.98, 40.1, 39.44, 39.73, 39.36, 44.62, 40.72, 39.73, 40.41, 39.38, 40.08, 39.64, 42.5, 41.53, 39.76, 39.42, 39.32, 39.21, 40.44, 41.29]
-726.2950000000001
-36.314750000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117637, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.62736988067627, 'TIME_S_1KI': 0.09034036808721975, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1348.652883746624, 'W': 104.36999999999999, 'J_1KI': 11.464529729138144, 'W_1KI': 0.8872208573833061, 'W_D': 68.05524999999999, 'J_D': 879.3993404867051, 'W_D_1KI': 0.5785190883820566, 'J_D_1KI': 0.00491783272594555}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.json
deleted file mode 100644
index 3e95d74..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 129597, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.245419979095459, "TIME_S_1KI": 0.08677222450439022, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1476.8643749785424, "W": 104.97, "J_1KI": 11.395822241090013, "W_1KI": 0.8099724530660432, "W_D": 69.20224999999999, "J_D": 973.6337781590818, "W_D_1KI": 0.5339803390510582, "J_D_1KI": 0.004120314043157312}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.output
deleted file mode 100644
index 958bdda..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_045.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.027070283889770508}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.9579, 0.3823, 0.7927,  ..., 0.9257, 0.1735, 0.9344])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 0.027070283889770508 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38787', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 3.142535448074341}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.7648, 0.3979, 0.1181,  ..., 0.8603, 0.9960, 0.9728])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 3.142535448074341 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '129597', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.245419979095459}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.1180, 0.3748, 0.1643,  ..., 0.9664, 0.3966, 0.4847])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 11.245419979095459 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.1180, 0.3748, 0.1643,  ..., 0.9664, 0.3966, 0.4847])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 11.245419979095459 seconds
-
-[39.97, 39.69, 41.18, 39.43, 39.33, 39.4, 39.92, 39.67, 40.11, 39.3]
-[104.97]
-14.069394826889038
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.245419979095459, 'TIME_S_1KI': 0.08677222450439022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1476.8643749785424, 'W': 104.97}
-[39.97, 39.69, 41.18, 39.43, 39.33, 39.4, 39.92, 39.67, 40.11, 39.3, 40.3, 39.27, 39.34, 39.54, 39.86, 39.21, 39.39, 41.12, 39.24, 39.74]
-715.355
-35.76775
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 129597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.245419979095459, 'TIME_S_1KI': 0.08677222450439022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1476.8643749785424, 'W': 104.97, 'J_1KI': 11.395822241090013, 'W_1KI': 0.8099724530660432, 'W_D': 69.20224999999999, 'J_D': 973.6337781590818, 'W_D_1KI': 0.5339803390510582, 'J_D_1KI': 0.004120314043157312}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.json
deleted file mode 100644
index 1a48113..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122142, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 11.075148582458496, "TIME_S_1KI": 0.09067436739580567, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1444.9410432815553, "W": 104.47000000000001, "J_1KI": 11.830009687753233, "W_1KI": 0.855315943737617, "W_D": 68.78025000000001, "J_D": 951.3104833173753, "W_D_1KI": 0.5631171095937516, "J_D_1KI": 0.004610347870460215}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.output
deleted file mode 100644
index d27a247..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_055.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.02579522132873535}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.4101, 0.8209, 0.7582,  ..., 0.7042, 0.4089, 0.9250])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 0.02579522132873535 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '40705', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 3.499220609664917}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.5165, 0.5886, 0.2570,  ..., 0.5351, 0.5985, 0.2855])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 3.499220609664917 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122142', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 11.075148582458496}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.1241, 0.2147, 0.8696,  ..., 0.1307, 0.0728, 0.1644])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 11.075148582458496 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.1241, 0.2147, 0.8696,  ..., 0.1307, 0.0728, 0.1644])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 11.075148582458496 seconds
-
-[40.03, 39.33, 39.47, 39.33, 39.45, 39.87, 39.7, 39.82, 40.96, 39.66]
-[104.47]
-13.831157684326172
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 11.075148582458496, 'TIME_S_1KI': 0.09067436739580567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1444.9410432815553, 'W': 104.47000000000001}
-[40.03, 39.33, 39.47, 39.33, 39.45, 39.87, 39.7, 39.82, 40.96, 39.66, 39.88, 39.36, 39.59, 39.25, 40.14, 39.15, 39.32, 39.55, 39.92, 39.6]
-713.7950000000001
-35.689750000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122142, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 11.075148582458496, 'TIME_S_1KI': 0.09067436739580567, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1444.9410432815553, 'W': 104.47000000000001, 'J_1KI': 11.830009687753233, 'W_1KI': 0.855315943737617, 'W_D': 68.78025000000001, 'J_D': 951.3104833173753, 'W_D_1KI': 0.5631171095937516, 'J_D_1KI': 0.004610347870460215}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.json
deleted file mode 100644
index 9c73b0a..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 121043, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.490303754806519, "TIME_S_1KI": 0.08666592661125815, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1396.8674520850182, "W": 104.75, "J_1KI": 11.540258024710377, "W_1KI": 0.8653949422932347, "W_D": 68.499, "J_D": 913.4512992875575, "W_D_1KI": 0.5659063308080599, "J_D_1KI": 0.004675250372248373}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.output
deleted file mode 100644
index ca600af..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_065.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.028383970260620117}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.6987, 0.1536, 0.6933,  ..., 0.9556, 0.5512, 0.6559])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 0.028383970260620117 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36992', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 3.2088987827301025}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.2523, 0.5417, 0.6382,  ..., 0.2729, 0.0339, 0.5004])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 3.2088987827301025 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '121043', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.490303754806519}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.9743, 0.5130, 0.5318,  ..., 0.8200, 0.9366, 0.1557])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.490303754806519 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.9743, 0.5130, 0.5318,  ..., 0.8200, 0.9366, 0.1557])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.490303754806519 seconds
-
-[41.45, 39.82, 40.25, 39.93, 39.31, 39.37, 39.34, 39.68, 44.83, 39.67]
-[104.75]
-13.33525013923645
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121043, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.490303754806519, 'TIME_S_1KI': 0.08666592661125815, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.8674520850182, 'W': 104.75}
-[41.45, 39.82, 40.25, 39.93, 39.31, 39.37, 39.34, 39.68, 44.83, 39.67, 40.18, 39.27, 39.88, 39.63, 40.85, 43.9, 39.97, 39.48, 39.27, 39.18]
-725.02
-36.251
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121043, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.490303754806519, 'TIME_S_1KI': 0.08666592661125815, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1396.8674520850182, 'W': 104.75, 'J_1KI': 11.540258024710377, 'W_1KI': 0.8653949422932347, 'W_D': 68.499, 'J_D': 913.4512992875575, 'W_D_1KI': 0.5659063308080599, 'J_D_1KI': 0.004675250372248373}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.json
deleted file mode 100644
index 6f29e46..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 120423, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.76958441734314, "TIME_S_1KI": 0.08943129150862493, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1443.2409217905997, "W": 104.71, "J_1KI": 11.984761397661574, "W_1KI": 0.8695182813914285, "W_D": 68.72225, "J_D": 947.213861498654, "W_D_1KI": 0.5706737915514478, "J_D_1KI": 0.0047389102708905095}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.output
deleted file mode 100644
index d3e5c99..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_075.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.026674270629882812}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.6187, 0.1169, 0.0618,  ..., 0.0036, 0.3565, 0.7239])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 0.026674270629882812 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '39363', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 3.4321558475494385}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.5402, 0.1074, 0.0917,  ..., 0.8328, 0.8213, 0.8141])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 3.4321558475494385 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '120423', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.76958441734314}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.9766, 0.5800, 0.0793,  ..., 0.9152, 0.2119, 0.8249])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.76958441734314 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.9766, 0.5800, 0.0793,  ..., 0.9152, 0.2119, 0.8249])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.76958441734314 seconds
-
-[40.44, 39.88, 39.92, 39.3, 39.43, 39.42, 39.28, 39.2, 40.68, 39.23]
-[104.71]
-13.783219575881958
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120423, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.76958441734314, 'TIME_S_1KI': 0.08943129150862493, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.2409217905997, 'W': 104.71}
-[40.44, 39.88, 39.92, 39.3, 39.43, 39.42, 39.28, 39.2, 40.68, 39.23, 53.53, 39.71, 40.17, 39.13, 39.61, 39.81, 39.45, 39.24, 39.21, 39.43]
-719.7549999999999
-35.98774999999999
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 120423, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.76958441734314, 'TIME_S_1KI': 0.08943129150862493, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.2409217905997, 'W': 104.71, 'J_1KI': 11.984761397661574, 'W_1KI': 0.8695182813914285, 'W_D': 68.72225, 'J_D': 947.213861498654, 'W_D_1KI': 0.5706737915514478, 'J_D_1KI': 0.0047389102708905095}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.json
deleted file mode 100644
index df39cfe..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123818, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 12.64590573310852, "TIME_S_1KI": 0.10213301566095818, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1404.5768914031983, "W": 105.84, "J_1KI": 11.343882887812743, "W_1KI": 0.8548030173318903, "W_D": 69.798, "J_D": 926.2722776470184, "W_D_1KI": 0.5637144841622381, "J_D_1KI": 0.004552766836503886}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.output
deleted file mode 100644
index b82b034..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_085.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.027594804763793945}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.3579, 0.6537, 0.3650,  ..., 0.6368, 0.7499, 0.3578])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 0.027594804763793945 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38050', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 3.2266926765441895}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.5666, 0.3607, 0.0681,  ..., 0.1783, 0.0421, 0.2428])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 3.2266926765441895 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123818', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 12.64590573310852}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.9239, 0.0838, 0.6171,  ..., 0.0890, 0.6862, 0.2789])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 12.64590573310852 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.9239, 0.0838, 0.6171,  ..., 0.0890, 0.6862, 0.2789])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 12.64590573310852 seconds
-
-[40.09, 39.23, 39.46, 39.19, 39.83, 39.63, 39.26, 44.52, 39.29, 39.12]
-[105.84]
-13.270756721496582
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123818, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 12.64590573310852, 'TIME_S_1KI': 0.10213301566095818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1404.5768914031983, 'W': 105.84}
-[40.09, 39.23, 39.46, 39.19, 39.83, 39.63, 39.26, 44.52, 39.29, 39.12, 40.73, 39.74, 39.65, 39.19, 44.6, 39.27, 39.31, 39.32, 39.56, 39.64]
-720.8399999999999
-36.041999999999994
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123818, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 12.64590573310852, 'TIME_S_1KI': 0.10213301566095818, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1404.5768914031983, 'W': 105.84, 'J_1KI': 11.343882887812743, 'W_1KI': 0.8548030173318903, 'W_D': 69.798, 'J_D': 926.2722776470184, 'W_D_1KI': 0.5637144841622381, 'J_D_1KI': 0.004552766836503886}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.json
deleted file mode 100644
index d2c7977..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123367, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.797792911529541, "TIME_S_1KI": 0.08752578008324383, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1381.98059548378, "W": 106.36000000000001, "J_1KI": 11.202190176333865, "W_1KI": 0.8621430366305415, "W_D": 70.495, "J_D": 915.9714373695851, "W_D_1KI": 0.5714250974733924, "J_D_1KI": 0.004631912079189673}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.output
deleted file mode 100644
index 61411f0..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_095.output
+++ /dev/null
@@ -1,110 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.02869391441345215}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.6977, 0.1794, 0.1733,  ..., 0.5021, 0.2371, 0.5915])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 0.02869391441345215 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36593', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 3.28193736076355}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2038, 0.2117, 0.0495,  ..., 0.7353, 0.4058, 0.7220])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 3.28193736076355 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117073', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 9.964244604110718}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2057, 0.4266, 0.2763,  ..., 0.1377, 0.7100, 0.3501])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 9.964244604110718 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123367', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.797792911529541}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2200, 0.4810, 0.3293,  ..., 0.7198, 0.3850, 0.3915])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.797792911529541 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2200, 0.4810, 0.3293,  ..., 0.7198, 0.3850, 0.3915])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.797792911529541 seconds
-
-[40.51, 39.95, 39.69, 39.82, 39.8, 39.17, 39.45, 39.15, 39.47, 39.6]
-[106.36]
-12.9934241771698
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123367, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.797792911529541, 'TIME_S_1KI': 0.08752578008324383, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.98059548378, 'W': 106.36000000000001}
-[40.51, 39.95, 39.69, 39.82, 39.8, 39.17, 39.45, 39.15, 39.47, 39.6, 41.41, 39.21, 39.7, 39.75, 39.19, 39.65, 39.19, 40.91, 42.9, 39.08]
-717.3000000000001
-35.865
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123367, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.797792911529541, 'TIME_S_1KI': 0.08752578008324383, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1381.98059548378, 'W': 106.36000000000001, 'J_1KI': 11.202190176333865, 'W_1KI': 0.8621430366305415, 'W_D': 70.495, 'J_D': 915.9714373695851, 'W_D_1KI': 0.5714250974733924, 'J_D_1KI': 0.004631912079189673}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.json
deleted file mode 100644
index 706b79f..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 130315, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.23905086517334, "TIME_S_1KI": 0.0862452585287445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1479.8992812085153, "W": 106.81, "J_1KI": 11.356323379568856, "W_1KI": 0.8196293596285923, "W_D": 71.0115, "J_D": 983.8954012502431, "W_D_1KI": 0.5449219199631662, "J_D_1KI": 0.004181574799241578}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.output
deleted file mode 100644
index 8b920ee..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_105.output
+++ /dev/null
@@ -1,110 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.02874302864074707}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.0653, 0.6055, 0.4558,  ..., 0.6884, 0.5872, 0.3001])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 0.02874302864074707 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36530', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 3.1528003215789795}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.0149, 0.2762, 0.5141,  ..., 0.7208, 0.6938, 0.9073])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 3.1528003215789795 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '121658', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 9.802452087402344}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.9575, 0.4028, 0.3578,  ..., 0.2947, 0.5779, 0.9432])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 9.802452087402344 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '130315', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 11.23905086517334}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.3162, 0.7565, 0.7515,  ..., 0.4628, 0.3648, 0.1630])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 11.23905086517334 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.3162, 0.7565, 0.7515,  ..., 0.4628, 0.3648, 0.1630])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 11.23905086517334 seconds
-
-[40.79, 41.78, 39.38, 39.23, 39.79, 39.25, 39.89, 39.93, 39.66, 39.98]
-[106.81]
-13.855437517166138
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130315, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.23905086517334, 'TIME_S_1KI': 0.0862452585287445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1479.8992812085153, 'W': 106.81}
-[40.79, 41.78, 39.38, 39.23, 39.79, 39.25, 39.89, 39.93, 39.66, 39.98, 41.03, 39.28, 39.81, 39.38, 39.32, 39.19, 40.5, 39.21, 39.67, 39.6]
-715.97
-35.798500000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130315, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 11.23905086517334, 'TIME_S_1KI': 0.0862452585287445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1479.8992812085153, 'W': 106.81, 'J_1KI': 11.356323379568856, 'W_1KI': 0.8196293596285923, 'W_D': 71.0115, 'J_D': 983.8954012502431, 'W_D_1KI': 0.5449219199631662, 'J_D_1KI': 0.004181574799241578}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.json
deleted file mode 100644
index 706d530..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122384, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.623056411743164, "TIME_S_1KI": 0.08680102310549716, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.5208738970757, "W": 106.31, "J_1KI": 11.223042831555397, "W_1KI": 0.8686593018695254, "W_D": 70.50425, "J_D": 910.9120409505963, "W_D_1KI": 0.5760904203163812, "J_D_1KI": 0.004707236406036584}
diff --git a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.output
deleted file mode 100644
index 4525251..0000000
--- a/pytorch/output_as-caida_16core/epyc_7313p_16_csr_10_10_10_as-caida_G_115.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.029188871383666992}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.1969, 0.7804, 0.2686,  ..., 0.5827, 0.0798, 0.7838])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 0.029188871383666992 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35972', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 3.086211681365967}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.5660, 0.8309, 0.9656,  ..., 0.1156, 0.8355, 0.1569])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 3.086211681365967 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122384', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.623056411743164}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.4590, 0.4390, 0.2483,  ..., 0.8018, 0.3092, 0.2454])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.623056411743164 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.4590, 0.4390, 0.2483,  ..., 0.8018, 0.3092, 0.2454])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.623056411743164 seconds
-
-[39.96, 41.64, 39.36, 39.3, 39.65, 39.66, 39.8, 40.15, 39.67, 40.32]
-[106.31]
-12.919959306716919
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122384, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.623056411743164, 'TIME_S_1KI': 0.08680102310549716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.5208738970757, 'W': 106.31}
-[39.96, 41.64, 39.36, 39.3, 39.65, 39.66, 39.8, 40.15, 39.67, 40.32, 39.97, 39.29, 39.38, 39.24, 39.46, 39.22, 39.78, 41.14, 39.67, 39.16]
-716.115
-35.80575
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122384, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.623056411743164, 'TIME_S_1KI': 0.08680102310549716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.5208738970757, 'W': 106.31, 'J_1KI': 11.223042831555397, 'W_1KI': 0.8686593018695254, 'W_D': 70.50425, 'J_D': 910.9120409505963, 'W_D_1KI': 0.5760904203163812, 'J_D_1KI': 0.004707236406036584}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..f041a66
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 3781, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.045656204223633, "TIME_S_1KI": 2.6568781285965706, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 735.7539411067963, "W": 54.2, "J_1KI": 194.59242028743623, "W_1KI": 14.334832055011901, "W_D": 36.8215, "J_D": 499.84434949195384, "W_D_1KI": 9.738561227188574, "J_D_1KI": 2.575657558103299}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..8b54a2a
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_010.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.2776789665222168}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.3307, 0.3897, 0.8418,  ..., 0.5769, 0.7460, 0.7214])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.2776789665222168 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '3781', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.045656204223633}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.8381, 0.6468, 0.8832,  ..., 0.6716, 0.5945, 0.2145])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.045656204223633 seconds
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.8381, 0.6468, 0.8832,  ..., 0.6716, 0.5945, 0.2145])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.045656204223633 seconds
+
+[18.97, 19.7, 18.61, 18.68, 18.95, 18.76, 18.82, 18.79, 23.19, 18.83]
+[54.2]
+13.574795961380005
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 3781, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.045656204223633, 'TIME_S_1KI': 2.6568781285965706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.7539411067963, 'W': 54.2}
+[18.97, 19.7, 18.61, 18.68, 18.95, 18.76, 18.82, 18.79, 23.19, 18.83, 18.72, 18.54, 18.93, 18.5, 22.74, 19.06, 19.06, 19.0, 18.57, 18.82]
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..f050e87
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 3534, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.09178638458252, "TIME_S_1KI": 2.8556271603232934, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 738.7126415205003, "W": 54.38, "J_1KI": 209.03017586884556, "W_1KI": 15.387662705149973, "W_D": 37.230500000000006, "J_D": 505.7491908813716, "W_D_1KI": 10.534946236559142, "J_D_1KI": 2.981026099762066}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..6a2bcd9
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_020.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.29705047607421875}
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+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..6b2dcf3
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_030.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 3294, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.149410963058472, "TIME_S_1KI": 3.081181227400872, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 741.0869712638855, "W": 54.36, "J_1KI": 224.980865593165, "W_1KI": 16.502732240437158, "W_D": 37.05375, "J_D": 505.1517910498381, "W_D_1KI": 11.248861566484518, "J_D_1KI": 3.4149549382163076}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..25e5fe4
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_030.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.3187413215637207}
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+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..0331216
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_040.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 3193, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.120986938476562, "TIME_S_1KI": 3.16974222940074, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 737.1458910942079, "W": 54.20000000000001, "J_1KI": 230.86310400695518, "W_1KI": 16.974632007516444, "W_D": 37.178250000000006, "J_D": 505.64195988142495, "W_D_1KI": 11.64367366113373, "J_D_1KI": 3.6466250113165457}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..dd787b0
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_040.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '16']
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..cf180e5
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_050.json
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..6eb35b4
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_050.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '16']
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..40f44b5
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_060.json
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..ea0c513
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_060.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '16']
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..a5395f9
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_070.json
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..74bed62
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_070.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '16']
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..cb1a803
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_080.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2927, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.156116485595703, "TIME_S_1KI": 3.4698040606749925, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 740.3946319580078, "W": 54.08, "J_1KI": 252.95341030338497, "W_1KI": 18.47625555175948, "W_D": 37.199999999999996, "J_D": 509.295124053955, "W_D_1KI": 12.709258626580116, "J_D_1KI": 4.342076742938202}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..b9bbbff
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_080.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '16']
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..c5cbc9c
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_090.json
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..bfede00
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_090.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '16']
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+                       [    2,     2,     2,  ..., 31373, 31376, 31378]]),
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..bfa1bb8
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_100.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2799, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.173045873641968, "TIME_S_1KI": 3.634528715127534, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 742.0090924072266, "W": 54.27, "J_1KI": 265.0979251186947, "W_1KI": 19.389067524115756, "W_D": 37.21000000000001, "J_D": 508.7554510498048, "W_D_1KI": 13.294033583422655, "J_D_1KI": 4.749565410297484}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..33ea9fd
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_100.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '16']
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..0c35faf
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2745, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.160125970840454, "TIME_S_1KI": 3.7013209365538997, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 738.6148199796676, "W": 54.04999999999999, "J_1KI": 269.07643715106286, "W_1KI": 19.690346083788704, "W_D": 37.206749999999985, "J_D": 508.4450870171783, "W_D_1KI": 13.554371584699448, "J_D_1KI": 4.937840285865009}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..2a75f87
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_110.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.3823854923248291}
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+                       [    2,     2,     2,  ..., 31373, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
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+tensor([0.1846, 0.1440, 0.0485,  ..., 0.2372, 0.0918, 0.7447])
+Matrix Type: SuiteSparse
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '2745', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.160125970840454}
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+                       [    2,     2,     2,  ..., 31373, 31378, 31378]]),
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   882,  2616, 12170],
+                       [    2,     2,     2,  ..., 31373, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..c206bbf
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2707, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.189634084701538, "TIME_S_1KI": 3.7641795658298998, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 744.9943315029144, "W": 54.21, "J_1KI": 275.21031825006077, "W_1KI": 20.025858884373847, "W_D": 37.37875, "J_D": 513.6867158949375, "W_D_1KI": 13.808182489841151, "J_D_1KI": 5.100917063110879}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..5aa8e9a
--- /dev/null
+++ b/pytorch/output_as-caida_16core/xeon_4216_16_coo_10_10_10_as-caida_G_120.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.38787221908569336}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.9457, 0.7692, 0.0445,  ..., 0.6073, 0.5689, 0.2068])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.38787221908569336 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '2707', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '16']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.189634084701538}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.6976, 0.9804, 0.6664,  ..., 0.1768, 0.5922, 0.2372])
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+Density: 0.0001081713341839054
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+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.6976, 0.9804, 0.6664,  ..., 0.1768, 0.5922, 0.2372])
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diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.json
deleted file mode 100644
index 253b8c7..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 118951, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.958328008651733, "TIME_S_1KI": 0.0921247236984282, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1244.7510617017745, "W": 85.66, "J_1KI": 10.46440182681755, "W_1KI": 0.7201284562550967, "W_D": 68.79275, "J_D": 999.64801073879, "W_D_1KI": 0.578328471387378, "J_D_1KI": 0.004861905081818379}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.output
deleted file mode 100644
index cc25f85..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_005.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.02218484878540039}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.8422, 0.7050, 0.1082,  ..., 0.1730, 0.8671, 0.7264])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 0.02218484878540039 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '47329', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 4.177785634994507}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.4803, 0.7198, 0.6367,  ..., 0.1841, 0.4829, 0.5995])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 4.177785634994507 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '118951', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.958328008651733}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.4882, 0.5169, 0.2032,  ..., 0.7665, 0.8923, 0.8813])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.958328008651733 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.4882, 0.5169, 0.2032,  ..., 0.7665, 0.8923, 0.8813])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.958328008651733 seconds
-
-[19.13, 18.64, 19.2, 18.61, 18.66, 18.55, 19.69, 18.7, 18.45, 18.58]
-[85.66]
-14.531298875808716
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 118951, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.958328008651733, 'TIME_S_1KI': 0.0921247236984282, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1244.7510617017745, 'W': 85.66}
-[19.13, 18.64, 19.2, 18.61, 18.66, 18.55, 19.69, 18.7, 18.45, 18.58, 18.68, 18.33, 18.75, 18.56, 18.36, 18.54, 18.71, 18.72, 19.33, 18.7]
-337.345
-16.867250000000002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 118951, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.958328008651733, 'TIME_S_1KI': 0.0921247236984282, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1244.7510617017745, 'W': 85.66, 'J_1KI': 10.46440182681755, 'W_1KI': 0.7201284562550967, 'W_D': 68.79275, 'J_D': 999.64801073879, 'W_D_1KI': 0.578328471387378, 'J_D_1KI': 0.004861905081818379}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.json
deleted file mode 100644
index 2fcbe89..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 111343, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.612449407577515, "TIME_S_1KI": 0.09531312617387276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1276.1634995460508, "W": 85.24, "J_1KI": 11.46155123847975, "W_1KI": 0.7655622715392975, "W_D": 68.50299999999999, "J_D": 1025.5869100117682, "W_D_1KI": 0.6152429878842853, "J_D_1KI": 0.005525654849288103}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.output
deleted file mode 100644
index 88be5a2..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_015.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.024092912673950195}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.5683, 0.4138, 0.4073,  ..., 0.3902, 0.8427, 0.0854])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 0.024092912673950195 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43581', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 4.109806299209595}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.5410, 0.2199, 0.7901,  ..., 0.5082, 0.9299, 0.4005])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 4.109806299209595 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '111343', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.612449407577515}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.4631, 0.6798, 0.1713,  ..., 0.4196, 0.8931, 0.1487])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.612449407577515 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.4631, 0.6798, 0.1713,  ..., 0.4196, 0.8931, 0.1487])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.612449407577515 seconds
-
-[19.22, 18.22, 18.5, 18.78, 18.53, 18.22, 18.28, 18.61, 18.44, 18.23]
-[85.24]
-14.971415996551514
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111343, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.612449407577515, 'TIME_S_1KI': 0.09531312617387276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1276.1634995460508, 'W': 85.24}
-[19.22, 18.22, 18.5, 18.78, 18.53, 18.22, 18.28, 18.61, 18.44, 18.23, 18.99, 19.53, 18.43, 18.28, 18.91, 18.45, 18.67, 18.31, 19.19, 18.34]
-334.74
-16.737000000000002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111343, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.612449407577515, 'TIME_S_1KI': 0.09531312617387276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1276.1634995460508, 'W': 85.24, 'J_1KI': 11.46155123847975, 'W_1KI': 0.7655622715392975, 'W_D': 68.50299999999999, 'J_D': 1025.5869100117682, 'W_D_1KI': 0.6152429878842853, 'J_D_1KI': 0.005525654849288103}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.json
deleted file mode 100644
index e3e64e8..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104212, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.081001043319702, "TIME_S_1KI": 0.0967355107216031, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1181.1547214508057, "W": 85.36, "J_1KI": 11.334152702671533, "W_1KI": 0.8190995278854643, "W_D": 68.42425, "J_D": 946.8091137444973, "W_D_1KI": 0.6565870533143975, "J_D_1KI": 0.006300493736943898}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.output
deleted file mode 100644
index f0ae3c3..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_025.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.02489328384399414}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.3735, 0.0521, 0.5930,  ..., 0.9245, 0.5806, 0.1020])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 0.02489328384399414 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42180', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 4.24988055229187}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.0848, 0.9892, 0.3897,  ..., 0.8536, 0.7541, 0.3970])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 4.24988055229187 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104212', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.081001043319702}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.7565, 0.9410, 0.8976,  ..., 0.0615, 0.0494, 0.4926])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.081001043319702 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.7565, 0.9410, 0.8976,  ..., 0.0615, 0.0494, 0.4926])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.081001043319702 seconds
-
-[19.13, 19.56, 18.72, 18.55, 18.88, 18.69, 18.9, 18.45, 18.62, 18.63]
-[85.36]
-13.837332725524902
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104212, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.081001043319702, 'TIME_S_1KI': 0.0967355107216031, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.1547214508057, 'W': 85.36}
-[19.13, 19.56, 18.72, 18.55, 18.88, 18.69, 18.9, 18.45, 18.62, 18.63, 19.24, 18.49, 18.65, 18.66, 20.06, 18.54, 18.65, 18.59, 18.9, 18.61]
-338.715
-16.93575
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104212, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.081001043319702, 'TIME_S_1KI': 0.0967355107216031, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.1547214508057, 'W': 85.36, 'J_1KI': 11.334152702671533, 'W_1KI': 0.8190995278854643, 'W_D': 68.42425, 'J_D': 946.8091137444973, 'W_D_1KI': 0.6565870533143975, 'J_D_1KI': 0.006300493736943898}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.json
deleted file mode 100644
index 96f01c7..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 108087, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.658639669418335, "TIME_S_1KI": 0.09861167087085713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1189.6392674064637, "W": 85.63000000000001, "J_1KI": 11.006312205968003, "W_1KI": 0.792232183333796, "W_D": 68.6405, "J_D": 953.6077792177201, "W_D_1KI": 0.635048618242712, "J_D_1KI": 0.005875346880223449}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.output
deleted file mode 100644
index 3e77f2c..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_035.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.024932384490966797}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.9497, 0.8791, 0.4095,  ..., 0.2660, 0.5237, 0.9335])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 0.024932384490966797 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42113', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 4.0910210609436035}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.3329, 0.1103, 0.0954,  ..., 0.6205, 0.8185, 0.8635])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 4.0910210609436035 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '108087', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.658639669418335}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.4504, 0.2201, 0.9468,  ..., 0.4623, 0.1299, 0.0544])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.658639669418335 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.4504, 0.2201, 0.9468,  ..., 0.4623, 0.1299, 0.0544])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.658639669418335 seconds
-
-[19.09, 18.49, 18.84, 18.58, 18.97, 18.67, 19.82, 19.26, 19.0, 18.71]
-[85.63]
-13.892786026000977
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 108087, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.658639669418335, 'TIME_S_1KI': 0.09861167087085713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.6392674064637, 'W': 85.63000000000001}
-[19.09, 18.49, 18.84, 18.58, 18.97, 18.67, 19.82, 19.26, 19.0, 18.71, 18.89, 18.81, 18.96, 18.53, 18.69, 19.02, 18.99, 18.72, 18.87, 18.45]
-339.78999999999996
-16.9895
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 108087, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.658639669418335, 'TIME_S_1KI': 0.09861167087085713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.6392674064637, 'W': 85.63000000000001, 'J_1KI': 11.006312205968003, 'W_1KI': 0.792232183333796, 'W_D': 68.6405, 'J_D': 953.6077792177201, 'W_D_1KI': 0.635048618242712, 'J_D_1KI': 0.005875346880223449}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.json
deleted file mode 100644
index f0db0de..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100525, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.002086639404297, "TIME_S_1KI": 0.09949849927286046, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1185.355191230774, "W": 85.6, "J_1KI": 11.791645772004715, "W_1KI": 0.8515294702810245, "W_D": 68.72399999999999, "J_D": 951.6629691839216, "W_D_1KI": 0.6836508331260879, "J_D_1KI": 0.0068008041096850325}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.output
deleted file mode 100644
index 351314e..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_045.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.02538919448852539}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.5332, 0.1841, 0.2305,  ..., 0.7034, 0.6077, 0.7798])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 0.02538919448852539 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '41356', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 4.319673299789429}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.4945, 0.7975, 0.9129,  ..., 0.8365, 0.5676, 0.7345])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 4.319673299789429 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100525', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.002086639404297}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.5847, 0.9197, 0.3856,  ..., 0.5045, 0.0806, 0.9239])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.002086639404297 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.5847, 0.9197, 0.3856,  ..., 0.5045, 0.0806, 0.9239])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.002086639404297 seconds
-
-[18.95, 18.66, 19.21, 18.65, 18.7, 18.61, 18.93, 18.45, 18.7, 18.82]
-[85.6]
-13.847607374191284
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100525, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.002086639404297, 'TIME_S_1KI': 0.09949849927286046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1185.355191230774, 'W': 85.6}
-[18.95, 18.66, 19.21, 18.65, 18.7, 18.61, 18.93, 18.45, 18.7, 18.82, 19.0, 18.49, 18.6, 18.39, 20.14, 18.43, 18.49, 18.66, 18.8, 18.45]
-337.5199999999999
-16.875999999999998
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100525, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.002086639404297, 'TIME_S_1KI': 0.09949849927286046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1185.355191230774, 'W': 85.6, 'J_1KI': 11.791645772004715, 'W_1KI': 0.8515294702810245, 'W_D': 68.72399999999999, 'J_D': 951.6629691839216, 'W_D_1KI': 0.6836508331260879, 'J_D_1KI': 0.0068008041096850325}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.json
deleted file mode 100644
index dcdc997..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105102, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.51480507850647, "TIME_S_1KI": 0.10004381532707722, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1208.6983181548117, "W": 85.70999999999998, "J_1KI": 11.500240891275253, "W_1KI": 0.8154935205800078, "W_D": 68.93374999999997, "J_D": 972.1165288659927, "W_D_1KI": 0.6558747692717548, "J_D_1KI": 0.0062403643058339025}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.output
deleted file mode 100644
index 74c7c49..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_055.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.02582073211669922}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.9834, 0.8900, 0.8704,  ..., 0.9063, 0.0304, 0.0345])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 0.02582073211669922 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '40664', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 4.062415599822998}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.3357, 0.2120, 0.4208,  ..., 0.4775, 0.1882, 0.4136])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 4.062415599822998 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105102', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.51480507850647}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.1109, 0.0481, 0.9697,  ..., 0.8619, 0.5058, 0.3084])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.51480507850647 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.1109, 0.0481, 0.9697,  ..., 0.8619, 0.5058, 0.3084])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.51480507850647 seconds
-
-[18.92, 18.5, 18.86, 19.4, 18.53, 18.55, 18.81, 18.88, 18.58, 19.05]
-[85.71]
-14.102185487747192
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105102, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.51480507850647, 'TIME_S_1KI': 0.10004381532707722, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1208.6983181548117, 'W': 85.70999999999998}
-[18.92, 18.5, 18.86, 19.4, 18.53, 18.55, 18.81, 18.88, 18.58, 19.05, 18.71, 18.29, 18.49, 18.92, 18.32, 18.49, 18.42, 18.56, 18.48, 18.21]
-335.525
-16.776249999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105102, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.51480507850647, 'TIME_S_1KI': 0.10004381532707722, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1208.6983181548117, 'W': 85.70999999999998, 'J_1KI': 11.500240891275253, 'W_1KI': 0.8154935205800078, 'W_D': 68.93374999999997, 'J_D': 972.1165288659927, 'W_D_1KI': 0.6558747692717548, 'J_D_1KI': 0.0062403643058339025}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.json
deleted file mode 100644
index 92c3f69..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103844, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 11.030998468399048, "TIME_S_1KI": 0.10622663291474758, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1189.5098596334458, "W": 85.7, "J_1KI": 11.45477696962218, "W_1KI": 0.8252763761026155, "W_D": 68.47900000000001, "J_D": 950.4836135103704, "W_D_1KI": 0.6594410847039791, "J_D_1KI": 0.006350305118292623}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.output
deleted file mode 100644
index 1f7dd97..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_065.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.0243070125579834}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.3510, 0.2711, 0.2123,  ..., 0.7532, 0.4454, 0.2960])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 0.0243070125579834 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43197', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 4.367758750915527}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.0208, 0.4742, 0.2939,  ..., 0.4314, 0.8353, 0.3494])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 4.367758750915527 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103844', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 11.030998468399048}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.6493, 0.5875, 0.0698,  ..., 0.4859, 0.5621, 0.3281])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 11.030998468399048 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.6493, 0.5875, 0.0698,  ..., 0.4859, 0.5621, 0.3281])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 11.030998468399048 seconds
-
-[18.89, 18.36, 18.45, 18.25, 18.55, 18.4, 23.39, 18.39, 19.05, 18.39]
-[85.7]
-13.879928350448608
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103844, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 11.030998468399048, 'TIME_S_1KI': 0.10622663291474758, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.5098596334458, 'W': 85.7}
-[18.89, 18.36, 18.45, 18.25, 18.55, 18.4, 23.39, 18.39, 19.05, 18.39, 18.99, 18.45, 23.17, 18.68, 18.53, 19.54, 18.63, 18.63, 18.53, 18.57]
-344.41999999999996
-17.220999999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103844, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 11.030998468399048, 'TIME_S_1KI': 0.10622663291474758, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.5098596334458, 'W': 85.7, 'J_1KI': 11.45477696962218, 'W_1KI': 0.8252763761026155, 'W_D': 68.47900000000001, 'J_D': 950.4836135103704, 'W_D_1KI': 0.6594410847039791, 'J_D_1KI': 0.006350305118292623}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.json
deleted file mode 100644
index 52891fa..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105572, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.73673415184021, "TIME_S_1KI": 0.1017005849263082, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1231.2992347717284, "W": 86.3, "J_1KI": 11.663123127076577, "W_1KI": 0.8174515970143598, "W_D": 69.285, "J_D": 988.534965019226, "W_D_1KI": 0.6562819687038229, "J_D_1KI": 0.006216439668698357}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.output
deleted file mode 100644
index cd9e97b..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_075.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.024676084518432617}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.7484, 0.8878, 0.0538,  ..., 0.1893, 0.9984, 0.9508])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 0.024676084518432617 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42551', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 4.232024908065796}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.9890, 0.2220, 0.6515,  ..., 0.9314, 0.4703, 0.0858])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 4.232024908065796 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105572', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.73673415184021}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.1882, 0.6590, 0.2723,  ..., 0.2031, 0.5459, 0.8199])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.73673415184021 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.1882, 0.6590, 0.2723,  ..., 0.2031, 0.5459, 0.8199])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.73673415184021 seconds
-
-[18.77, 18.76, 20.69, 18.88, 18.46, 19.3, 18.82, 18.52, 18.64, 18.63]
-[86.3]
-14.267662048339844
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105572, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.73673415184021, 'TIME_S_1KI': 0.1017005849263082, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1231.2992347717284, 'W': 86.3}
-[18.77, 18.76, 20.69, 18.88, 18.46, 19.3, 18.82, 18.52, 18.64, 18.63, 19.02, 18.96, 18.67, 18.88, 18.59, 18.43, 18.73, 19.37, 19.23, 18.32]
-340.29999999999995
-17.014999999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105572, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.73673415184021, 'TIME_S_1KI': 0.1017005849263082, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1231.2992347717284, 'W': 86.3, 'J_1KI': 11.663123127076577, 'W_1KI': 0.8174515970143598, 'W_D': 69.285, 'J_D': 988.534965019226, 'W_D_1KI': 0.6562819687038229, 'J_D_1KI': 0.006216439668698357}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.json
deleted file mode 100644
index a627580..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 106480, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.531679153442383, "TIME_S_1KI": 0.09890758032909826, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1190.395678498745, "W": 86.17, "J_1KI": 11.17952365231729, "W_1KI": 0.8092599549211119, "W_D": 69.328, "J_D": 957.7318277702332, "W_D_1KI": 0.6510894064613073, "J_D_1KI": 0.006114663847307544}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.output
deleted file mode 100644
index 07afbfa..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_085.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.02328658103942871}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.2863, 0.5847, 0.8482,  ..., 0.4284, 0.4105, 0.6616])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 0.02328658103942871 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '45090', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 4.446286916732788}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.5970, 0.5454, 0.1693,  ..., 0.9920, 0.1544, 0.9728])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 4.446286916732788 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '106480', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.531679153442383}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.7784, 0.7505, 0.3000,  ..., 0.8404, 0.5502, 0.8322])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.531679153442383 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.7784, 0.7505, 0.3000,  ..., 0.8404, 0.5502, 0.8322])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.531679153442383 seconds
-
-[19.21, 19.37, 18.51, 18.62, 19.05, 18.73, 19.09, 18.38, 18.96, 19.04]
-[86.17]
-13.814502477645874
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 106480, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.531679153442383, 'TIME_S_1KI': 0.09890758032909826, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1190.395678498745, 'W': 86.17}
-[19.21, 19.37, 18.51, 18.62, 19.05, 18.73, 19.09, 18.38, 18.96, 19.04, 18.74, 18.5, 18.95, 18.29, 18.76, 18.43, 18.59, 18.5, 18.53, 18.17]
-336.8399999999999
-16.841999999999995
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 106480, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.531679153442383, 'TIME_S_1KI': 0.09890758032909826, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1190.395678498745, 'W': 86.17, 'J_1KI': 11.17952365231729, 'W_1KI': 0.8092599549211119, 'W_D': 69.328, 'J_D': 957.7318277702332, 'W_D_1KI': 0.6510894064613073, 'J_D_1KI': 0.006114663847307544}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.json
deleted file mode 100644
index a3e924b..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104065, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.379182815551758, "TIME_S_1KI": 0.09973749882815315, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1212.61552161932, "W": 85.99, "J_1KI": 11.652481829811366, "W_1KI": 0.8263104790275307, "W_D": 69.18924999999999, "J_D": 975.6943653820156, "W_D_1KI": 0.6648657089319174, "J_D_1KI": 0.006388946417449838}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.output
deleted file mode 100644
index 6d4b807..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_095.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.025043249130249023}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2035, 0.4087, 0.6865,  ..., 0.9100, 0.1998, 0.7367])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 0.025043249130249023 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '41927', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 4.230342864990234}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.5515, 0.9023, 0.2427,  ..., 0.4631, 0.3433, 0.5826])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 4.230342864990234 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104065', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.379182815551758}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.6625, 0.9150, 0.9753,  ..., 0.4828, 0.8648, 0.9099])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.379182815551758 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.6625, 0.9150, 0.9753,  ..., 0.4828, 0.8648, 0.9099])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.379182815551758 seconds
-
-[19.15, 18.78, 18.48, 18.51, 18.58, 18.54, 18.54, 18.39, 18.69, 18.84]
-[85.99]
-14.101820230484009
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104065, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.379182815551758, 'TIME_S_1KI': 0.09973749882815315, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1212.61552161932, 'W': 85.99}
-[19.15, 18.78, 18.48, 18.51, 18.58, 18.54, 18.54, 18.39, 18.69, 18.84, 20.02, 18.38, 18.49, 18.45, 18.71, 18.57, 18.85, 18.98, 18.94, 18.26]
-336.015
-16.80075
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104065, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.379182815551758, 'TIME_S_1KI': 0.09973749882815315, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1212.61552161932, 'W': 85.99, 'J_1KI': 11.652481829811366, 'W_1KI': 0.8263104790275307, 'W_D': 69.18924999999999, 'J_D': 975.6943653820156, 'W_D_1KI': 0.6648657089319174, 'J_D_1KI': 0.006388946417449838}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.json
deleted file mode 100644
index 725bf61..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100814, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.194071531295776, "TIME_S_1KI": 0.10111761790322552, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1196.0577488923072, "W": 86.07, "J_1KI": 11.8640044923553, "W_1KI": 0.8537504711647191, "W_D": 69.22699999999999, "J_D": 962.001740241289, "W_D_1KI": 0.6866804213700477, "J_D_1KI": 0.006811359745373139}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.output
deleted file mode 100644
index 304c321..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_105.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.023812294006347656}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.9211, 0.8071, 0.6204,  ..., 0.9847, 0.2575, 0.7368])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 0.023812294006347656 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44094', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 4.5924482345581055}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.8718, 0.5640, 0.5184,  ..., 0.6471, 0.8654, 0.3406])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 4.5924482345581055 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100814', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.194071531295776}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.1208, 0.0816, 0.0441,  ..., 0.8026, 0.5634, 0.6977])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.194071531295776 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.1208, 0.0816, 0.0441,  ..., 0.8026, 0.5634, 0.6977])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.194071531295776 seconds
-
-[19.02, 18.57, 18.51, 18.59, 18.84, 18.86, 18.78, 18.52, 18.61, 18.92]
-[86.07]
-13.896337270736694
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100814, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.194071531295776, 'TIME_S_1KI': 0.10111761790322552, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.0577488923072, 'W': 86.07}
-[19.02, 18.57, 18.51, 18.59, 18.84, 18.86, 18.78, 18.52, 18.61, 18.92, 19.41, 18.61, 18.61, 18.39, 18.76, 18.27, 18.39, 18.46, 18.45, 21.93]
-336.86
-16.843
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100814, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.194071531295776, 'TIME_S_1KI': 0.10111761790322552, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1196.0577488923072, 'W': 86.07, 'J_1KI': 11.8640044923553, 'W_1KI': 0.8537504711647191, 'W_D': 69.22699999999999, 'J_D': 962.001740241289, 'W_D_1KI': 0.6866804213700477, 'J_D_1KI': 0.006811359745373139}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.json
deleted file mode 100644
index efcda8e..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103838, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.47437047958374, "TIME_S_1KI": 0.10087222865987153, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1219.8774162769319, "W": 86.43, "J_1KI": 11.74789013922583, "W_1KI": 0.8323542441110191, "W_D": 69.44200000000001, "J_D": 980.1079201793672, "W_D_1KI": 0.6687532502552053, "J_D_1KI": 0.006440351800450753}
diff --git a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.output
deleted file mode 100644
index 6c56636..0000000
--- a/pytorch/output_as-caida_16core/xeon_4216_16_csr_10_10_10_as-caida_G_115.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.02495574951171875}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.7637, 0.0348, 0.3593,  ..., 0.5639, 0.9253, 0.8280])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 0.02495574951171875 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42074', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 4.254471302032471}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.9777, 0.8326, 0.9278,  ..., 0.9597, 0.3522, 0.8734])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 4.254471302032471 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103838', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '16']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.47437047958374}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.3121, 0.4413, 0.1857,  ..., 0.7457, 0.4579, 0.8211])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.47437047958374 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.3121, 0.4413, 0.1857,  ..., 0.7457, 0.4579, 0.8211])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.47437047958374 seconds
-
-[19.18, 18.73, 18.88, 18.69, 19.03, 18.53, 18.6, 19.09, 18.57, 18.44]
-[86.43]
-14.11405086517334
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103838, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.47437047958374, 'TIME_S_1KI': 0.10087222865987153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.8774162769319, 'W': 86.43}
-[19.18, 18.73, 18.88, 18.69, 19.03, 18.53, 18.6, 19.09, 18.57, 18.44, 19.57, 18.51, 18.39, 18.4, 18.57, 18.52, 19.38, 21.21, 18.66, 18.81]
-339.76000000000005
-16.988000000000003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103838, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.47437047958374, 'TIME_S_1KI': 0.10087222865987153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.8774162769319, 'W': 86.43, 'J_1KI': 11.74789013922583, 'W_1KI': 0.8323542441110191, 'W_D': 69.44200000000001, 'J_D': 980.1079201793672, 'W_D_1KI': 0.6687532502552053, 'J_D_1KI': 0.006440351800450753}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..9477087
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 6684, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.596766233444214, "TIME_S_1KI": 1.5853929134416838, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 327.90283897399905, "W": 22.36582722247819, "J_1KI": 49.057875370137495, "W_1KI": 3.3461740308914107, "W_D": 3.775827222478192, "J_D": 55.35697175025943, "W_D_1KI": 0.5649053295149898, "J_D_1KI": 0.08451605767728752}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..3d52824
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_010.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.1712493896484375}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.6452, 0.0568, 0.3264,  ..., 0.3600, 0.0611, 0.3174])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.1712493896484375 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 6131 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 9.630002737045288}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.1282, 0.3720, 0.9892,  ..., 0.5185, 0.4502, 0.8046])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 9.630002737045288 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 6684 -m matrices/as-caida_pruned/as-caida_G_010.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.596766233444214}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.1347, 0.6614, 0.0574,  ..., 0.7477, 0.0306, 0.5424])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.596766233444214 seconds
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.1347, 0.6614, 0.0574,  ..., 0.7477, 0.0306, 0.5424])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.596766233444214 seconds
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+[20.44, 20.56, 20.56, 20.48, 20.76, 20.76, 20.76, 21.24, 21.16, 21.0]
+[21.08, 21.04, 21.04, 21.72, 22.72, 24.6, 25.48, 26.32, 25.84, 24.96, 25.04, 25.04, 24.68, 24.88]
+14.66088581085205
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 6684, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.596766233444214, 'TIME_S_1KI': 1.5853929134416838, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.90283897399905, 'W': 22.36582722247819}
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+371.8
+18.59
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 6684, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.596766233444214, 'TIME_S_1KI': 1.5853929134416838, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.90283897399905, 'W': 22.36582722247819, 'J_1KI': 49.057875370137495, 'W_1KI': 3.3461740308914107, 'W_D': 3.775827222478192, 'J_D': 55.35697175025943, 'W_D_1KI': 0.5649053295149898, 'J_D_1KI': 0.08451605767728752}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..6287bd1
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 5943, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.110217094421387, "TIME_S_1KI": 1.7011975592161177, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 324.9486833190918, "W": 23.872072143622507, "J_1KI": 54.67755061738042, "W_1KI": 4.0168386578533575, "W_D": 5.442072143622507, "J_D": 74.0779504585266, "W_D_1KI": 0.9157112811076068, "J_D_1KI": 0.15408232897654497}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..49afc5f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_020.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_020.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.17665648460388184}
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.0371, 0.9727, 0.1237,  ..., 0.8123, 0.0651, 0.8577])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.17665648460388184 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 5943 -m matrices/as-caida_pruned/as-caida_G_020.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.110217094421387}
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.6592, 0.6422, 0.6073,  ..., 0.5638, 0.6280, 0.3780])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.110217094421387 seconds
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.6592, 0.6422, 0.6073,  ..., 0.5638, 0.6280, 0.3780])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
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+Density: 8.221062021893506e-05
+Time: 10.110217094421387 seconds
+
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+[20.24, 20.48, 23.68, 25.96, 28.0, 29.0, 29.0, 29.64, 26.08, 25.8, 24.64, 24.88, 24.88]
+13.612085342407227
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5943, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.110217094421387, 'TIME_S_1KI': 1.7011975592161177, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.9486833190918, 'W': 23.872072143622507}
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+368.6
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+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5943, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 10.110217094421387, 'TIME_S_1KI': 1.7011975592161177, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.9486833190918, 'W': 23.872072143622507, 'J_1KI': 54.67755061738042, 'W_1KI': 4.0168386578533575, 'W_D': 5.442072143622507, 'J_D': 74.0779504585266, 'W_D_1KI': 0.9157112811076068, 'J_D_1KI': 0.15408232897654497}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..6a78561
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 5511, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.074766874313354, "TIME_S_1KI": 1.8281195562172663, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 299.8301674938202, "W": 22.025321363262098, "J_1KI": 54.4057643792089, "W_1KI": 3.9966106629036653, "W_D": 3.413321363262096, "J_D": 46.465461237907405, "W_D_1KI": 0.6193651539216287, "J_D_1KI": 0.11238707202352181}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..5394c10
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_030.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_030.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.1905057430267334}
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+tensor(indices=tensor([[ 1809, 21783,   106,  ...,  7018,   160,   882],
+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=86850, layout=torch.sparse_coo)
+tensor([0.3570, 0.4245, 0.5682,  ..., 0.1493, 0.7612, 0.0314])
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+Matrix: as-caida_G_030
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+Density: 8.820467912752026e-05
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 5511 -m matrices/as-caida_pruned/as-caida_G_030.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.074766874313354}
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+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
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+tensor(indices=tensor([[ 1809, 21783,   106,  ...,  7018,   160,   882],
+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=86850, layout=torch.sparse_coo)
+tensor([0.6012, 0.1948, 0.6091,  ..., 0.0586, 0.9511, 0.3949])
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diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..b546f1e
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_040.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 5410, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.350580930709839, "TIME_S_1KI": 1.9132312256395267, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 304.02454459190375, "W": 22.31971265525147, "J_1KI": 56.19677349203397, "W_1KI": 4.1256400471814185, "W_D": 3.7447126552514725, "J_D": 51.00802941441544, "W_D_1KI": 0.6921834852590522, "J_D_1KI": 0.1279451913602684}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..9dd6711
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_040.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_040.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.19407224655151367}
+
+tensor(indices=tensor([[  106,   329,  1040,  ..., 10144,   882, 16085],
+                       [    2,     2,     2,  ..., 31371, 31373, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=89658, layout=torch.sparse_coo)
+tensor([0.9858, 0.9455, 0.4455,  ..., 0.7237, 0.4288, 0.4336])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_040
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+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 89658
+Density: 9.105647807962247e-05
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 5410 -m matrices/as-caida_pruned/as-caida_G_040.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 10.350580930709839}
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+                       [    2,     2,     2,  ..., 31371, 31373, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=89658, layout=torch.sparse_coo)
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+                       [    2,     2,     2,  ..., 31371, 31373, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=89658, layout=torch.sparse_coo)
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diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..cea6c2e
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_050.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 5314, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 11.12612009048462, "TIME_S_1KI": 2.093737314731769, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 311.38884366989146, "W": 22.878302616552556, "J_1KI": 58.59782530483467, "W_1KI": 4.305288411093819, "W_D": 4.562302616552561, "J_D": 62.09595878028885, "W_D_1KI": 0.85854396246755, "J_D_1KI": 0.16156265759645277}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..4532f75
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_050.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_050.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.19758129119873047}
+
+tensor(indices=tensor([[ 5326,   106,   329,  ...,   882,  2232, 16085],
+                       [    0,     2,     2,  ..., 31373, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=90392, layout=torch.sparse_coo)
+tensor([0.5739, 0.3810, 0.5582,  ..., 0.5489, 0.7422, 0.9564])
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 5314 -m matrices/as-caida_pruned/as-caida_G_050.mtx -c 1']
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+                       [    0,     2,     2,  ..., 31373, 31378, 31378]]),
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diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..6d47c16
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_060.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 5085, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.525958061218262, "TIME_S_1KI": 2.07000158529366, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 302.745055847168, "W": 22.22538985335849, "J_1KI": 59.536884139069414, "W_1KI": 4.370774799087215, "W_D": 3.7583898533584907, "J_D": 51.19523003005984, "W_D_1KI": 0.7391130488413945, "J_D_1KI": 0.14535163202387308}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..5c16455
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_060.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_060.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.20647120475769043}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160,   882],
+                       [    2,     2,     2,  ..., 31355, 31360, 31373]]),
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diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..f1d701e
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_070.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 6077, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.154123067855835, "TIME_S_1KI": 1.6709104933118044, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 303.88201436996457, "W": 22.316826518529073, "J_1KI": 50.00526812077745, "W_1KI": 3.672342688584675, "W_D": 3.690826518529075, "J_D": 50.25695728778838, "W_D_1KI": 0.607343511359071, "J_D_1KI": 0.09994133805480847}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..c064c7f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_070.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_070.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.1727752685546875}
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+tensor(indices=tensor([[  106,   329,  1040,  ..., 16263,  2242,  2242],
+                       [    2,     2,     2,  ..., 31240, 31283, 31284]]),
+       values=tensor([3., 3., 3.,  ..., 1., 3., 3.]),
+       size=(31379, 31379), nnz=78684, layout=torch.sparse_coo)
+tensor([0.0538, 0.1111, 0.8161,  ..., 0.1419, 0.7593, 0.0124])
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+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.154123067855835}
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+                       [    2,     2,     2,  ..., 31240, 31283, 31284]]),
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+                       [    2,     2,     2,  ..., 31240, 31283, 31284]]),
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diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..bbcd998
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_080.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4917, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.629328727722168, "TIME_S_1KI": 2.161750808973392, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 303.4654243469238, "W": 22.302782898169355, "J_1KI": 61.71759697924014, "W_1KI": 4.535851718155248, "W_D": 3.767782898169351, "J_D": 51.26677873611439, "W_D_1KI": 0.7662767740836589, "J_D_1KI": 0.15584233762124444}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..b6068b1
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_080.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_080.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.21352767944335938}
+
+tensor(indices=tensor([[22754, 22754,   106,  ...,  4133, 31329, 12170],
+                       [    0,     1,     2,  ..., 31373, 31373, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 1., 1., 3.]),
+       size=(31379, 31379), nnz=98112, layout=torch.sparse_coo)
+tensor([0.0366, 0.2556, 0.7847,  ..., 0.9501, 0.2572, 0.0287])
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 4917 -m matrices/as-caida_pruned/as-caida_G_080.mtx -c 1']
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diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..e4070a2
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_090.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4831, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.462406873703003, "TIME_S_1KI": 2.165681406272615, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.1915158748627, "W": 21.996883087414542, "J_1KI": 66.69251001342636, "W_1KI": 4.553277393379123, "W_D": 3.7938830874145424, "J_D": 55.5695522010327, "W_D_1KI": 0.7853204486471833, "J_D_1KI": 0.1625585693742876}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..ccea40b
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_090.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_090.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.2173292636871338}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   882, 31211, 12170],
+                       [    2,     2,     2,  ..., 31373, 31376, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=100924, layout=torch.sparse_coo)
+tensor([0.3815, 0.7351, 0.7102,  ..., 0.3255, 0.6582, 0.9932])
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diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..b44334e
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_100.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4664, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.589112997055054, "TIME_S_1KI": 2.2703930096601743, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 300.8093952274322, "W": 22.10024910726145, "J_1KI": 64.49601098358323, "W_1KI": 4.738475366050912, "W_D": 3.6292491072614546, "J_D": 49.39818658995631, "W_D_1KI": 0.7781408892070014, "J_D_1KI": 0.16683981329481162}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..a4f6ee1
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_100.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_100.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.2251274585723877}
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+tensor(indices=tensor([[  106,   329,  1040,  ..., 25970,  5128, 12170],
+                       [    2,     2,     2,  ..., 31373, 31377, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=102888, layout=torch.sparse_coo)
+tensor([0.2175, 0.7006, 0.4911,  ..., 0.0119, 0.6684, 0.1169])
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diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..6208968
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4636, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.19464373588562, "TIME_S_1KI": 2.1990171992850778, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.45444195747376, "W": 22.879855937024917, "J_1KI": 72.35859403741884, "W_1KI": 4.935257967434193, "W_D": 4.441855937024918, "J_D": 65.12454924154285, "W_D_1KI": 0.9581225058293612, "J_D_1KI": 0.20667008322462493}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..65aefa3
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_110.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_110.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.2264866828918457}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   882,  2616, 12170],
+                       [    2,     2,     2,  ..., 31373, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=104846, layout=torch.sparse_coo)
+tensor([0.9539, 0.4312, 0.6583,  ..., 0.3840, 0.0268, 0.7022])
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diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..94c893b
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4561, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.306824684143066, "TIME_S_1KI": 2.2597730068281225, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 300.2287587738037, "W": 22.06639017218802, "J_1KI": 65.82520473006001, "W_1KI": 4.8380596737969785, "W_D": 3.5303901721880173, "J_D": 48.03344140624998, "W_D_1KI": 0.7740386257811922, "J_D_1KI": 0.16970809598359837}
diff --git a/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..c95dd8c
--- /dev/null
+++ b/pytorch/output_as-caida_1core/altra_1_coo_10_10_10_as-caida_G_120.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.23018765449523926}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.9774, 0.2338, 0.9789,  ..., 0.6740, 0.3484, 0.3404])
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 4561 -m matrices/as-caida_pruned/as-caida_G_120.mtx -c 1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.306824684143066}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.4499, 0.4864, 0.0865,  ..., 0.0486, 0.2555, 0.8299])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.306824684143066 seconds
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.4499, 0.4864, 0.0865,  ..., 0.0486, 0.2555, 0.8299])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.306824684143066 seconds
+
+[20.36, 20.4, 20.64, 20.8, 20.64, 20.52, 20.48, 20.08, 19.88, 20.04]
+[20.2, 20.32, 20.32, 21.24, 23.16, 25.0, 25.96, 26.48, 26.24, 24.84, 24.68, 24.6, 24.68]
+13.605703353881836
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4561, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.306824684143066, 'TIME_S_1KI': 2.2597730068281225, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 300.2287587738037, 'W': 22.06639017218802}
+[20.36, 20.4, 20.64, 20.8, 20.64, 20.52, 20.48, 20.08, 19.88, 20.04, 20.48, 20.64, 20.36, 20.6, 20.6, 20.88, 21.12, 21.12, 21.12, 20.8]
+370.72
+18.536
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4561, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.306824684143066, 'TIME_S_1KI': 2.2597730068281225, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 300.2287587738037, 'W': 22.06639017218802, 'J_1KI': 65.82520473006001, 'W_1KI': 4.8380596737969785, 'W_D': 3.5303901721880173, 'J_D': 48.03344140624998, 'W_D_1KI': 0.7740386257811922, 'J_D_1KI': 0.16970809598359837}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.json
deleted file mode 100644
index fba9bea..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 6124, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.514057636260986, "TIME_S_1KI": 1.7168611424332114, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 323.7508854293823, "W": 22.266094017640313, "J_1KI": 52.865918587423636, "W_1KI": 3.635874268066674, "W_D": 3.974094017640315, "J_D": 57.78366227912903, "W_D_1KI": 0.6489376253494963, "J_D_1KI": 0.1059663006775794}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.output
deleted file mode 100644
index 3a8f57d..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_005.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.18268108367919922}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.1439, 0.3000, 0.8170,  ..., 0.9615, 0.9230, 0.5483])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 0.18268108367919922 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5747 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 9.852523803710938}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.0400, 0.8950, 0.6861,  ..., 0.6705, 0.3750, 0.3934])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 9.852523803710938 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 6124 -m matrices/as-caida_pruned/as-caida_G_005.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.514057636260986}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.4293, 0.3058, 0.4645,  ..., 0.7892, 0.5701, 0.7940])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.514057636260986 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.4293, 0.3058, 0.4645,  ..., 0.7892, 0.5701, 0.7940])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.514057636260986 seconds
-
-[20.6, 20.36, 20.44, 20.36, 20.24, 20.24, 20.12, 20.12, 20.12, 20.2]
-[20.4, 20.4, 20.6, 21.52, 23.12, 24.8, 25.76, 26.28, 25.56, 24.96, 25.04, 24.88, 24.92, 24.72]
-14.54008436203003
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 6124, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.514057636260986, 'TIME_S_1KI': 1.7168611424332114, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.7508854293823, 'W': 22.266094017640313}
-[20.6, 20.36, 20.44, 20.36, 20.24, 20.24, 20.12, 20.12, 20.12, 20.2, 20.36, 20.32, 20.16, 20.2, 20.2, 20.2, 20.36, 20.52, 20.84, 20.92]
-365.84
-18.291999999999998
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 6124, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.514057636260986, 'TIME_S_1KI': 1.7168611424332114, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.7508854293823, 'W': 22.266094017640313, 'J_1KI': 52.865918587423636, 'W_1KI': 3.635874268066674, 'W_D': 3.974094017640315, 'J_D': 57.78366227912903, 'W_D_1KI': 0.6489376253494963, 'J_D_1KI': 0.1059663006775794}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.json
deleted file mode 100644
index 0d7a637..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 5301, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.19014310836792, "TIME_S_1KI": 1.9223058118030407, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 311.1123313903809, "W": 22.88424377272302, "J_1KI": 58.68936641961534, "W_1KI": 4.31696732177382, "W_D": 4.340243772723021, "J_D": 59.00581082534796, "W_D_1KI": 0.8187594364691607, "J_D_1KI": 0.1544537703205359}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.output
deleted file mode 100644
index d4b8de4..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_015.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.19806742668151855}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.8497, 0.2593, 0.7660,  ..., 0.4860, 0.0646, 0.1972])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 0.19806742668151855 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5301 -m matrices/as-caida_pruned/as-caida_G_015.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.19014310836792}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.9506, 0.6483, 0.4255,  ..., 0.7600, 0.5524, 0.6427])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.19014310836792 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.9506, 0.6483, 0.4255,  ..., 0.7600, 0.5524, 0.6427])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.19014310836792 seconds
-
-[20.32, 20.32, 20.32, 20.56, 20.56, 20.64, 20.56, 20.36, 20.28, 20.12]
-[19.96, 20.12, 21.44, 23.84, 25.76, 25.76, 26.84, 27.28, 26.2, 26.24, 25.04, 25.28, 24.96]
-13.595045328140259
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5301, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.19014310836792, 'TIME_S_1KI': 1.9223058118030407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 311.1123313903809, 'W': 22.88424377272302}
-[20.32, 20.32, 20.32, 20.56, 20.56, 20.64, 20.56, 20.36, 20.28, 20.12, 19.96, 20.04, 20.44, 20.4, 21.0, 21.24, 21.2, 21.32, 20.96, 20.96]
-370.88
-18.544
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 5301, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.19014310836792, 'TIME_S_1KI': 1.9223058118030407, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 311.1123313903809, 'W': 22.88424377272302, 'J_1KI': 58.68936641961534, 'W_1KI': 4.31696732177382, 'W_D': 4.340243772723021, 'J_D': 59.00581082534796, 'W_D_1KI': 0.8187594364691607, 'J_D_1KI': 0.1544537703205359}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.json
deleted file mode 100644
index 893b380..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4892, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.115711212158203, "TIME_S_1KI": 2.067806870841824, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 307.40472288131707, "W": 22.646829011678925, "J_1KI": 62.838250793400874, "W_1KI": 4.629359977857507, "W_D": 4.094829011678925, "J_D": 55.58260615348808, "W_D_1KI": 0.8370459958460599, "J_D_1KI": 0.1711050686520973}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.output
deleted file mode 100644
index b73490e..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_025.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.2146143913269043}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.5233, 0.6684, 0.0720,  ..., 0.8096, 0.6371, 0.4657])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 0.2146143913269043 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4892 -m matrices/as-caida_pruned/as-caida_G_025.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.115711212158203}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.2946, 0.5991, 0.8521,  ..., 0.8859, 0.0862, 0.1832])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.115711212158203 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.2946, 0.5991, 0.8521,  ..., 0.8859, 0.0862, 0.1832])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.115711212158203 seconds
-
-[20.32, 20.6, 20.76, 20.72, 20.72, 20.72, 20.8, 20.6, 20.56, 20.76]
-[20.72, 20.68, 21.0, 22.84, 24.56, 25.56, 26.44, 26.76, 25.56, 25.2, 25.52, 25.52, 25.52]
-13.573852777481079
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4892, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.115711212158203, 'TIME_S_1KI': 2.067806870841824, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 307.40472288131707, 'W': 22.646829011678925}
-[20.32, 20.6, 20.76, 20.72, 20.72, 20.72, 20.8, 20.6, 20.56, 20.76, 20.0, 20.0, 20.16, 20.32, 20.28, 20.64, 21.0, 21.04, 21.0, 21.16]
-371.03999999999996
-18.552
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4892, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.115711212158203, 'TIME_S_1KI': 2.067806870841824, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 307.40472288131707, 'W': 22.646829011678925, 'J_1KI': 62.838250793400874, 'W_1KI': 4.629359977857507, 'W_D': 4.094829011678925, 'J_D': 55.58260615348808, 'W_D_1KI': 0.8370459958460599, 'J_D_1KI': 0.1711050686520973}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.json
deleted file mode 100644
index 1ccb8e4..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4974, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.731950759887695, "TIME_S_1KI": 2.1576097225347195, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 342.8746109008789, "W": 23.52079658107547, "J_1KI": 68.93337573399253, "W_1KI": 4.728748810027236, "W_D": 4.868796581075472, "J_D": 70.97492329978948, "W_D_1KI": 0.9788493327453702, "J_D_1KI": 0.19679319114301774}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.output
deleted file mode 100644
index 5ba179d..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_035.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.22348880767822266}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.7766, 0.4782, 0.2449,  ..., 0.0046, 0.9056, 0.4984])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 0.22348880767822266 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4698 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 9.915407419204712}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.3469, 0.1883, 0.3355,  ..., 0.8764, 0.8263, 0.9306])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 9.915407419204712 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4974 -m matrices/as-caida_pruned/as-caida_G_035.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.731950759887695}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.3179, 0.6318, 0.5104,  ..., 0.0206, 0.4518, 0.3308])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.731950759887695 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.3179, 0.6318, 0.5104,  ..., 0.0206, 0.4518, 0.3308])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.731950759887695 seconds
-
-[20.48, 20.6, 20.6, 20.36, 20.4, 20.56, 20.6, 20.88, 21.16, 20.88]
-[20.76, 20.76, 20.76, 23.6, 25.72, 27.76, 28.8, 29.72, 26.76, 25.88, 25.56, 25.32, 24.96, 24.96]
-14.577508449554443
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4974, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.731950759887695, 'TIME_S_1KI': 2.1576097225347195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.8746109008789, 'W': 23.52079658107547}
-[20.48, 20.6, 20.6, 20.36, 20.4, 20.56, 20.6, 20.88, 21.16, 20.88, 20.6, 20.68, 20.72, 20.76, 20.96, 20.96, 21.04, 20.88, 20.64, 20.52]
-373.03999999999996
-18.651999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4974, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.731950759887695, 'TIME_S_1KI': 2.1576097225347195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.8746109008789, 'W': 23.52079658107547, 'J_1KI': 68.93337573399253, 'W_1KI': 4.728748810027236, 'W_D': 4.868796581075472, 'J_D': 70.97492329978948, 'W_D_1KI': 0.9788493327453702, 'J_D_1KI': 0.19679319114301774}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.json
deleted file mode 100644
index 66cd4e3..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4843, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.373887062072754, "TIME_S_1KI": 2.1420373863458093, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.9727068328857, "W": 22.843118658084368, "J_1KI": 68.75339806584466, "W_1KI": 4.716729022937098, "W_D": 4.402118658084369, "J_D": 64.16748025178906, "W_D_1KI": 0.9089652401578296, "J_D_1KI": 0.18768640102371045}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.output
deleted file mode 100644
index f96d685..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_045.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.23405838012695312}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.2593, 0.7313, 0.0475,  ..., 0.6609, 0.0319, 0.0461])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 0.23405838012695312 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4486 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 9.72571873664856}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.8910, 0.8980, 0.3385,  ..., 0.3949, 0.7661, 0.7330])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 9.72571873664856 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4843 -m matrices/as-caida_pruned/as-caida_G_045.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.373887062072754}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.4834, 0.2409, 0.6592,  ..., 0.5071, 0.3977, 0.4858])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.373887062072754 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.4834, 0.2409, 0.6592,  ..., 0.5071, 0.3977, 0.4858])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.373887062072754 seconds
-
-[20.88, 21.0, 21.08, 21.08, 20.96, 20.8, 20.68, 20.28, 20.6, 20.8]
-[20.6, 20.88, 21.36, 22.44, 24.12, 25.6, 26.4, 26.48, 26.48, 26.88, 25.56, 25.12, 25.0, 24.48]
-14.576499462127686
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4843, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.373887062072754, 'TIME_S_1KI': 2.1420373863458093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.9727068328857, 'W': 22.843118658084368}
-[20.88, 21.0, 21.08, 21.08, 20.96, 20.8, 20.68, 20.28, 20.6, 20.8, 19.96, 19.92, 20.2, 20.2, 20.36, 20.36, 20.16, 20.0, 20.24, 20.16]
-368.82
-18.441
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4843, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.373887062072754, 'TIME_S_1KI': 2.1420373863458093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.9727068328857, 'W': 22.843118658084368, 'J_1KI': 68.75339806584466, 'W_1KI': 4.716729022937098, 'W_D': 4.402118658084369, 'J_D': 64.16748025178906, 'W_D_1KI': 0.9089652401578296, 'J_D_1KI': 0.18768640102371045}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.json
deleted file mode 100644
index 77336b8..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4597, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.816685914993286, "TIME_S_1KI": 2.35298801718366, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 324.7557634544372, "W": 22.27027274930315, "J_1KI": 70.64515193701048, "W_1KI": 4.84452311274813, "W_D": 3.9532727493031494, "J_D": 57.64851307821269, "W_D_1KI": 0.8599679680885685, "J_D_1KI": 0.18707156147238815}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.output
deleted file mode 100644
index 8734f9a..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_055.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.22839808464050293}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.2187, 0.5179, 0.0914,  ..., 0.1693, 0.7207, 0.1990])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 0.22839808464050293 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4597 -m matrices/as-caida_pruned/as-caida_G_055.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.816685914993286}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.0686, 0.3478, 0.1639,  ..., 0.3403, 0.2083, 0.0411])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.816685914993286 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.0686, 0.3478, 0.1639,  ..., 0.3403, 0.2083, 0.0411])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.816685914993286 seconds
-
-[20.2, 20.36, 20.48, 20.52, 20.32, 20.32, 20.28, 19.92, 20.0, 20.08]
-[20.12, 20.12, 20.6, 21.84, 23.64, 25.04, 25.96, 26.0, 25.96, 25.04, 24.68, 24.68, 24.68, 24.44]
-14.582478046417236
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.816685914993286, 'TIME_S_1KI': 2.35298801718366, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.7557634544372, 'W': 22.27027274930315}
-[20.2, 20.36, 20.48, 20.52, 20.32, 20.32, 20.28, 19.92, 20.0, 20.08, 20.56, 20.32, 20.36, 20.4, 20.28, 20.44, 20.6, 20.48, 20.52, 20.64]
-366.34000000000003
-18.317
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.816685914993286, 'TIME_S_1KI': 2.35298801718366, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.7557634544372, 'W': 22.27027274930315, 'J_1KI': 70.64515193701048, 'W_1KI': 4.84452311274813, 'W_D': 3.9532727493031494, 'J_D': 57.64851307821269, 'W_D_1KI': 0.8599679680885685, 'J_D_1KI': 0.18707156147238815}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.json
deleted file mode 100644
index 6cfe633..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4457, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.40576696395874, "TIME_S_1KI": 2.3347020336456676, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 304.0924919033051, "W": 22.47261995332125, "J_1KI": 68.22806639068995, "W_1KI": 5.042095569513406, "W_D": 3.977619953321252, "J_D": 53.82391398787505, "W_D_1KI": 0.8924433370700587, "J_D_1KI": 0.20023408953781888}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.output
deleted file mode 100644
index 2383595..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_065.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.23553967475891113}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.1485, 0.8406, 0.3211,  ..., 0.8758, 0.2934, 0.6273])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 0.23553967475891113 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4457 -m matrices/as-caida_pruned/as-caida_G_065.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.40576696395874}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.0947, 0.4563, 0.0701,  ..., 0.1127, 0.0889, 0.4578])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.40576696395874 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.0947, 0.4563, 0.0701,  ..., 0.1127, 0.0889, 0.4578])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.40576696395874 seconds
-
-[20.16, 20.24, 20.52, 20.72, 20.92, 20.84, 20.84, 21.04, 20.6, 20.44]
-[20.16, 20.12, 20.84, 22.08, 24.0, 25.48, 26.48, 26.04, 26.28, 25.32, 25.36, 25.56, 25.64]
-13.531688451766968
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4457, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.40576696395874, 'TIME_S_1KI': 2.3347020336456676, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 304.0924919033051, 'W': 22.47261995332125}
-[20.16, 20.24, 20.52, 20.72, 20.92, 20.84, 20.84, 21.04, 20.6, 20.44, 20.24, 20.24, 20.6, 20.56, 20.52, 20.56, 20.48, 20.28, 20.32, 20.4]
-369.9
-18.494999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4457, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.40576696395874, 'TIME_S_1KI': 2.3347020336456676, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 304.0924919033051, 'W': 22.47261995332125, 'J_1KI': 68.22806639068995, 'W_1KI': 5.042095569513406, 'W_D': 3.977619953321252, 'J_D': 53.82391398787505, 'W_D_1KI': 0.8924433370700587, 'J_D_1KI': 0.20023408953781888}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.json
deleted file mode 100644
index 2e451d6..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4241, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.165872573852539, "TIME_S_1KI": 2.397046115032431, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 308.4233190155029, "W": 22.773842448783192, "J_1KI": 72.724196891182, "W_1KI": 5.369922765570194, "W_D": 4.36484244878319, "J_D": 59.1125190253257, "W_D_1KI": 1.0292012376286699, "J_D_1KI": 0.2426789053592714}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.output
deleted file mode 100644
index 15086a5..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_075.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_075.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.24754810333251953}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.2352, 0.9461, 0.6829,  ..., 0.6578, 0.9838, 0.5986])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 0.24754810333251953 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4241 -m matrices/as-caida_pruned/as-caida_G_075.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.165872573852539}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.1680, 0.3295, 0.5975,  ..., 0.6100, 0.6760, 0.0664])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.165872573852539 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.1680, 0.3295, 0.5975,  ..., 0.6100, 0.6760, 0.0664])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.165872573852539 seconds
-
-[20.8, 20.76, 20.64, 20.64, 20.48, 20.68, 20.6, 20.56, 20.68, 20.52]
-[20.32, 20.32, 20.36, 23.6, 24.68, 27.04, 27.88, 28.48, 25.8, 24.88, 24.64, 24.72, 24.8]
-13.54287576675415
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4241, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.165872573852539, 'TIME_S_1KI': 2.397046115032431, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 308.4233190155029, 'W': 22.773842448783192}
-[20.8, 20.76, 20.64, 20.64, 20.48, 20.68, 20.6, 20.56, 20.68, 20.52, 20.24, 20.32, 20.2, 20.2, 20.2, 20.16, 20.44, 20.44, 20.24, 20.32]
-368.18000000000006
-18.409000000000002
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4241, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.165872573852539, 'TIME_S_1KI': 2.397046115032431, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 308.4233190155029, 'W': 22.773842448783192, 'J_1KI': 72.724196891182, 'W_1KI': 5.369922765570194, 'W_D': 4.36484244878319, 'J_D': 59.1125190253257, 'W_D_1KI': 1.0292012376286699, 'J_D_1KI': 0.2426789053592714}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.json
deleted file mode 100644
index 80844ab..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4258, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.52000379562378, "TIME_S_1KI": 2.470644386008403, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 340.48363485336307, "W": 23.399428779350817, "J_1KI": 79.96327732582505, "W_1KI": 5.495403658842371, "W_D": 4.919428779350817, "J_D": 71.58230262756351, "W_D_1KI": 1.1553379002702717, "J_D_1KI": 0.2713334664796317}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.output
deleted file mode 100644
index e5dc514..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_085.output
+++ /dev/null
@@ -1,65 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.246551513671875}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.0320, 0.2964, 0.1884,  ..., 0.9782, 0.1676, 0.8891])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 0.246551513671875 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4258 -m matrices/as-caida_pruned/as-caida_G_085.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.52000379562378}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.0888, 0.3161, 0.3875,  ..., 0.4649, 0.9695, 0.2632])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.52000379562378 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.0888, 0.3161, 0.3875,  ..., 0.4649, 0.9695, 0.2632])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.52000379562378 seconds
-
-[20.24, 20.4, 20.28, 20.32, 20.52, 20.48, 20.6, 20.72, 20.68, 20.76]
-[20.8, 20.68, 23.92, 24.68, 24.68, 26.56, 27.48, 28.76, 26.44, 25.24, 25.28, 24.8, 25.08, 25.2]
-14.55093789100647
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4258, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.52000379562378, 'TIME_S_1KI': 2.470644386008403, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 340.48363485336307, 'W': 23.399428779350817}
-[20.24, 20.4, 20.28, 20.32, 20.52, 20.48, 20.6, 20.72, 20.68, 20.76, 20.28, 20.36, 20.32, 20.44, 20.64, 20.64, 20.76, 20.76, 20.72, 20.64]
-369.6
-18.48
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4258, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.52000379562378, 'TIME_S_1KI': 2.470644386008403, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 340.48363485336307, 'W': 23.399428779350817, 'J_1KI': 79.96327732582505, 'W_1KI': 5.495403658842371, 'W_D': 4.919428779350817, 'J_D': 71.58230262756351, 'W_D_1KI': 1.1553379002702717, 'J_D_1KI': 0.2713334664796317}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.json
deleted file mode 100644
index 73a712c..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4299, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.539864301681519, "TIME_S_1KI": 2.451701396064554, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 330.04471103668214, "W": 22.651642406245635, "J_1KI": 76.77243801737198, "W_1KI": 5.269049175679375, "W_D": 4.1926424062456356, "J_D": 61.08870282483105, "W_D_1KI": 0.9752599223646513, "J_D_1KI": 0.2268573906407656}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.output
deleted file mode 100644
index 0dcb914..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_095.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.2615513801574707}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.3439, 0.2522, 0.8155,  ..., 0.9546, 0.8272, 0.9957])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 0.2615513801574707 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4014 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 9.802011013031006}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.1432, 0.6569, 0.7193,  ..., 0.9653, 0.9751, 0.3574])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 9.802011013031006 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4299 -m matrices/as-caida_pruned/as-caida_G_095.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.539864301681519}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.7221, 0.0635, 0.9129,  ..., 0.8366, 0.9671, 0.5596])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.539864301681519 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.7221, 0.0635, 0.9129,  ..., 0.8366, 0.9671, 0.5596])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.539864301681519 seconds
-
-[20.28, 20.32, 20.44, 20.64, 20.88, 20.92, 20.76, 20.6, 20.36, 20.2]
-[20.08, 20.04, 21.68, 23.8, 23.8, 26.2, 26.88, 27.4, 25.72, 24.76, 24.44, 24.48, 24.6, 24.48]
-14.570453882217407
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.539864301681519, 'TIME_S_1KI': 2.451701396064554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 330.04471103668214, 'W': 22.651642406245635}
-[20.28, 20.32, 20.44, 20.64, 20.88, 20.92, 20.76, 20.6, 20.36, 20.2, 20.56, 20.4, 20.28, 20.48, 20.4, 20.4, 20.4, 20.4, 20.64, 20.68]
-369.18
-18.459
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4299, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.539864301681519, 'TIME_S_1KI': 2.451701396064554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 330.04471103668214, 'W': 22.651642406245635, 'J_1KI': 76.77243801737198, 'W_1KI': 5.269049175679375, 'W_D': 4.1926424062456356, 'J_D': 61.08870282483105, 'W_D_1KI': 0.9752599223646513, 'J_D_1KI': 0.2268573906407656}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.json
deleted file mode 100644
index 2b479f7..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4049, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.376229763031006, "TIME_S_1KI": 2.562664796994568, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 310.42938493728633, "W": 22.891819526408486, "J_1KI": 76.66816125890006, "W_1KI": 5.653697092222397, "W_D": 4.550819526408485, "J_D": 61.7123555824756, "W_D_1KI": 1.1239366575471685, "J_D_1KI": 0.2775837632865321}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.output
deleted file mode 100644
index 5514581..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_105.output
+++ /dev/null
@@ -1,68 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.2592613697052002}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.4913, 0.3479, 0.5546,  ..., 0.3694, 0.0850, 0.9788])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 0.2592613697052002 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4049 -m matrices/as-caida_pruned/as-caida_G_105.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.376229763031006}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.3564, 0.9920, 0.3563,  ..., 0.1738, 0.9526, 0.3099])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.376229763031006 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.3564, 0.9920, 0.3563,  ..., 0.1738, 0.9526, 0.3099])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.376229763031006 seconds
-
-[20.6, 20.4, 20.48, 20.56, 20.64, 20.36, 20.32, 20.32, 20.32, 20.24]
-[20.32, 20.52, 20.76, 22.96, 23.72, 26.2, 27.36, 27.96, 27.24, 25.8, 25.52, 25.16, 25.52]
-13.560712575912476
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4049, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.376229763031006, 'TIME_S_1KI': 2.562664796994568, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.42938493728633, 'W': 22.891819526408486}
-[20.6, 20.4, 20.48, 20.56, 20.64, 20.36, 20.32, 20.32, 20.32, 20.24, 20.56, 20.32, 20.24, 20.24, 20.12, 20.28, 20.44, 20.44, 20.48, 20.32]
-366.82
-18.341
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4049, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.376229763031006, 'TIME_S_1KI': 2.562664796994568, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.42938493728633, 'W': 22.891819526408486, 'J_1KI': 76.66816125890006, 'W_1KI': 5.653697092222397, 'W_D': 4.550819526408485, 'J_D': 61.7123555824756, 'W_D_1KI': 1.1239366575471685, 'J_D_1KI': 0.2775837632865321}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.json
deleted file mode 100644
index 4a8e502..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4098, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.480108499526978, "TIME_S_1KI": 2.5573715225785696, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 337.20645687103274, "W": 23.049017782427377, "J_1KI": 82.28561661079374, "W_1KI": 5.624455290977886, "W_D": 4.55101778242738, "J_D": 66.58125721693045, "W_D_1KI": 1.1105460669661738, "J_D_1KI": 0.2709970880834977}
diff --git a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.output
deleted file mode 100644
index 261c286..0000000
--- a/pytorch/output_as-caida_1core/altra_1_csr_10_10_10_as-caida_G_115.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.2717466354370117}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.2910, 0.1313, 0.5488,  ..., 0.2360, 0.4747, 0.6889])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 0.2717466354370117 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3863 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 9.896336078643799}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.7215, 0.6948, 0.2553,  ..., 0.1689, 0.6463, 0.2591])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 9.896336078643799 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4098 -m matrices/as-caida_pruned/as-caida_G_115.mtx -c 1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.480108499526978}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.7345, 0.2555, 0.9166,  ..., 0.8790, 0.4016, 0.3913])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.480108499526978 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.7345, 0.2555, 0.9166,  ..., 0.8790, 0.4016, 0.3913])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.480108499526978 seconds
-
-[20.52, 20.56, 20.72, 20.72, 20.8, 20.08, 20.16, 20.24, 20.2, 20.52]
-[20.84, 20.84, 21.28, 22.72, 24.36, 25.48, 26.68, 26.36, 26.36, 26.44, 26.0, 25.88, 25.64, 25.44]
-14.629970788955688
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4098, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.480108499526978, 'TIME_S_1KI': 2.5573715225785696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.20645687103274, 'W': 23.049017782427377}
-[20.52, 20.56, 20.72, 20.72, 20.8, 20.08, 20.16, 20.24, 20.2, 20.52, 20.68, 20.64, 20.44, 20.4, 20.44, 20.36, 20.52, 20.84, 21.36, 21.24]
-369.96
-18.497999999999998
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4098, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.480108499526978, 'TIME_S_1KI': 2.5573715225785696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.20645687103274, 'W': 23.049017782427377, 'J_1KI': 82.28561661079374, 'W_1KI': 5.624455290977886, 'W_D': 4.55101778242738, 'J_D': 66.58125721693045, 'W_D_1KI': 1.1105460669661738, 'J_D_1KI': 0.2709970880834977}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..e69de29
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..3c8a67d
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.output
@@ -0,0 +1,55 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.2207937240600586}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.7193, 0.7070, 0.1341,  ..., 0.0458, 0.8165, 0.6087])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.2207937240600586 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4755', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.089951038360596}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.1581, 0.9236, 0.8715,  ..., 0.0128, 0.7061, 0.4474])
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+Matrix: as-caida_G_010
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+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
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+Density: 7.616375021864427e-05
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+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.1581, 0.9236, 0.8715,  ..., 0.0128, 0.7061, 0.4474])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
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+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
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+Density: 7.616375021864427e-05
+Time: 10.089951038360596 seconds
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+[44.57, 39.27, 38.78, 39.37, 39.08, 39.0, 39.29, 40.29, 38.94, 38.6]
+[64.64]
+12.744337558746338
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 4755, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.089951038360596, 'TIME_S_1KI': 2.1219665695816183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 823.7939797973632, 'W': 64.64}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..dfbc954
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 4416, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.121088027954102, "TIME_S_1KI": 2.291913049808447, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 820.2321936893463, "W": 64.26, "J_1KI": 185.74098588979763, "W_1KI": 14.551630434782611, "W_D": 29.148750000000007, "J_D": 372.0626074665786, "W_D_1KI": 6.600713315217393, "J_D_1KI": 1.494726747105388}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..6b94cd3
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_020.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.23772501945495605}
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.0235, 0.3572, 0.0680,  ..., 0.1697, 0.5664, 0.9391])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
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+Density: 8.221062021893506e-05
+Time: 0.23772501945495605 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4416', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.121088027954102}
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.0401, 0.4359, 0.4509,  ..., 0.9036, 0.4719, 0.1003])
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+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.0401, 0.4359, 0.4509,  ..., 0.9036, 0.4719, 0.1003])
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..5236f95
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_030.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 4131, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.162817478179932, "TIME_S_1KI": 2.4601349499346243, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 827.8255462884903, "W": 64.19, "J_1KI": 200.39349946465512, "W_1KI": 15.538610505930768, "W_D": 28.75374999999999, "J_D": 370.82238357365117, "W_D_1KI": 6.960481723553617, "J_D_1KI": 1.6849386888292464}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..4fb4ec6
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_030.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.25415992736816406}
+
+tensor(indices=tensor([[ 1809, 21783,   106,  ...,  7018,   160,   882],
+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=86850, layout=torch.sparse_coo)
+tensor([0.2884, 0.9387, 0.6290,  ..., 0.6114, 0.3783, 0.6499])
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+Matrix: as-caida_G_030
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+Rows: 31379
+Size: 984641641
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+Time: 0.25415992736816406 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4131', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.162817478179932}
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+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
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+tensor([0.4470, 0.6434, 0.7978,  ..., 0.5536, 0.7218, 0.3322])
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+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
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new file mode 100644
index 0000000..c370098
--- /dev/null
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..06c6aa2
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..a98cbc5
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..6eead2d
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..8470d53
--- /dev/null
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..c48423f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_060.output
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..a8d53ed
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_070.json
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..a440572
--- /dev/null
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new file mode 100644
index 0000000..5e73d37
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..5d8c8b3
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+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_080.output
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..eb07b88
--- /dev/null
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..92e1dbd
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+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_090.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx', '-c', '1']
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..f712666
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_100.json
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..c8fe2ea
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index 0000000..08f93ab
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..815c8c8
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+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_110.output
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..742da2a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_120.json
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..f5893c7
--- /dev/null
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_120.output
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diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.json
deleted file mode 100644
index 2cc2723..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 43083, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.477100133895874, "TIME_S_1KI": 0.2431840896385088, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 851.2608031511307, "W": 65.15, "J_1KI": 19.758624124390845, "W_1KI": 1.5121973864401275, "W_D": 29.482000000000006, "J_D": 385.2167459478379, "W_D_1KI": 0.6843070352575262, "J_D_1KI": 0.01588345833060665}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.output
deleted file mode 100644
index ba290cf..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_005.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.03452706336975098}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.7546, 0.5295, 0.0524,  ..., 0.5498, 0.1309, 0.9141])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 0.03452706336975098 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30410', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 7.411336183547974}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.6311, 0.0149, 0.4270,  ..., 0.8172, 0.7151, 0.0022])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 7.411336183547974 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '43083', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.477100133895874}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.5533, 0.0502, 0.2758,  ..., 0.2070, 0.2491, 0.2171])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.477100133895874 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.5533, 0.0502, 0.2758,  ..., 0.2070, 0.2491, 0.2171])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.477100133895874 seconds
-
-[40.15, 38.92, 39.51, 39.69, 38.96, 39.49, 39.05, 39.12, 39.72, 39.3]
-[65.15]
-13.06616735458374
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 43083, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.477100133895874, 'TIME_S_1KI': 0.2431840896385088, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 851.2608031511307, 'W': 65.15}
-[40.15, 38.92, 39.51, 39.69, 38.96, 39.49, 39.05, 39.12, 39.72, 39.3, 39.55, 39.14, 39.79, 38.87, 39.85, 39.3, 43.36, 40.57, 39.04, 38.96]
-713.36
-35.668
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 43083, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.477100133895874, 'TIME_S_1KI': 0.2431840896385088, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 851.2608031511307, 'W': 65.15, 'J_1KI': 19.758624124390845, 'W_1KI': 1.5121973864401275, 'W_D': 29.482000000000006, 'J_D': 385.2167459478379, 'W_D_1KI': 0.6843070352575262, 'J_D_1KI': 0.01588345833060665}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.json
deleted file mode 100644
index 61da900..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 38858, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.485401391983032, "TIME_S_1KI": 0.269838936434789, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 857.5578077483177, "W": 65.17, "J_1KI": 22.069015588767247, "W_1KI": 1.6771321220855422, "W_D": 29.260250000000006, "J_D": 385.0292441946865, "W_D_1KI": 0.7530045293118536, "J_D_1KI": 0.019378365569814544}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.output
deleted file mode 100644
index 7a0d3ec..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_015.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.03696393966674805}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.7337, 0.4673, 0.2553,  ..., 0.7979, 0.4172, 0.5121])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 0.03696393966674805 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '28406', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 7.675543546676636}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.1361, 0.1751, 0.5794,  ..., 0.9159, 0.1555, 0.6066])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 7.675543546676636 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38858', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.485401391983032}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.0846, 0.1902, 0.2817,  ..., 0.3581, 0.4321, 0.1894])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.485401391983032 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.0846, 0.1902, 0.2817,  ..., 0.3581, 0.4321, 0.1894])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.485401391983032 seconds
-
-[40.2, 38.88, 39.37, 39.26, 39.72, 44.47, 39.38, 38.79, 39.26, 39.05]
-[65.17]
-13.15878176689148
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 38858, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.485401391983032, 'TIME_S_1KI': 0.269838936434789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.5578077483177, 'W': 65.17}
-[40.2, 38.88, 39.37, 39.26, 39.72, 44.47, 39.38, 38.79, 39.26, 39.05, 39.64, 39.12, 44.98, 38.93, 39.41, 39.85, 39.14, 39.66, 39.04, 38.98]
-718.1949999999999
-35.909749999999995
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 38858, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.485401391983032, 'TIME_S_1KI': 0.269838936434789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.5578077483177, 'W': 65.17, 'J_1KI': 22.069015588767247, 'W_1KI': 1.6771321220855422, 'W_D': 29.260250000000006, 'J_D': 385.0292441946865, 'W_D_1KI': 0.7530045293118536, 'J_D_1KI': 0.019378365569814544}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.json
deleted file mode 100644
index 4eb5957..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 36057, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.52190113067627, "TIME_S_1KI": 0.29181299416690987, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 870.8484698581696, "W": 65.88, "J_1KI": 24.151994615696523, "W_1KI": 1.8271070804559446, "W_D": 30.036999999999992, "J_D": 397.05032618594157, "W_D_1KI": 0.8330421277421858, "J_D_1KI": 0.02310347859617233}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.output
deleted file mode 100644
index 4921121..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_025.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.03890419006347656}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.5918, 0.7842, 0.2922,  ..., 0.7811, 0.4187, 0.7857])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 0.03890419006347656 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26989', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 7.8591344356536865}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.4944, 0.9993, 0.3541,  ..., 0.8129, 0.1587, 0.8643])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 7.8591344356536865 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36057', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.52190113067627}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.3640, 0.1663, 0.5631,  ..., 0.5487, 0.9918, 0.2869])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.52190113067627 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.3640, 0.1663, 0.5631,  ..., 0.5487, 0.9918, 0.2869])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.52190113067627 seconds
-
-[40.01, 39.03, 39.43, 39.2, 39.48, 39.28, 44.38, 38.91, 39.07, 39.26]
-[65.88]
-13.218707799911499
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 36057, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.52190113067627, 'TIME_S_1KI': 0.29181299416690987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 870.8484698581696, 'W': 65.88}
-[40.01, 39.03, 39.43, 39.2, 39.48, 39.28, 44.38, 38.91, 39.07, 39.26, 40.2, 38.93, 39.03, 44.7, 39.08, 39.28, 39.65, 38.85, 39.34, 38.97]
-716.8600000000001
-35.843
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 36057, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.52190113067627, 'TIME_S_1KI': 0.29181299416690987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 870.8484698581696, 'W': 65.88, 'J_1KI': 24.151994615696523, 'W_1KI': 1.8271070804559446, 'W_D': 30.036999999999992, 'J_D': 397.05032618594157, 'W_D_1KI': 0.8330421277421858, 'J_D_1KI': 0.02310347859617233}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.json
deleted file mode 100644
index c0ad406..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35173, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.447309255599976, "TIME_S_1KI": 0.29702639114093127, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 861.1301214361191, "W": 65.59, "J_1KI": 24.48270325067862, "W_1KI": 1.8647826457794332, "W_D": 29.845750000000002, "J_D": 391.84440191876894, "W_D_1KI": 0.8485414948966538, "J_D_1KI": 0.024124797284754036}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.output
deleted file mode 100644
index bf75d87..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_035.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.03972125053405762}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.7495, 0.7125, 0.1641,  ..., 0.3237, 0.6914, 0.4632])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 0.03972125053405762 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '26434', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 7.891004800796509}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.4191, 0.3155, 0.4177,  ..., 0.6606, 0.8993, 0.7459])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 7.891004800796509 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35173', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.447309255599976}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.1483, 0.5699, 0.1162,  ..., 0.9827, 0.6104, 0.1631])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.447309255599976 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.1483, 0.5699, 0.1162,  ..., 0.9827, 0.6104, 0.1631])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.447309255599976 seconds
-
-[39.68, 44.32, 39.73, 38.97, 39.63, 39.5, 39.25, 39.37, 39.5, 39.02]
-[65.59]
-13.128984928131104
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35173, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.447309255599976, 'TIME_S_1KI': 0.29702639114093127, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 861.1301214361191, 'W': 65.59}
-[39.68, 44.32, 39.73, 38.97, 39.63, 39.5, 39.25, 39.37, 39.5, 39.02, 39.68, 38.91, 39.15, 39.15, 41.28, 39.51, 39.35, 39.72, 38.94, 38.83]
-714.885
-35.74425
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35173, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.447309255599976, 'TIME_S_1KI': 0.29702639114093127, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 861.1301214361191, 'W': 65.59, 'J_1KI': 24.48270325067862, 'W_1KI': 1.8647826457794332, 'W_D': 29.845750000000002, 'J_D': 391.84440191876894, 'W_D_1KI': 0.8485414948966538, 'J_D_1KI': 0.024124797284754036}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.json
deleted file mode 100644
index cb38459..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 34316, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.484640121459961, "TIME_S_1KI": 0.3055321168393741, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.8062327885629, "W": 65.67, "J_1KI": 25.142972164254658, "W_1KI": 1.9136845786222172, "W_D": 30.424499999999995, "J_D": 399.73272772157185, "W_D_1KI": 0.8865980883552861, "J_D_1KI": 0.025836288855207078}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.output
deleted file mode 100644
index 95736a8..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_045.output
+++ /dev/null
@@ -1,105 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.04044318199157715}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.9025, 0.6353, 0.0498,  ..., 0.1623, 0.8280, 0.3887])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 0.04044318199157715 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '25962', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 8.462014198303223}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.0457, 0.1982, 0.5371,  ..., 0.8451, 0.9072, 0.4847])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 8.462014198303223 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '32214', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 9.856708288192749}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.9221, 0.7340, 0.0148,  ..., 0.4303, 0.3457, 0.2080])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 9.856708288192749 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '34316', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.484640121459961}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.9270, 0.0174, 0.7942,  ..., 0.2212, 0.8209, 0.0128])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.484640121459961 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.9270, 0.0174, 0.7942,  ..., 0.2212, 0.8209, 0.0128])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.484640121459961 seconds
-
-[40.45, 39.27, 39.34, 38.8, 39.26, 39.86, 39.18, 39.18, 38.87, 39.35]
-[65.67]
-13.138514280319214
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 34316, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.484640121459961, 'TIME_S_1KI': 0.3055321168393741, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.8062327885629, 'W': 65.67}
-[40.45, 39.27, 39.34, 38.8, 39.26, 39.86, 39.18, 39.18, 38.87, 39.35, 39.9, 39.2, 38.96, 38.82, 39.04, 38.79, 39.3, 38.81, 38.94, 38.88]
-704.9100000000001
-35.24550000000001
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 34316, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.484640121459961, 'TIME_S_1KI': 0.3055321168393741, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.8062327885629, 'W': 65.67, 'J_1KI': 25.142972164254658, 'W_1KI': 1.9136845786222172, 'W_D': 30.424499999999995, 'J_D': 399.73272772157185, 'W_D_1KI': 0.8865980883552861, 'J_D_1KI': 0.025836288855207078}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.json
deleted file mode 100644
index c15af60..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 33510, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.483581304550171, "TIME_S_1KI": 0.31284933764697614, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 858.1138673710824, "W": 65.23, "J_1KI": 25.60769523637966, "W_1KI": 1.9465831095195465, "W_D": 29.08325, "J_D": 382.59604680699107, "W_D_1KI": 0.8678976424947777, "J_D_1KI": 0.02589966107116615}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.output
deleted file mode 100644
index df4afb4..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_055.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.04136204719543457}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.2193, 0.1796, 0.4399,  ..., 0.4451, 0.3006, 0.2875])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 0.04136204719543457 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '25385', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 7.95404314994812}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.9926, 0.3031, 0.5874,  ..., 0.7443, 0.4408, 0.2995])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 7.95404314994812 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '33510', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.483581304550171}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.7506, 0.9011, 0.4947,  ..., 0.5418, 0.4388, 0.4192])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.483581304550171 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.7506, 0.9011, 0.4947,  ..., 0.5418, 0.4388, 0.4192])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.483581304550171 seconds
-
-[40.21, 39.23, 39.1, 39.38, 39.37, 39.09, 39.81, 39.02, 40.1, 38.85]
-[65.23]
-13.155202627182007
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 33510, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.483581304550171, 'TIME_S_1KI': 0.31284933764697614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.1138673710824, 'W': 65.23}
-[40.21, 39.23, 39.1, 39.38, 39.37, 39.09, 39.81, 39.02, 40.1, 38.85, 39.87, 39.56, 40.29, 39.1, 50.99, 41.72, 39.11, 38.85, 39.27, 38.96]
-722.9350000000001
-36.146750000000004
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 33510, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.483581304550171, 'TIME_S_1KI': 0.31284933764697614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.1138673710824, 'W': 65.23, 'J_1KI': 25.60769523637966, 'W_1KI': 1.9465831095195465, 'W_D': 29.08325, 'J_D': 382.59604680699107, 'W_D_1KI': 0.8678976424947777, 'J_D_1KI': 0.02589966107116615}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.json
deleted file mode 100644
index 2ed5a03..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 32796, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.488871335983276, "TIME_S_1KI": 0.3198216653245297, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 858.9457808732986, "W": 64.95, "J_1KI": 26.190565339471235, "W_1KI": 1.9804244420051227, "W_D": 29.534750000000003, "J_D": 390.588897638917, "W_D_1KI": 0.900559519453592, "J_D_1KI": 0.027459431621343823}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.output
deleted file mode 100644
index 4e4ed45..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_065.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.042150020599365234}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.1926, 0.4247, 0.2336,  ..., 0.9542, 0.9130, 0.4279])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 0.042150020599365234 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24911', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 7.975389719009399}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.7725, 0.0253, 0.1316,  ..., 0.7396, 0.9784, 0.7056])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 7.975389719009399 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '32796', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.488871335983276}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.1802, 0.5827, 0.7701,  ..., 0.8741, 0.2809, 0.8210])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.488871335983276 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.1802, 0.5827, 0.7701,  ..., 0.8741, 0.2809, 0.8210])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.488871335983276 seconds
-
-[39.86, 39.29, 39.08, 38.93, 39.59, 39.04, 39.49, 40.53, 39.33, 39.32]
-[64.95]
-13.22472333908081
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 32796, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.488871335983276, 'TIME_S_1KI': 0.3198216653245297, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.9457808732986, 'W': 64.95}
-[39.86, 39.29, 39.08, 38.93, 39.59, 39.04, 39.49, 40.53, 39.33, 39.32, 40.13, 40.37, 38.95, 38.92, 39.06, 38.93, 38.92, 38.89, 39.88, 38.9]
-708.305
-35.41525
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 32796, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.488871335983276, 'TIME_S_1KI': 0.3198216653245297, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.9457808732986, 'W': 64.95, 'J_1KI': 26.190565339471235, 'W_1KI': 1.9804244420051227, 'W_D': 29.534750000000003, 'J_D': 390.588897638917, 'W_D_1KI': 0.900559519453592, 'J_D_1KI': 0.027459431621343823}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.json
deleted file mode 100644
index bc43cfa..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 32317, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.516169548034668, "TIME_S_1KI": 0.32540673787896984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 867.264186372757, "W": 65.48, "J_1KI": 26.836160113028964, "W_1KI": 2.026178172478881, "W_D": 19.231750000000005, "J_D": 254.71912059062726, "W_D_1KI": 0.5950970077668103, "J_D_1KI": 0.018414364197382502}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.output
deleted file mode 100644
index 419c65f..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_075.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.04397153854370117}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.8835, 0.2905, 0.1746,  ..., 0.2508, 0.5375, 0.9105])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 0.04397153854370117 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23879', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 7.75836181640625}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.2327, 0.1142, 0.1910,  ..., 0.9478, 0.2863, 0.7780])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 7.75836181640625 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '32317', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.516169548034668}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.8802, 0.8846, 0.9410,  ..., 0.6286, 0.1450, 0.7013])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.516169548034668 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.8802, 0.8846, 0.9410,  ..., 0.6286, 0.1450, 0.7013])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.516169548034668 seconds
-
-[39.64, 38.82, 40.18, 38.79, 38.93, 39.23, 39.2, 38.83, 40.26, 39.15]
-[65.48]
-13.244718790054321
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 32317, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.516169548034668, 'TIME_S_1KI': 0.32540673787896984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 867.264186372757, 'W': 65.48}
-[39.64, 38.82, 40.18, 38.79, 38.93, 39.23, 39.2, 38.83, 40.26, 39.15, 68.29, 72.7, 70.94, 63.35, 66.33, 64.79, 60.51, 60.49, 58.48, 39.19]
-924.965
-46.24825
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 32317, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.516169548034668, 'TIME_S_1KI': 0.32540673787896984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 867.264186372757, 'W': 65.48, 'J_1KI': 26.836160113028964, 'W_1KI': 2.026178172478881, 'W_D': 19.231750000000005, 'J_D': 254.71912059062726, 'W_D_1KI': 0.5950970077668103, 'J_D_1KI': 0.018414364197382502}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.json
deleted file mode 100644
index b427c33..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 31855, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.502902507781982, "TIME_S_1KI": 0.3297097004483435, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.4069772052765, "W": 65.08, "J_1KI": 26.85314635709548, "W_1KI": 2.043007377177837, "W_D": 29.676000000000002, "J_D": 390.0592725191117, "W_D_1KI": 0.9315962957149585, "J_D_1KI": 0.029244900195101505}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.output
deleted file mode 100644
index ea5504f..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_085.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.04311323165893555}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.1764, 0.0789, 0.0500,  ..., 0.7871, 0.4459, 0.1044])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 0.04311323165893555 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24354', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 8.02746057510376}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.4941, 0.1029, 0.9153,  ..., 0.6749, 0.6031, 0.7456])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 8.02746057510376 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '31855', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.502902507781982}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.8603, 0.6409, 0.8738,  ..., 0.6986, 0.4715, 0.6839])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.502902507781982 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.8603, 0.6409, 0.8738,  ..., 0.6986, 0.4715, 0.6839])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.502902507781982 seconds
-
-[39.81, 38.99, 39.18, 39.42, 39.32, 38.97, 39.04, 38.84, 38.98, 38.98]
-[65.08]
-13.143930196762085
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 31855, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.502902507781982, 'TIME_S_1KI': 0.3297097004483435, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.4069772052765, 'W': 65.08}
-[39.81, 38.99, 39.18, 39.42, 39.32, 38.97, 39.04, 38.84, 38.98, 38.98, 40.72, 41.01, 39.07, 38.92, 39.38, 39.46, 39.36, 39.9, 39.05, 38.87]
-708.0799999999999
-35.403999999999996
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 31855, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.502902507781982, 'TIME_S_1KI': 0.3297097004483435, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.4069772052765, 'W': 65.08, 'J_1KI': 26.85314635709548, 'W_1KI': 2.043007377177837, 'W_D': 29.676000000000002, 'J_D': 390.0592725191117, 'W_D_1KI': 0.9315962957149585, 'J_D_1KI': 0.029244900195101505}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.json
deleted file mode 100644
index c7609a2..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30992, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.461604595184326, "TIME_S_1KI": 0.3375582277744039, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.3391364908218, "W": 65.31, "J_1KI": 27.598707295134933, "W_1KI": 2.107318017552917, "W_D": 30.06725, "J_D": 393.77883404767516, "W_D_1KI": 0.9701616546205473, "J_D_1KI": 0.03130361559823655}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.output
deleted file mode 100644
index 4dc3c3e..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_095.output
+++ /dev/null
@@ -1,90 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.04361534118652344}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([1.2265e-01, 9.2792e-01, 5.3994e-01,  ..., 3.2830e-04, 7.8885e-01,
-        4.0460e-01])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 0.04361534118652344 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '24074', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 8.15611457824707}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.1199, 0.5589, 0.0427,  ..., 0.8467, 0.0895, 0.6403])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 8.15611457824707 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30992', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.461604595184326}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.8237, 0.6815, 0.7303,  ..., 0.8961, 0.9057, 0.1353])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.461604595184326 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.8237, 0.6815, 0.7303,  ..., 0.8961, 0.9057, 0.1353])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.461604595184326 seconds
-
-[39.49, 38.75, 39.8, 39.15, 38.92, 38.75, 39.12, 39.0, 39.17, 39.28]
-[65.31]
-13.09660291671753
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30992, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.461604595184326, 'TIME_S_1KI': 0.3375582277744039, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3391364908218, 'W': 65.31}
-[39.49, 38.75, 39.8, 39.15, 38.92, 38.75, 39.12, 39.0, 39.17, 39.28, 39.59, 39.49, 39.32, 39.13, 39.22, 39.92, 38.97, 38.72, 38.76, 38.97]
-704.855
-35.24275
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30992, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.461604595184326, 'TIME_S_1KI': 0.3375582277744039, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3391364908218, 'W': 65.31, 'J_1KI': 27.598707295134933, 'W_1KI': 2.107318017552917, 'W_D': 30.06725, 'J_D': 393.77883404767516, 'W_D_1KI': 0.9701616546205473, 'J_D_1KI': 0.03130361559823655}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.json
deleted file mode 100644
index 45b4823..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30536, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.540117979049683, "TIME_S_1KI": 0.3451702246217475, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 861.2643821239473, "W": 65.4, "J_1KI": 28.204885450744932, "W_1KI": 2.141734346345298, "W_D": 29.81275, "J_D": 392.6094756600261, "W_D_1KI": 0.9763148414985592, "J_D_1KI": 0.031972584539512676}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.output
deleted file mode 100644
index 6c9a46f..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_105.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.044251441955566406}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.6090, 0.7079, 0.0401,  ..., 0.3126, 0.5822, 0.6098])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 0.044251441955566406 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23728', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 8.158772230148315}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.2992, 0.2859, 0.3611,  ..., 0.2485, 0.8593, 0.7485])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 8.158772230148315 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30536', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.540117979049683}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.7959, 0.6851, 0.8740,  ..., 0.0031, 0.3114, 0.8796])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.540117979049683 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.7959, 0.6851, 0.8740,  ..., 0.0031, 0.3114, 0.8796])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.540117979049683 seconds
-
-[40.59, 39.08, 39.45, 39.57, 39.25, 39.0, 39.96, 39.75, 39.54, 39.19]
-[65.4]
-13.169180154800415
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30536, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.540117979049683, 'TIME_S_1KI': 0.3451702246217475, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 861.2643821239473, 'W': 65.4}
-[40.59, 39.08, 39.45, 39.57, 39.25, 39.0, 39.96, 39.75, 39.54, 39.19, 40.78, 41.19, 39.05, 39.13, 39.19, 39.09, 39.45, 39.45, 39.13, 40.37]
-711.7450000000001
-35.587250000000004
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30536, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.540117979049683, 'TIME_S_1KI': 0.3451702246217475, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 861.2643821239473, 'W': 65.4, 'J_1KI': 28.204885450744932, 'W_1KI': 2.141734346345298, 'W_D': 29.81275, 'J_D': 392.6094756600261, 'W_D_1KI': 0.9763148414985592, 'J_D_1KI': 0.031972584539512676}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.json
deleted file mode 100644
index b5a6d38..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 30237, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.500168800354004, "TIME_S_1KI": 0.34726225486503304, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 857.1604746675492, "W": 65.26, "J_1KI": 28.34806610006116, "W_1KI": 2.1582828984356914, "W_D": 29.86950000000001, "J_D": 392.3223229862453, "W_D_1KI": 0.9878460164698883, "J_D_1KI": 0.032670106706018734}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.output
deleted file mode 100644
index 5bbb6c5..0000000
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_csr_10_10_10_as-caida_G_115.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.04454636573791504}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.7341, 0.3777, 0.6942,  ..., 0.5169, 0.0997, 0.0147])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 0.04454636573791504 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '23570', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 8.184704303741455}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.5580, 0.0255, 0.1246,  ..., 0.1598, 0.8729, 0.3337])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 8.184704303741455 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '30237', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.500168800354004}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.2420, 0.6857, 0.8364,  ..., 0.8458, 0.0047, 0.6450])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.500168800354004 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.2420, 0.6857, 0.8364,  ..., 0.8458, 0.0047, 0.6450])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.500168800354004 seconds
-
-[40.88, 39.7, 39.32, 38.97, 39.02, 38.99, 39.0, 38.96, 38.99, 39.43]
-[65.26]
-13.134546041488647
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30237, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.500168800354004, 'TIME_S_1KI': 0.34726225486503304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.1604746675492, 'W': 65.26}
-[40.88, 39.7, 39.32, 38.97, 39.02, 38.99, 39.0, 38.96, 38.99, 39.43, 42.02, 39.37, 39.48, 38.85, 39.01, 39.6, 39.28, 38.9, 39.76, 38.89]
-707.81
-35.390499999999996
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 30237, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.500168800354004, 'TIME_S_1KI': 0.34726225486503304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.1604746675492, 'W': 65.26, 'J_1KI': 28.34806610006116, 'W_1KI': 2.1582828984356914, 'W_D': 29.86950000000001, 'J_D': 392.3223229862453, 'W_D_1KI': 0.9878460164698883, 'J_D_1KI': 0.032670106706018734}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..4b9f352
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3790, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.07475233078003, "TIME_S_1KI": 2.65824599756729, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 731.8950152492523, "W": 52.68, "J_1KI": 193.11214122671566, "W_1KI": 13.899736147757256, "W_D": 35.516000000000005, "J_D": 493.43172668170934, "W_D_1KI": 9.370976253298155, "J_D_1KI": 2.472553101134078}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..0ebaca7
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_010.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.2769815921783447}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.8960, 0.3749, 0.9657,  ..., 0.2230, 0.7966, 0.0159])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.2769815921783447 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '3790', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.07475233078003}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.6474, 0.0251, 0.6591,  ..., 0.8505, 0.7101, 0.8498])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.07475233078003 seconds
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.6474, 0.0251, 0.6591,  ..., 0.8505, 0.7101, 0.8498])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.07475233078003 seconds
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+[52.68]
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+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3790, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.07475233078003, 'TIME_S_1KI': 2.65824599756729, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.8950152492523, 'W': 52.68, 'J_1KI': 193.11214122671566, 'W_1KI': 13.899736147757256, 'W_D': 35.516000000000005, 'J_D': 493.43172668170934, 'W_D_1KI': 9.370976253298155, 'J_D_1KI': 2.472553101134078}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..08b56c8
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_020.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3518, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.111847162246704, "TIME_S_1KI": 2.87431698756302, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 736.1022858762741, "W": 53.06, "J_1KI": 209.2388532905839, "W_1KI": 15.082433200682207, "W_D": 36.133, "J_D": 501.2737258870602, "W_D_1KI": 10.270892552586696, "J_D_1KI": 2.919526024043973}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..0b9fa77
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_020.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.2984433174133301}
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.4460, 0.0480, 0.9008,  ..., 0.7954, 0.4313, 0.1974])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 0.2984433174133301 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '3518', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 10.111847162246704}
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.9483, 0.3621, 0.6533,  ..., 0.1002, 0.5049, 0.6191])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.111847162246704 seconds
+
+tensor(indices=tensor([[ 1040,  5699,   106,  ..., 31378, 17998, 31377],
+                       [    0,     0,     2,  ..., 31377, 31378, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 1.]),
+       size=(31379, 31379), nnz=80948, layout=torch.sparse_coo)
+tensor([0.9483, 0.3621, 0.6533,  ..., 0.1002, 0.5049, 0.6191])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_020
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 80948
+Density: 8.221062021893506e-05
+Time: 10.111847162246704 seconds
+
+[18.96, 18.58, 18.72, 18.63, 18.68, 18.61, 18.99, 18.58, 19.62, 18.63]
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..5d82afe
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_030.json
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+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3294, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 10.230846405029297, "TIME_S_1KI": 3.1059035837976006, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 738.3630283427239, "W": 52.93, "J_1KI": 224.15392481564172, "W_1KI": 16.068609593199756, "W_D": 35.9885, "J_D": 502.0324550446272, "W_D_1KI": 10.925470552519732, "J_D_1KI": 3.316779159842056}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..fb15217
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_030.output
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+                       [    0,     0,     2,  ..., 31357, 31360, 31373]]),
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..f89b80e
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_040.json
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..e304c1f
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_040.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx', '-c', '1']
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+                       [    2,     2,     2,  ..., 31371, 31373, 31378]]),
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..bd1d920
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_050.json
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+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3276, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 10.485815286636353, "TIME_S_1KI": 3.2007983170440637, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 757.6888778162003, "W": 53.26, "J_1KI": 231.28476123815636, "W_1KI": 16.257631257631257, "W_D": 36.31425, "J_D": 516.614782787025, "W_D_1KI": 11.084935897435898, "J_D_1KI": 3.3836800663723743}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..5467ff2
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_050.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.3464834690093994}
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+tensor(indices=tensor([[ 5326,   106,   329,  ...,   882,  2232, 16085],
+                       [    0,     2,     2,  ..., 31373, 31378, 31378]]),
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..587f2d5
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_060.json
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+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3035, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 10.118342638015747, "TIME_S_1KI": 3.3338855479458807, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 732.9580327033997, "W": 52.7, "J_1KI": 241.50182296652378, "W_1KI": 17.364085667215814, "W_D": 35.8665, "J_D": 498.83565996122366, "W_D_1KI": 11.817627677100495, "J_D_1KI": 3.893781771697033}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..a128e94
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_060.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.3459279537200928}
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..59b6581
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_070.json
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+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3609, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.091247320175171, "TIME_S_1KI": 2.7961339208022085, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.9008146309852, "W": 53.43, "J_1KI": 203.35295500997097, "W_1KI": 14.804655029093931, "W_D": 15.148000000000003, "J_D": 208.0690537157059, "W_D_1KI": 4.197284566361874, "J_D_1KI": 1.1630048673765234}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..ccb40ae
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_070.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.29088449478149414}
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+                       [    2,     2,     2,  ..., 31240, 31283, 31284]]),
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..15ab2ac
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_080.json
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+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2930, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.166032314300537, "TIME_S_1KI": 3.469635602150354, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 736.2924279117585, "W": 52.84000000000001, "J_1KI": 251.29434399718718, "W_1KI": 18.03412969283277, "W_D": 36.02175000000001, "J_D": 501.94060872691887, "W_D_1KI": 12.294112627986351, "J_D_1KI": 4.195942876445853}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..e1deb6e
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_080.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.35835909843444824}
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+                       [    0,     1,     2,  ..., 31373, 31373, 31378]]),
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..2b2e584
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_090.json
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..d56d587
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_090.output
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..42ba2c3
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_100.json
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..84041b8
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_100.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx', '-c', '1']
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..4fec191
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_110.json
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..73b027a
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_110.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx', '-c', '1']
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..da72dbc
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2656, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.007952213287354, "TIME_S_1KI": 3.7680542971714437, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 730.8129847311974, "W": 52.79, "J_1KI": 275.15549123915565, "W_1KI": 19.875753012048193, "W_D": 35.510999999999996, "J_D": 491.6063629624843, "W_D_1KI": 13.370105421686745, "J_D_1KI": 5.033925234068804}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..2135174
--- /dev/null
+++ b/pytorch/output_as-caida_1core/xeon_4216_1_coo_10_10_10_as-caida_G_120.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.3952159881591797}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.1404, 0.8175, 0.0124,  ..., 0.7312, 0.3496, 0.9826])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.3952159881591797 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '2656', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.007952213287354}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
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+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.007952213287354 seconds
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.5064, 0.7184, 0.1043,  ..., 0.0170, 0.9266, 0.2559])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.007952213287354 seconds
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diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.json
deleted file mode 100644
index d27eaee..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 24585, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.531149864196777, "TIME_S_1KI": 0.4283567160543737, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 791.8324437737465, "W": 51.25, "J_1KI": 32.20794971623943, "W_1KI": 2.0846044335977223, "W_D": 34.32525, "J_D": 530.3384700613617, "W_D_1KI": 1.3961866992068332, "J_D_1KI": 0.05679018503993627}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.output
deleted file mode 100644
index e758720..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_005.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.05796647071838379}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.1404, 0.8835, 0.3196,  ..., 0.0994, 0.4882, 0.6406])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 0.05796647071838379 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18113', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 7.735689640045166}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.5576, 0.2413, 0.8850,  ..., 0.5956, 0.8314, 0.8126])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 7.735689640045166 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '24585', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.531149864196777}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.5694, 0.0146, 0.7356,  ..., 0.9762, 0.7728, 0.4476])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.531149864196777 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.5694, 0.0146, 0.7356,  ..., 0.9762, 0.7728, 0.4476])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.531149864196777 seconds
-
-[18.73, 18.22, 18.48, 18.73, 18.67, 18.5, 18.68, 18.44, 18.46, 18.66]
-[51.25]
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-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 24585, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.531149864196777, 'TIME_S_1KI': 0.4283567160543737, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 791.8324437737465, 'W': 51.25}
-[18.73, 18.22, 18.48, 18.73, 18.67, 18.5, 18.68, 18.44, 18.46, 18.66, 19.19, 18.59, 18.77, 18.6, 18.48, 18.55, 18.66, 18.93, 22.2, 18.49]
-338.495
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-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 24585, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.531149864196777, 'TIME_S_1KI': 0.4283567160543737, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 791.8324437737465, 'W': 51.25, 'J_1KI': 32.20794971623943, 'W_1KI': 2.0846044335977223, 'W_D': 34.32525, 'J_D': 530.3384700613617, 'W_D_1KI': 1.3961866992068332, 'J_D_1KI': 0.05679018503993627}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.json
deleted file mode 100644
index d7993e2..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 22326, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 11.536637306213379, "TIME_S_1KI": 0.5167355238830682, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 786.4002640724183, "W": 51.46000000000001, "J_1KI": 35.22351805394689, "W_1KI": 2.304935949117621, "W_D": 34.605250000000005, "J_D": 528.8297267448903, "W_D_1KI": 1.5499977604586583, "J_D_1KI": 0.06942568128901991}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.output
deleted file mode 100644
index 1bc2ade..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_015.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.06119132041931152}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.5762, 0.9295, 0.5427,  ..., 0.4978, 0.3271, 0.2927])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 0.06119132041931152 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17159', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 8.069887161254883}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.6269, 0.3774, 0.5755,  ..., 0.2711, 0.6777, 0.1164])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 8.069887161254883 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '22326', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 11.536637306213379}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.1977, 0.1619, 0.4572,  ..., 0.7258, 0.1988, 0.9324])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 11.536637306213379 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.1977, 0.1619, 0.4572,  ..., 0.7258, 0.1988, 0.9324])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 11.536637306213379 seconds
-
-[19.37, 18.48, 18.69, 18.78, 18.91, 18.43, 18.91, 18.58, 19.73, 18.48]
-[51.46]
-15.281777381896973
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22326, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 11.536637306213379, 'TIME_S_1KI': 0.5167355238830682, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 786.4002640724183, 'W': 51.46000000000001}
-[19.37, 18.48, 18.69, 18.78, 18.91, 18.43, 18.91, 18.58, 19.73, 18.48, 19.21, 18.86, 18.55, 18.48, 18.51, 18.54, 18.44, 18.39, 18.75, 19.07]
-337.095
-16.854750000000003
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22326, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 11.536637306213379, 'TIME_S_1KI': 0.5167355238830682, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 786.4002640724183, 'W': 51.46000000000001, 'J_1KI': 35.22351805394689, 'W_1KI': 2.304935949117621, 'W_D': 34.605250000000005, 'J_D': 528.8297267448903, 'W_D_1KI': 1.5499977604586583, 'J_D_1KI': 0.06942568128901991}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.json
deleted file mode 100644
index b2b7d85..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18821, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.504743814468384, "TIME_S_1KI": 0.5581395151409799, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 734.5338620448113, "W": 51.230000000000004, "J_1KI": 39.027355722055745, "W_1KI": 2.7219595133096015, "W_D": 34.108250000000005, "J_D": 489.042838182509, "W_D_1KI": 1.812244301578025, "J_D_1KI": 0.09628841727740423}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.output
deleted file mode 100644
index 90f0d29..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_025.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.0701456069946289}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.9196, 0.4027, 0.9458,  ..., 0.2281, 0.7234, 0.8003])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 0.0701456069946289 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14968', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 8.350371360778809}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.8651, 0.5048, 0.3484,  ..., 0.4454, 0.2746, 0.9370])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 8.350371360778809 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18821', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.504743814468384}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.6340, 0.0545, 0.8586,  ..., 0.8211, 0.3677, 0.7072])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.504743814468384 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.6340, 0.0545, 0.8586,  ..., 0.8211, 0.3677, 0.7072])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.504743814468384 seconds
-
-[19.75, 18.72, 18.91, 18.56, 18.68, 18.41, 18.8, 18.57, 19.13, 22.25]
-[51.23]
-14.337963342666626
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18821, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.504743814468384, 'TIME_S_1KI': 0.5581395151409799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 734.5338620448113, 'W': 51.230000000000004}
-[19.75, 18.72, 18.91, 18.56, 18.68, 18.41, 18.8, 18.57, 19.13, 22.25, 19.1, 18.76, 18.51, 18.55, 18.62, 22.18, 18.61, 18.88, 18.75, 18.49]
-342.435
-17.12175
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18821, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.504743814468384, 'TIME_S_1KI': 0.5581395151409799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 734.5338620448113, 'W': 51.230000000000004, 'J_1KI': 39.027355722055745, 'W_1KI': 2.7219595133096015, 'W_D': 34.108250000000005, 'J_D': 489.042838182509, 'W_D_1KI': 1.812244301578025, 'J_D_1KI': 0.09628841727740423}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.json
deleted file mode 100644
index 8823eba..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 20462, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.523966312408447, "TIME_S_1KI": 0.5143175795332053, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 815.268922715187, "W": 51.17, "J_1KI": 39.84307119124167, "W_1KI": 2.5007330661714398, "W_D": 33.975, "J_D": 541.3086114764213, "W_D_1KI": 1.6603948783110156, "J_D_1KI": 0.0811452877681075}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.output
deleted file mode 100644
index 75c581b..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_035.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.07111167907714844}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.4364, 0.4295, 0.9701,  ..., 0.8211, 0.4909, 0.8334])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 0.07111167907714844 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14765', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 7.576246500015259}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.4654, 0.9230, 0.1647,  ..., 0.9259, 0.4832, 0.5316])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 7.576246500015259 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '20462', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.523966312408447}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.9802, 0.9301, 0.1785,  ..., 0.2797, 0.8123, 0.6307])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.523966312408447 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.9802, 0.9301, 0.1785,  ..., 0.2797, 0.8123, 0.6307])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.523966312408447 seconds
-
-[19.45, 18.69, 18.94, 18.43, 18.57, 18.71, 18.77, 18.49, 18.55, 22.44]
-[51.17]
-15.932556629180908
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20462, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.523966312408447, 'TIME_S_1KI': 0.5143175795332053, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 815.268922715187, 'W': 51.17}
-[19.45, 18.69, 18.94, 18.43, 18.57, 18.71, 18.77, 18.49, 18.55, 22.44, 18.92, 18.51, 18.96, 23.11, 18.62, 18.6, 19.39, 18.51, 19.19, 18.91]
-343.90000000000003
-17.195
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20462, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.523966312408447, 'TIME_S_1KI': 0.5143175795332053, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 815.268922715187, 'W': 51.17, 'J_1KI': 39.84307119124167, 'W_1KI': 2.5007330661714398, 'W_D': 33.975, 'J_D': 541.3086114764213, 'W_D_1KI': 1.6603948783110156, 'J_D_1KI': 0.0811452877681075}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.json
deleted file mode 100644
index 076f312..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 20010, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.59175419807434, "TIME_S_1KI": 0.5792980608732804, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 732.6207900404929, "W": 51.54999999999999, "J_1KI": 36.61273313545692, "W_1KI": 2.576211894052973, "W_D": 34.619249999999994, "J_D": 492.00353609329454, "W_D_1KI": 1.7300974512743625, "J_D_1KI": 0.0864616417428467}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.output
deleted file mode 100644
index 641af44..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_045.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.06652593612670898}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.6643, 0.8543, 0.2565,  ..., 0.9055, 0.9867, 0.8474])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 0.06652593612670898 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '15783', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 8.281726121902466}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.0337, 0.0275, 0.6347,  ..., 0.6984, 0.9483, 0.5691])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 8.281726121902466 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '20010', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.59175419807434}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.8084, 0.5325, 0.2590,  ..., 0.3310, 0.8155, 0.3301])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 11.59175419807434 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.8084, 0.5325, 0.2590,  ..., 0.3310, 0.8155, 0.3301])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 11.59175419807434 seconds
-
-[19.13, 18.4, 18.97, 18.7, 19.22, 18.41, 18.83, 18.48, 18.52, 18.45]
-[51.55]
-14.211848497390747
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20010, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.59175419807434, 'TIME_S_1KI': 0.5792980608732804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.6207900404929, 'W': 51.54999999999999}
-[19.13, 18.4, 18.97, 18.7, 19.22, 18.41, 18.83, 18.48, 18.52, 18.45, 19.53, 18.8, 18.54, 18.5, 19.03, 18.53, 18.43, 18.45, 18.93, 22.64]
-338.615
-16.93075
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 20010, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.59175419807434, 'TIME_S_1KI': 0.5792980608732804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.6207900404929, 'W': 51.54999999999999, 'J_1KI': 36.61273313545692, 'W_1KI': 2.576211894052973, 'W_D': 34.619249999999994, 'J_D': 492.00353609329454, 'W_D_1KI': 1.7300974512743625, 'J_D_1KI': 0.0864616417428467}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.json
deleted file mode 100644
index 48cbf8f..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 19548, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.464083194732666, "TIME_S_1KI": 0.5353019845883296, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 732.7007483005524, "W": 51.11, "J_1KI": 37.48213363518275, "W_1KI": 2.6145897278493964, "W_D": 33.930499999999995, "J_D": 486.4195409941673, "W_D_1KI": 1.7357530182115817, "J_D_1KI": 0.08879440445117565}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.output
deleted file mode 100644
index badbc0b..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_055.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.07336711883544922}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.0799, 0.9526, 0.9637,  ..., 0.9474, 0.7458, 0.3085])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 0.07336711883544922 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14311', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 7.686976194381714}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.8753, 0.6638, 0.8748,  ..., 0.2712, 0.9678, 0.3069])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 7.686976194381714 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '19548', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.464083194732666}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.4610, 0.8810, 0.6667,  ..., 0.9302, 0.2771, 0.3767])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.464083194732666 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.4610, 0.8810, 0.6667,  ..., 0.9302, 0.2771, 0.3767])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.464083194732666 seconds
-
-[19.14, 18.52, 19.06, 18.57, 18.57, 18.64, 19.64, 22.11, 19.05, 18.43]
-[51.11]
-14.335761070251465
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19548, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.464083194732666, 'TIME_S_1KI': 0.5353019845883296, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.7007483005524, 'W': 51.11}
-[19.14, 18.52, 19.06, 18.57, 18.57, 18.64, 19.64, 22.11, 19.05, 18.43, 19.35, 18.43, 18.57, 21.26, 18.97, 18.59, 18.67, 18.39, 18.78, 18.62]
-343.59000000000003
-17.1795
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19548, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.464083194732666, 'TIME_S_1KI': 0.5353019845883296, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 732.7007483005524, 'W': 51.11, 'J_1KI': 37.48213363518275, 'W_1KI': 2.6145897278493964, 'W_D': 33.930499999999995, 'J_D': 486.4195409941673, 'W_D_1KI': 1.7357530182115817, 'J_D_1KI': 0.08879440445117565}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.json
deleted file mode 100644
index 9daa366..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 19229, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.47281002998352, "TIME_S_1KI": 0.5446362280921275, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 735.5914888072015, "W": 51.27, "J_1KI": 38.25427681144112, "W_1KI": 2.666285298247439, "W_D": 34.261500000000005, "J_D": 491.5636394337416, "W_D_1KI": 1.781761922096833, "J_D_1KI": 0.0926601446823461}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.output
deleted file mode 100644
index 72ee088..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_065.output
+++ /dev/null
@@ -1,105 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.06865715980529785}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.4901, 0.0666, 0.4753,  ..., 0.5898, 0.7621, 0.4426])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 0.06865715980529785 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '15293', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 9.309351921081543}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.9797, 0.1862, 0.4685,  ..., 0.3877, 0.4753, 0.7598])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 9.309351921081543 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17248', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 9.417802572250366}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.2025, 0.5486, 0.8359,  ..., 0.6633, 0.0587, 0.9608])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 9.417802572250366 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '19229', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.47281002998352}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.9709, 0.1511, 0.4432,  ..., 0.8939, 0.4938, 0.1652])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.47281002998352 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.9709, 0.1511, 0.4432,  ..., 0.8939, 0.4938, 0.1652])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.47281002998352 seconds
-
-[18.91, 22.63, 19.24, 18.52, 18.84, 18.59, 18.72, 18.36, 18.85, 18.37]
-[51.27]
-14.347405672073364
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19229, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.47281002998352, 'TIME_S_1KI': 0.5446362280921275, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.5914888072015, 'W': 51.27}
-[18.91, 22.63, 19.24, 18.52, 18.84, 18.59, 18.72, 18.36, 18.85, 18.37, 19.24, 18.47, 18.46, 18.62, 19.16, 18.39, 18.45, 18.47, 19.01, 18.26]
-340.16999999999996
-17.008499999999998
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 19229, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.47281002998352, 'TIME_S_1KI': 0.5446362280921275, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 735.5914888072015, 'W': 51.27, 'J_1KI': 38.25427681144112, 'W_1KI': 2.666285298247439, 'W_D': 34.261500000000005, 'J_D': 491.5636394337416, 'W_D_1KI': 1.781761922096833, 'J_D_1KI': 0.0926601446823461}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.json
deleted file mode 100644
index b670e50..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18895, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.52974534034729, "TIME_S_1KI": 0.557276810814887, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 731.8454258918762, "W": 51.4, "J_1KI": 38.73222682677302, "W_1KI": 2.7202963747023023, "W_D": 34.51575, "J_D": 491.44345834100244, "W_D_1KI": 1.8267134162476844, "J_D_1KI": 0.09667707945211348}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.output
deleted file mode 100644
index 4542b1a..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_075.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.06986713409423828}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.6412, 0.9170, 0.6451,  ..., 0.6226, 0.2917, 0.6640])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 0.06986713409423828 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '15028', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 8.351078987121582}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.1989, 0.7767, 0.9282,  ..., 0.6106, 0.4662, 0.7304])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 8.351078987121582 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18895', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.52974534034729}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.2342, 0.7865, 0.1763,  ..., 0.6603, 0.8473, 0.6206])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.52974534034729 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.2342, 0.7865, 0.1763,  ..., 0.6603, 0.8473, 0.6206])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.52974534034729 seconds
-
-[19.04, 18.64, 18.67, 18.52, 18.72, 19.12, 19.11, 18.73, 18.51, 19.11]
-[51.4]
-14.238237857818604
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18895, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.52974534034729, 'TIME_S_1KI': 0.557276810814887, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.8454258918762, 'W': 51.4}
-[19.04, 18.64, 18.67, 18.52, 18.72, 19.12, 19.11, 18.73, 18.51, 19.11, 19.23, 18.93, 18.61, 19.01, 18.59, 18.49, 18.73, 18.71, 18.62, 18.57]
-337.685
-16.88425
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18895, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.52974534034729, 'TIME_S_1KI': 0.557276810814887, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 731.8454258918762, 'W': 51.4, 'J_1KI': 38.73222682677302, 'W_1KI': 2.7202963747023023, 'W_D': 34.51575, 'J_D': 491.44345834100244, 'W_D_1KI': 1.8267134162476844, 'J_D_1KI': 0.09667707945211348}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.json
deleted file mode 100644
index 3ea5426..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18645, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.518952131271362, "TIME_S_1KI": 0.5641701330797191, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 737.6119203472138, "W": 51.34, "J_1KI": 39.56084314010264, "W_1KI": 2.7535532314293376, "W_D": 34.097500000000004, "J_D": 489.8855172193051, "W_D_1KI": 1.828774470367391, "J_D_1KI": 0.09808390830610839}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.output
deleted file mode 100644
index 9d8d636..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_085.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.0699915885925293}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.7446, 0.2717, 0.8801,  ..., 0.1140, 0.4332, 0.2882])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 0.0699915885925293 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '15001', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 8.447453022003174}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.7520, 0.7891, 0.1920,  ..., 0.3124, 0.0972, 0.0468])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 8.447453022003174 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18645', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.518952131271362}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.3323, 0.6853, 0.9554,  ..., 0.7735, 0.0729, 0.3877])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.518952131271362 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.3323, 0.6853, 0.9554,  ..., 0.7735, 0.0729, 0.3877])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.518952131271362 seconds
-
-[18.83, 18.36, 18.45, 18.83, 18.31, 18.39, 23.19, 18.95, 18.51, 18.47]
-[51.34]
-14.367197513580322
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18645, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.518952131271362, 'TIME_S_1KI': 0.5641701330797191, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 737.6119203472138, 'W': 51.34}
-[18.83, 18.36, 18.45, 18.83, 18.31, 18.39, 23.19, 18.95, 18.51, 18.47, 19.38, 21.16, 20.25, 18.57, 19.26, 18.53, 18.9, 18.58, 19.02, 18.5]
-344.84999999999997
-17.2425
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18645, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.518952131271362, 'TIME_S_1KI': 0.5641701330797191, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 737.6119203472138, 'W': 51.34, 'J_1KI': 39.56084314010264, 'W_1KI': 2.7535532314293376, 'W_D': 34.097500000000004, 'J_D': 489.8855172193051, 'W_D_1KI': 1.828774470367391, 'J_D_1KI': 0.09808390830610839}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.json
deleted file mode 100644
index 3926144..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 18160, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.447108745574951, "TIME_S_1KI": 0.5752813185889291, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 728.1026530575753, "W": 51.47, "J_1KI": 40.09375842828058, "W_1KI": 2.834251101321586, "W_D": 34.3515, "J_D": 485.94168032848836, "W_D_1KI": 1.891602422907489, "J_D_1KI": 0.10416312901472957}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.output
deleted file mode 100644
index c40d387..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_095.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.07135295867919922}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2816, 0.0519, 0.4526,  ..., 0.4658, 0.0063, 0.4440])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 0.07135295867919922 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14715', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 8.507978677749634}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2864, 0.5383, 0.4718,  ..., 0.4578, 0.9727, 0.9566])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 8.507978677749634 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '18160', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.447108745574951}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.7032, 0.3468, 0.7464,  ..., 0.5468, 0.4215, 0.7103])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.447108745574951 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.7032, 0.3468, 0.7464,  ..., 0.5468, 0.4215, 0.7103])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.447108745574951 seconds
-
-[19.54, 18.52, 18.53, 18.56, 18.7, 18.39, 18.57, 19.3, 18.89, 22.85]
-[51.47]
-14.146156072616577
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18160, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.447108745574951, 'TIME_S_1KI': 0.5752813185889291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 728.1026530575753, 'W': 51.47}
-[19.54, 18.52, 18.53, 18.56, 18.7, 18.39, 18.57, 19.3, 18.89, 22.85, 18.87, 18.66, 18.64, 18.39, 22.24, 19.23, 18.56, 18.41, 18.82, 18.66]
-342.37
-17.1185
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 18160, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.447108745574951, 'TIME_S_1KI': 0.5752813185889291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 728.1026530575753, 'W': 51.47, 'J_1KI': 40.09375842828058, 'W_1KI': 2.834251101321586, 'W_D': 34.3515, 'J_D': 485.94168032848836, 'W_D_1KI': 1.891602422907489, 'J_D_1KI': 0.10416312901472957}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.json
deleted file mode 100644
index ef2ea78..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 17816, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.415440320968628, "TIME_S_1KI": 0.5846116031078036, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 736.0956484270096, "W": 51.53, "J_1KI": 41.31654964228837, "W_1KI": 2.8923439604849577, "W_D": 34.6645, "J_D": 495.17538530755036, "W_D_1KI": 1.9456948810058372, "J_D_1KI": 0.109210534407602}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.output
deleted file mode 100644
index 8d2a378..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_105.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.07289290428161621}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.2212, 0.1777, 0.5151,  ..., 0.3390, 0.7939, 0.3077])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 0.07289290428161621 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14404', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 8.488957166671753}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.1035, 0.0189, 0.8464,  ..., 0.3615, 0.8119, 0.4494])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 8.488957166671753 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17816', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.415440320968628}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.8605, 0.5482, 0.2234,  ..., 0.4827, 0.0878, 0.0084])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.415440320968628 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.8605, 0.5482, 0.2234,  ..., 0.4827, 0.0878, 0.0084])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.415440320968628 seconds
-
-[19.37, 18.59, 18.91, 18.57, 18.46, 19.18, 18.55, 18.37, 18.39, 18.4]
-[51.53]
-14.28479814529419
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17816, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.415440320968628, 'TIME_S_1KI': 0.5846116031078036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 736.0956484270096, 'W': 51.53}
-[19.37, 18.59, 18.91, 18.57, 18.46, 19.18, 18.55, 18.37, 18.39, 18.4, 19.0, 18.46, 18.84, 18.52, 18.63, 19.09, 19.6, 18.71, 18.79, 18.53]
-337.31000000000006
-16.865500000000004
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17816, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.415440320968628, 'TIME_S_1KI': 0.5846116031078036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 736.0956484270096, 'W': 51.53, 'J_1KI': 41.31654964228837, 'W_1KI': 2.8923439604849577, 'W_D': 34.6645, 'J_D': 495.17538530755036, 'W_D_1KI': 1.9456948810058372, 'J_D_1KI': 0.109210534407602}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.json
deleted file mode 100644
index 0480bc3..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 17784, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.485624551773071, "TIME_S_1KI": 0.5896100175310993, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 734.9326595020294, "W": 51.42, "J_1KI": 41.325498172628734, "W_1KI": 2.8913630229419707, "W_D": 34.413, "J_D": 491.8560406737327, "W_D_1KI": 1.9350539811066125, "J_D_1KI": 0.10880870339106008}
diff --git a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.output
deleted file mode 100644
index 588bc33..0000000
--- a/pytorch/output_as-caida_1core/xeon_4216_1_csr_10_10_10_as-caida_G_115.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.073089599609375}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.4304, 0.3415, 0.8565,  ..., 0.5779, 0.8119, 0.1412])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 0.073089599609375 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '14365', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 8.481326341629028}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.0724, 0.0070, 0.1571,  ..., 0.0880, 0.9717, 0.4875])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 8.481326341629028 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '17784', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.485624551773071}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.0693, 0.9266, 0.3660,  ..., 0.8825, 0.1759, 0.2255])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.485624551773071 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.0693, 0.9266, 0.3660,  ..., 0.8825, 0.1759, 0.2255])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.485624551773071 seconds
-
-[19.17, 18.45, 18.63, 18.57, 22.5, 18.51, 18.76, 18.65, 18.52, 18.6]
-[51.42]
-14.292739391326904
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17784, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.485624551773071, 'TIME_S_1KI': 0.5896100175310993, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 734.9326595020294, 'W': 51.42}
-[19.17, 18.45, 18.63, 18.57, 22.5, 18.51, 18.76, 18.65, 18.52, 18.6, 19.86, 18.33, 18.58, 18.45, 18.8, 18.42, 18.91, 18.37, 19.02, 19.71]
-340.14000000000004
-17.007
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 17784, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.485624551773071, 'TIME_S_1KI': 0.5896100175310993, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 734.9326595020294, 'W': 51.42, 'J_1KI': 41.325498172628734, 'W_1KI': 2.8913630229419707, 'W_D': 34.413, 'J_D': 491.8560406737327, 'W_D_1KI': 1.9350539811066125, 'J_D_1KI': 0.10880870339106008}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..46e4410
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 6520, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.294081211090088, "TIME_S_1KI": 1.5788468115168846, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 348.8066624069213, "W": 24.428518062094398, "J_1KI": 53.49795435688977, "W_1KI": 3.746705224247607, "W_D": 5.844518062094398, "J_D": 83.45192424011222, "W_D_1KI": 0.8963984757813493, "J_D_1KI": 0.13748442880082046}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..1a3175f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_010.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.20679163932800293}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..b6114a8
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_020.json
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..7df181c
--- /dev/null
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..32d48e6
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_030.json
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..2b1b2ce
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_030.output
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..8db8426
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_040.json
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..d3f9184
--- /dev/null
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..31b3512
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_050.json
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..d18f417
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_050.output
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..b85e4be
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_060.json
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..7130655
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_060.output
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..e9d6e60
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_070.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 6057, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 10.495833396911621, "TIME_S_1KI": 1.732843552404098, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.9003308677673, "W": 23.162184495225404, "J_1KI": 54.46596184047669, "W_1KI": 3.8240357429792646, "W_D": 4.690184495225402, "J_D": 66.80256851959226, "W_D_1KI": 0.7743411747111445, "J_D_1KI": 0.12784236003155763}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..d73b830
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_070.output
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..fac3eeb
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_080.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4719, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.253804922103882, "TIME_S_1KI": 2.1728766522788474, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 354.2040447235107, "W": 24.884304983030177, "J_1KI": 75.0591321728143, "W_1KI": 5.273215720074205, "W_D": 6.290304983030175, "J_D": 89.53641538524623, "W_D_1KI": 1.3329741434689926, "J_D_1KI": 0.28246962141745974}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..245dd39
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_080.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_080.mtx']
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..d5334aa
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_090.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4846, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 10.36161494255066, "TIME_S_1KI": 2.138178898586599, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 351.4446623516082, "W": 24.65769698049189, "J_1KI": 72.52262945761622, "W_1KI": 5.08825773431529, "W_D": 6.245696980491893, "J_D": 89.01954096508018, "W_D_1KI": 1.2888355304358012, "J_D_1KI": 0.265958631951259}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..bae7c4a
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_090.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_090.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.21666717529296875}
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+                       [    2,     2,     2,  ..., 31373, 31376, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=100924, layout=torch.sparse_coo)
+tensor([0.9488, 0.9009, 0.6804,  ..., 0.0977, 0.6993, 0.4762])
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 4846 -m matrices/as-caida_pruned/as-caida_G_090.mtx']
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..4663799
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_100.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4840, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 10.68853497505188, "TIME_S_1KI": 2.20837499484543, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 366.4244141674042, "W": 24.001572968085217, "J_1KI": 75.70752358830666, "W_1KI": 4.95900267935645, "W_D": 5.636572968085215, "J_D": 86.0517746269703, "W_D_1KI": 1.1645811917531437, "J_D_1KI": 0.2406159487093272}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..519b46f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_100.output
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+tensor([0.3383, 0.7557, 0.1440,  ..., 0.8833, 0.2656, 0.4061])
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..3000ad6
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_110.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4705, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 10.883554220199585, "TIME_S_1KI": 2.3131889947289235, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 363.10562927246093, "W": 25.50254716925525, "J_1KI": 77.17441642347734, "W_1KI": 5.420307581138204, "W_D": 7.0935471692552525, "J_D": 100.99802547454833, "W_D_1KI": 1.5076614599904894, "J_D_1KI": 0.32043814239967894}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..61a043a
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_110.output
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diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..96cc02a
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_120.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4648, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.618494272232056, "TIME_S_1KI": 2.284529748759048, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 334.98270019531253, "W": 23.516723983529083, "J_1KI": 72.07028833806207, "W_1KI": 5.059536141034656, "W_D": 5.003723983529085, "J_D": 71.2752750854493, "W_D_1KI": 1.076532698693865, "J_D_1KI": 0.23161202639713102}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..87fd18c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/altra_max_coo_10_10_10_as-caida_G_120.output
@@ -0,0 +1,77 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 100 -m matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.2820718288421631}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.4852, 0.3480, 0.0346,  ..., 0.0697, 0.5303, 0.1133])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.2820718288421631 seconds
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 3722 -m matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 8.406552076339722}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.1794, 0.0744, 0.4639,  ..., 0.4809, 0.9452, 0.1633])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 8.406552076339722 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse coo 4648 -m matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.618494272232056}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.8721, 0.5237, 0.9372,  ..., 0.6662, 0.6718, 0.8104])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.618494272232056 seconds
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.8721, 0.5237, 0.9372,  ..., 0.6662, 0.6718, 0.8104])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.618494272232056 seconds
+
+[20.52, 20.56, 20.6, 20.68, 20.44, 20.36, 20.52, 20.6, 20.6, 20.72]
+[20.92, 20.88, 21.2, 23.52, 24.0, 28.92, 29.96, 29.52, 28.68, 24.92, 24.8, 24.8, 24.92, 24.72]
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+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4648, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.618494272232056, 'TIME_S_1KI': 2.284529748759048, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.98270019531253, 'W': 23.516723983529083}
+[20.52, 20.56, 20.6, 20.68, 20.44, 20.36, 20.52, 20.6, 20.6, 20.72, 20.16, 20.32, 20.6, 20.52, 20.72, 20.88, 20.84, 20.52, 20.56, 20.48]
+370.26
+18.512999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4648, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 10.618494272232056, 'TIME_S_1KI': 2.284529748759048, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.98270019531253, 'W': 23.516723983529083, 'J_1KI': 72.07028833806207, 'W_1KI': 5.059536141034656, 'W_D': 5.003723983529085, 'J_D': 71.2752750854493, 'W_D_1KI': 1.076532698693865, 'J_D_1KI': 0.23161202639713102}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.json
deleted file mode 100644
index 83b3559..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 5968, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.525087594985962, "TIME_S_1KI": 1.7635870635030098, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 272.15640623092645, "W": 19.161924669821744, "J_1KI": 45.60261498507481, "W_1KI": 3.2107782623695953, "W_D": 4.226924669821745, "J_D": 60.03492067575449, "W_D_1KI": 0.708264857543858, "J_D_1KI": 0.11867708739005664}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.output
deleted file mode 100644
index 7a87255..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_005.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_005.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.23140215873718262}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.4043, 0.1456, 0.8788,  ..., 0.2899, 0.4254, 0.2185])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 0.23140215873718262 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4537 -m matrices/as-caida_pruned/as-caida_G_005.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 7.981162071228027}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.6074, 0.8880, 0.3045,  ..., 0.0454, 0.8927, 0.3776])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 7.981162071228027 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5968 -m matrices/as-caida_pruned/as-caida_G_005.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.525087594985962}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.9329, 0.1644, 0.2943,  ..., 0.4186, 0.6680, 0.2503])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.525087594985962 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.9329, 0.1644, 0.2943,  ..., 0.4186, 0.6680, 0.2503])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.525087594985962 seconds
-
-[16.68, 16.44, 16.68, 16.88, 16.84, 16.72, 16.76, 16.76, 16.64, 16.56]
-[16.84, 16.52, 17.32, 18.8, 20.28, 23.6, 24.52, 23.72, 23.16, 20.48, 20.48, 20.32, 20.28, 20.28]
-14.202978610992432
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5968, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.525087594985962, 'TIME_S_1KI': 1.7635870635030098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 272.15640623092645, 'W': 19.161924669821744}
-[16.68, 16.44, 16.68, 16.88, 16.84, 16.72, 16.76, 16.76, 16.64, 16.56, 16.48, 16.48, 16.48, 16.6, 16.28, 16.6, 16.48, 16.48, 16.48, 16.48]
-298.7
-14.934999999999999
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5968, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.525087594985962, 'TIME_S_1KI': 1.7635870635030098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 272.15640623092645, 'W': 19.161924669821744, 'J_1KI': 45.60261498507481, 'W_1KI': 3.2107782623695953, 'W_D': 4.226924669821745, 'J_D': 60.03492067575449, 'W_D_1KI': 0.708264857543858, 'J_D_1KI': 0.11867708739005664}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.json
deleted file mode 100644
index 8be9e23..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 5272, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.199397802352905, "TIME_S_1KI": 1.9346353949834798, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 269.08102276802066, "W": 20.433569505252233, "J_1KI": 51.039647717758086, "W_1KI": 3.8758667498581625, "W_D": 5.440569505252233, "J_D": 71.64455561900142, "W_D_1KI": 1.0319744888566451, "J_D_1KI": 0.19574629910027413}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.output
deleted file mode 100644
index c2ade6c..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_015.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_015.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.25819969177246094}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.1310, 0.9221, 0.7402,  ..., 0.2439, 0.9445, 0.3797])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 0.25819969177246094 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4066 -m matrices/as-caida_pruned/as-caida_G_015.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 8.096591711044312}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.1967, 0.8576, 0.8759,  ..., 0.1214, 0.7615, 0.8638])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 8.096591711044312 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 5272 -m matrices/as-caida_pruned/as-caida_G_015.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.199397802352905}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.1490, 0.8093, 0.3487,  ..., 0.4943, 0.2296, 0.2275])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.199397802352905 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.1490, 0.8093, 0.3487,  ..., 0.4943, 0.2296, 0.2275])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.199397802352905 seconds
-
-[16.92, 16.92, 16.92, 16.36, 16.52, 16.48, 16.64, 16.6, 16.56, 16.6]
-[16.64, 16.92, 17.6, 18.72, 24.2, 24.2, 25.08, 25.84, 25.64, 25.96, 20.96, 21.28, 21.48]
-13.16857647895813
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5272, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.199397802352905, 'TIME_S_1KI': 1.9346353949834798, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 269.08102276802066, 'W': 20.433569505252233}
-[16.92, 16.92, 16.92, 16.36, 16.52, 16.48, 16.64, 16.6, 16.56, 16.6, 16.8, 16.72, 16.72, 16.72, 16.72, 16.72, 16.72, 16.68, 16.44, 16.52]
-299.86
-14.993
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 5272, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.199397802352905, 'TIME_S_1KI': 1.9346353949834798, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 269.08102276802066, 'W': 20.433569505252233, 'J_1KI': 51.039647717758086, 'W_1KI': 3.8758667498581625, 'W_D': 5.440569505252233, 'J_D': 71.64455561900142, 'W_D_1KI': 1.0319744888566451, 'J_D_1KI': 0.19574629910027413}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.json
deleted file mode 100644
index 89e7aab..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4821, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.69611644744873, "TIME_S_1KI": 2.2186509951148583, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 249.86814396858213, "W": 18.967472090057612, "J_1KI": 51.82911096631034, "W_1KI": 3.934343930731718, "W_D": 4.019472090057613, "J_D": 52.95054744815828, "W_D_1KI": 0.8337423957804633, "J_D_1KI": 0.17293972117412637}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.output
deleted file mode 100644
index 2fd70ad..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_025.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_025.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.26701927185058594}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.7832, 0.3673, 0.6801,  ..., 0.8769, 0.0952, 0.5341])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 0.26701927185058594 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3932 -m matrices/as-caida_pruned/as-caida_G_025.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 8.563699960708618}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.7522, 0.7746, 0.7995,  ..., 0.5259, 0.5024, 0.1696])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 8.563699960708618 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4821 -m matrices/as-caida_pruned/as-caida_G_025.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.69611644744873}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.0926, 0.0666, 0.7412,  ..., 0.4121, 0.6596, 0.6440])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.69611644744873 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.0926, 0.0666, 0.7412,  ..., 0.4121, 0.6596, 0.6440])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.69611644744873 seconds
-
-[17.0, 16.88, 16.88, 16.76, 16.68, 16.68, 16.72, 16.72, 16.24, 16.32]
-[16.36, 16.28, 16.64, 18.24, 19.08, 22.76, 23.84, 23.92, 23.92, 21.0, 21.0, 21.08, 20.68]
-13.173507928848267
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4821, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.69611644744873, 'TIME_S_1KI': 2.2186509951148583, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 249.86814396858213, 'W': 18.967472090057612}
-[17.0, 16.88, 16.88, 16.76, 16.68, 16.68, 16.72, 16.72, 16.24, 16.32, 16.44, 16.68, 16.8, 16.64, 16.6, 16.24, 16.32, 16.28, 16.52, 16.88]
-298.96
-14.947999999999999
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4821, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.69611644744873, 'TIME_S_1KI': 2.2186509951148583, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 249.86814396858213, 'W': 18.967472090057612, 'J_1KI': 51.82911096631034, 'W_1KI': 3.934343930731718, 'W_D': 4.019472090057613, 'J_D': 52.95054744815828, 'W_D_1KI': 0.8337423957804633, 'J_D_1KI': 0.17293972117412637}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.json
deleted file mode 100644
index fb8fd0b..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4874, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.324414014816284, "TIME_S_1KI": 2.118263031353362, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.37779233932497, "W": 22.209521493082242, "J_1KI": 64.91132382833914, "W_1KI": 4.556733995297957, "W_D": 7.24852149308224, "J_D": 103.25621956419945, "W_D_1KI": 1.4871812665330817, "J_D_1KI": 0.3051254137326799}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.output
deleted file mode 100644
index 13dde5b..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_035.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_035.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.2807776927947998}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.6842, 0.1495, 0.0414,  ..., 0.6601, 0.3430, 0.9557])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 0.2807776927947998 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3739 -m matrices/as-caida_pruned/as-caida_G_035.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 8.053443431854248}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.8981, 0.1545, 0.9805,  ..., 0.9874, 0.0100, 0.6766])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 8.053443431854248 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4874 -m matrices/as-caida_pruned/as-caida_G_035.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.324414014816284}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.0642, 0.5090, 0.9459,  ..., 0.2103, 0.6817, 0.2365])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.324414014816284 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.0642, 0.5090, 0.9459,  ..., 0.2103, 0.6817, 0.2365])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.324414014816284 seconds
-
-[16.24, 16.12, 16.2, 16.04, 16.04, 16.52, 17.12, 17.28, 17.36, 17.08]
-[16.84, 16.76, 19.6, 21.72, 28.36, 29.16, 30.08, 30.08, 27.72, 26.6, 20.76, 20.68, 20.76, 21.04]
-14.245142221450806
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4874, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.324414014816284, 'TIME_S_1KI': 2.118263031353362, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.37779233932497, 'W': 22.209521493082242}
-[16.24, 16.12, 16.2, 16.04, 16.04, 16.52, 17.12, 17.28, 17.36, 17.08, 16.88, 16.88, 16.88, 16.92, 16.68, 16.76, 16.44, 16.44, 16.28, 16.32]
-299.22
-14.961000000000002
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4874, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.324414014816284, 'TIME_S_1KI': 2.118263031353362, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.37779233932497, 'W': 22.209521493082242, 'J_1KI': 64.91132382833914, 'W_1KI': 4.556733995297957, 'W_D': 7.24852149308224, 'J_D': 103.25621956419945, 'W_D_1KI': 1.4871812665330817, 'J_D_1KI': 0.3051254137326799}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.json
deleted file mode 100644
index 7e89948..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4657, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.087504863739014, "TIME_S_1KI": 2.380825609563885, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 301.57965709686283, "W": 22.814763029917664, "J_1KI": 64.75835454087671, "W_1KI": 4.89902577408582, "W_D": 7.638763029917666, "J_D": 100.97389712905891, "W_D_1KI": 1.640275505672679, "J_D_1KI": 0.35221720113220506}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.output
deleted file mode 100644
index a098f6a..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_045.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_045.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.30149173736572266}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.3987, 0.5045, 0.3045,  ..., 0.2984, 0.8050, 0.7869])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 0.30149173736572266 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3482 -m matrices/as-caida_pruned/as-caida_G_045.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 7.849581480026245}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.1977, 0.0810, 0.4349,  ..., 0.8550, 0.4211, 0.9804])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 7.849581480026245 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4657 -m matrices/as-caida_pruned/as-caida_G_045.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 11.087504863739014}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.6761, 0.3112, 0.9967,  ..., 0.7840, 0.7388, 0.4121])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 11.087504863739014 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.6761, 0.3112, 0.9967,  ..., 0.7840, 0.7388, 0.4121])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 11.087504863739014 seconds
-
-[16.56, 16.8, 16.88, 16.88, 17.04, 16.96, 17.2, 17.12, 17.32, 17.0]
-[16.88, 16.68, 19.68, 21.68, 28.56, 30.0, 30.0, 30.92, 28.64, 27.84, 21.96, 21.6, 21.68]
-13.218618869781494
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4657, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.087504863739014, 'TIME_S_1KI': 2.380825609563885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 301.57965709686283, 'W': 22.814763029917664}
-[16.56, 16.8, 16.88, 16.88, 17.04, 16.96, 17.2, 17.12, 17.32, 17.0, 16.6, 16.52, 16.68, 16.76, 16.84, 16.88, 16.6, 16.6, 16.84, 17.04]
-303.52
-15.175999999999998
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4657, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 11.087504863739014, 'TIME_S_1KI': 2.380825609563885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 301.57965709686283, 'W': 22.814763029917664, 'J_1KI': 64.75835454087671, 'W_1KI': 4.89902577408582, 'W_D': 7.638763029917666, 'J_D': 100.97389712905891, 'W_D_1KI': 1.640275505672679, 'J_D_1KI': 0.35221720113220506}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.json
deleted file mode 100644
index efbb301..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4739, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.536023616790771, "TIME_S_1KI": 2.2232588345201036, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 296.09617205619816, "W": 20.864532191184587, "J_1KI": 62.48072843557674, "W_1KI": 4.40272888609086, "W_D": 5.663532191184588, "J_D": 80.37324713349346, "W_D_1KI": 1.1950901437401538, "J_D_1KI": 0.2521819252458649}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.output
deleted file mode 100644
index 213e50c..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_055.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_055.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.2958800792694092}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.0279, 0.3720, 0.5419,  ..., 0.9391, 0.6222, 0.1335])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 0.2958800792694092 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3548 -m matrices/as-caida_pruned/as-caida_G_055.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 7.860142707824707}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.0798, 0.6202, 0.2546,  ..., 0.5902, 0.1772, 0.5374])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 7.860142707824707 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4739 -m matrices/as-caida_pruned/as-caida_G_055.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.536023616790771}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.8748, 0.1554, 0.2380,  ..., 0.7350, 0.2527, 0.7569])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.536023616790771 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.8748, 0.1554, 0.2380,  ..., 0.7350, 0.2527, 0.7569])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.536023616790771 seconds
-
-[16.32, 16.6, 16.8, 16.8, 17.0, 16.76, 16.84, 17.08, 17.24, 17.52]
-[17.4, 17.4, 17.0, 20.2, 22.04, 27.64, 28.64, 29.48, 25.76, 23.6, 20.68, 20.52, 20.36, 20.36]
-14.191364049911499
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4739, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.536023616790771, 'TIME_S_1KI': 2.2232588345201036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.09617205619816, 'W': 20.864532191184587}
-[16.32, 16.6, 16.8, 16.8, 17.0, 16.76, 16.84, 17.08, 17.24, 17.52, 17.2, 17.2, 17.16, 17.04, 16.92, 16.8, 16.6, 16.6, 16.64, 16.84]
-304.02
-15.200999999999999
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4739, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.536023616790771, 'TIME_S_1KI': 2.2232588345201036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.09617205619816, 'W': 20.864532191184587, 'J_1KI': 62.48072843557674, 'W_1KI': 4.40272888609086, 'W_D': 5.663532191184588, 'J_D': 80.37324713349346, 'W_D_1KI': 1.1950901437401538, 'J_D_1KI': 0.2521819252458649}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.json
deleted file mode 100644
index a60fb61..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4568, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.563419342041016, "TIME_S_1KI": 2.3124823428285937, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 318.5167537593842, "W": 20.961702986599526, "J_1KI": 69.72783576168655, "W_1KI": 4.588814138922839, "W_D": 5.795702986599526, "J_D": 88.0667235016823, "W_D_1KI": 1.268761599518285, "J_D_1KI": 0.27774991232887153}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.output
deleted file mode 100644
index e314abf..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_065.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_065.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.2923262119293213}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.4849, 0.2731, 0.9040,  ..., 0.3334, 0.6361, 0.9845])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 0.2923262119293213 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3591 -m matrices/as-caida_pruned/as-caida_G_065.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 8.252921104431152}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.4814, 0.9926, 0.6267,  ..., 0.1528, 0.7014, 0.4043])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 8.252921104431152 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4568 -m matrices/as-caida_pruned/as-caida_G_065.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.563419342041016}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.5323, 0.6987, 0.7869,  ..., 0.7650, 0.9130, 0.3621])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.563419342041016 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.5323, 0.6987, 0.7869,  ..., 0.7650, 0.9130, 0.3621])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.563419342041016 seconds
-
-[17.04, 17.04, 17.04, 17.04, 17.0, 17.0, 16.92, 16.92, 16.92, 16.84]
-[16.56, 16.68, 19.6, 21.2, 21.2, 26.52, 27.64, 28.44, 24.84, 24.72, 20.76, 20.96, 21.36, 21.44, 21.4]
-15.195175409317017
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4568, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.563419342041016, 'TIME_S_1KI': 2.3124823428285937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.5167537593842, 'W': 20.961702986599526}
-[17.04, 17.04, 17.04, 17.04, 17.0, 17.0, 16.92, 16.92, 16.92, 16.84, 16.8, 16.64, 16.72, 16.72, 16.48, 16.44, 16.68, 16.96, 16.92, 17.08]
-303.32
-15.166
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4568, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.563419342041016, 'TIME_S_1KI': 2.3124823428285937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.5167537593842, 'W': 20.961702986599526, 'J_1KI': 69.72783576168655, 'W_1KI': 4.588814138922839, 'W_D': 5.795702986599526, 'J_D': 88.0667235016823, 'W_D_1KI': 1.268761599518285, 'J_D_1KI': 0.27774991232887153}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.json
deleted file mode 100644
index 72a8002..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4489, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.591835021972656, "TIME_S_1KI": 2.359508804181924, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 293.3003367614746, "W": 20.65804885532527, "J_1KI": 65.33756666551005, "W_1KI": 4.601926677506186, "W_D": 5.662048855325271, "J_D": 80.38904582214357, "W_D_1KI": 1.2613162965750213, "J_D_1KI": 0.28097934875808}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.output
deleted file mode 100644
index 05a6be0..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_075.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_075.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.2903709411621094}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.5496, 0.8721, 0.4707,  ..., 0.6399, 0.3015, 0.0205])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 0.2903709411621094 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3616 -m matrices/as-caida_pruned/as-caida_G_075.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 8.456918239593506}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.8149, 0.0064, 0.2491,  ..., 0.0040, 0.4481, 0.7236])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 8.456918239593506 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4489 -m matrices/as-caida_pruned/as-caida_G_075.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.591835021972656}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.6259, 0.8430, 0.9333,  ..., 0.4721, 0.8188, 0.1886])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.591835021972656 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.6259, 0.8430, 0.9333,  ..., 0.4721, 0.8188, 0.1886])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.591835021972656 seconds
-
-[16.64, 16.72, 16.88, 17.2, 17.0, 16.92, 16.72, 16.44, 16.24, 16.36]
-[16.36, 16.44, 16.52, 20.16, 21.24, 27.36, 28.6, 29.16, 26.2, 23.56, 20.64, 20.4, 20.52, 20.52]
-14.197872161865234
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4489, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.591835021972656, 'TIME_S_1KI': 2.359508804181924, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 293.3003367614746, 'W': 20.65804885532527}
-[16.64, 16.72, 16.88, 17.2, 17.0, 16.92, 16.72, 16.44, 16.24, 16.36, 16.16, 16.08, 16.2, 16.48, 16.4, 16.56, 16.8, 17.04, 17.12, 17.08]
-299.91999999999996
-14.995999999999999
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4489, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.591835021972656, 'TIME_S_1KI': 2.359508804181924, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 293.3003367614746, 'W': 20.65804885532527, 'J_1KI': 65.33756666551005, 'W_1KI': 4.601926677506186, 'W_D': 5.662048855325271, 'J_D': 80.38904582214357, 'W_D_1KI': 1.2613162965750213, 'J_D_1KI': 0.28097934875808}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.json
deleted file mode 100644
index 41045b6..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4271, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.287059783935547, "TIME_S_1KI": 2.408583419324642, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 283.75852571487434, "W": 19.954165262711065, "J_1KI": 66.43842793605111, "W_1KI": 4.672012470782268, "W_D": 4.871165262711063, "J_D": 69.27048339343077, "W_D_1KI": 1.1405210167902278, "J_D_1KI": 0.26703840243273885}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.output
deleted file mode 100644
index 918dbe8..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_085.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_085.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.30860424041748047}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.6175, 0.3198, 0.3809,  ..., 0.8583, 0.2210, 0.7236])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 0.30860424041748047 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3402 -m matrices/as-caida_pruned/as-caida_G_085.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 8.36231279373169}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.2189, 0.7362, 0.7287,  ..., 0.2468, 0.1466, 0.5130])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 8.36231279373169 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4271 -m matrices/as-caida_pruned/as-caida_G_085.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.287059783935547}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.3002, 0.9937, 0.1201,  ..., 0.2870, 0.2280, 0.7451])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.287059783935547 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.3002, 0.9937, 0.1201,  ..., 0.2870, 0.2280, 0.7451])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.287059783935547 seconds
-
-[16.52, 16.64, 16.6, 16.32, 16.16, 16.4, 16.72, 17.0, 17.0, 17.44]
-[17.48, 17.52, 17.16, 19.72, 20.28, 25.76, 26.2, 26.24, 25.12, 20.56, 20.6, 20.6, 20.56, 20.4]
-14.220515966415405
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4271, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.287059783935547, 'TIME_S_1KI': 2.408583419324642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 283.75852571487434, 'W': 19.954165262711065}
-[16.52, 16.64, 16.6, 16.32, 16.16, 16.4, 16.72, 17.0, 17.0, 17.44, 17.12, 17.2, 17.36, 17.12, 16.88, 16.76, 16.76, 16.32, 16.56, 16.64]
-301.66
-15.083000000000002
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4271, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.287059783935547, 'TIME_S_1KI': 2.408583419324642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 283.75852571487434, 'W': 19.954165262711065, 'J_1KI': 66.43842793605111, 'W_1KI': 4.672012470782268, 'W_D': 4.871165262711063, 'J_D': 69.27048339343077, 'W_D_1KI': 1.1405210167902278, 'J_D_1KI': 0.26703840243273885}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.json
deleted file mode 100644
index dc9a8a8..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4250, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.384530544281006, "TIME_S_1KI": 2.4434189515955307, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 290.7397892761231, "W": 19.124429821644952, "J_1KI": 68.4093621826172, "W_1KI": 4.499865840387048, "W_D": 4.16442982164495, "J_D": 63.309884796142576, "W_D_1KI": 0.9798658403870469, "J_D_1KI": 0.23055666832636398}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.output
deleted file mode 100644
index 031e728..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_095.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_095.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.26108789443969727}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.7556, 0.9846, 0.5564,  ..., 0.2660, 0.6333, 0.6730])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 0.26108789443969727 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4021 -m matrices/as-caida_pruned/as-caida_G_095.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 9.932278633117676}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.9238, 0.9591, 0.3426,  ..., 0.4580, 0.1545, 0.0101])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 9.932278633117676 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4250 -m matrices/as-caida_pruned/as-caida_G_095.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.384530544281006}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2116, 0.2482, 0.7132,  ..., 0.4592, 0.9483, 0.3636])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.384530544281006 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2116, 0.2482, 0.7132,  ..., 0.4592, 0.9483, 0.3636])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.384530544281006 seconds
-
-[16.56, 16.56, 16.6, 16.64, 16.52, 16.64, 16.36, 16.4, 16.68, 16.92]
-[16.92, 16.84, 17.48, 18.12, 18.12, 21.68, 22.84, 23.64, 23.52, 23.32, 20.64, 20.72, 20.56, 20.44, 20.44]
-15.202533721923828
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4250, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.384530544281006, 'TIME_S_1KI': 2.4434189515955307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 290.7397892761231, 'W': 19.124429821644952}
-[16.56, 16.56, 16.6, 16.64, 16.52, 16.64, 16.36, 16.4, 16.68, 16.92, 16.52, 16.64, 16.6, 16.6, 16.64, 16.8, 16.8, 16.6, 16.76, 16.72]
-299.20000000000005
-14.960000000000003
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4250, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.384530544281006, 'TIME_S_1KI': 2.4434189515955307, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 290.7397892761231, 'W': 19.124429821644952, 'J_1KI': 68.4093621826172, 'W_1KI': 4.499865840387048, 'W_D': 4.16442982164495, 'J_D': 63.309884796142576, 'W_D_1KI': 0.9798658403870469, 'J_D_1KI': 0.23055666832636398}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.json
deleted file mode 100644
index 137d00d..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4088, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.275744199752808, "TIME_S_1KI": 2.513636056691, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 289.4017578125, "W": 20.387012119755294, "J_1KI": 70.79299359405577, "W_1KI": 4.987038189764015, "W_D": 5.504012119755293, "J_D": 78.13164445686343, "W_D_1KI": 1.346382612464602, "J_D_1KI": 0.32934995412539186}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.output
deleted file mode 100644
index 7d25df3..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_105.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_105.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.31772923469543457}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.8246, 0.9143, 0.6234,  ..., 0.5880, 0.0932, 0.8821])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 0.31772923469543457 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3304 -m matrices/as-caida_pruned/as-caida_G_105.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 8.484694242477417}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.5893, 0.7585, 0.8994,  ..., 0.5279, 0.1317, 0.1500])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 8.484694242477417 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 4088 -m matrices/as-caida_pruned/as-caida_G_105.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.275744199752808}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.6257, 0.8973, 0.7039,  ..., 0.3870, 0.8920, 0.1294])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.275744199752808 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.6257, 0.8973, 0.7039,  ..., 0.3870, 0.8920, 0.1294])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.275744199752808 seconds
-
-[16.64, 16.6, 16.52, 16.8, 16.76, 16.6, 16.56, 16.48, 16.28, 16.48]
-[16.72, 16.6, 17.92, 17.92, 19.24, 25.56, 26.8, 27.68, 26.8, 26.44, 20.84, 20.4, 20.6, 20.48]
-14.195398330688477
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4088, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.275744199752808, 'TIME_S_1KI': 2.513636056691, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 289.4017578125, 'W': 20.387012119755294}
-[16.64, 16.6, 16.52, 16.8, 16.76, 16.6, 16.56, 16.48, 16.28, 16.48, 16.36, 16.44, 16.44, 16.52, 16.68, 16.8, 16.68, 16.48, 16.2, 16.16]
-297.66
-14.883000000000001
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4088, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.275744199752808, 'TIME_S_1KI': 2.513636056691, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 289.4017578125, 'W': 20.387012119755294, 'J_1KI': 70.79299359405577, 'W_1KI': 4.987038189764015, 'W_D': 5.504012119755293, 'J_D': 78.13164445686343, 'W_D_1KI': 1.346382612464602, 'J_D_1KI': 0.32934995412539186}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.json
deleted file mode 100644
index db98120..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 3741, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.685789585113525, "TIME_S_1KI": 2.8563992475577455, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 251.80008142471314, "W": 19.08240471242988, "J_1KI": 67.30822812742933, "W_1KI": 5.100883376752173, "W_D": 4.222404712429881, "J_D": 55.71634531497957, "W_D_1KI": 1.1286834302138147, "J_D_1KI": 0.3017063432808914}
diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.output
deleted file mode 100644
index 8a8882c..0000000
--- a/pytorch/output_as-caida_maxcore/altra_max_csr_10_10_10_as-caida_G_115.output
+++ /dev/null
@@ -1,68 +0,0 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 100 -m matrices/as-caida_pruned/as-caida_G_115.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.28065943717956543}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.5799, 0.8054, 0.1060,  ..., 0.8848, 0.9407, 0.5317])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 0.28065943717956543 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 3741 -m matrices/as-caida_pruned/as-caida_G_115.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.685789585113525}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.6935, 0.6685, 0.1445,  ..., 0.3962, 0.8482, 0.0100])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.685789585113525 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.6935, 0.6685, 0.1445,  ..., 0.3962, 0.8482, 0.0100])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.685789585113525 seconds
-
-[16.24, 16.12, 15.88, 16.04, 16.12, 16.2, 16.36, 16.44, 16.52, 16.44]
-[16.64, 16.56, 17.16, 17.16, 18.44, 22.28, 23.4, 24.16, 24.24, 23.92, 20.76, 20.88, 20.88]
-13.195406198501587
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3741, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.685789585113525, 'TIME_S_1KI': 2.8563992475577455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 251.80008142471314, 'W': 19.08240471242988}
-[16.24, 16.12, 15.88, 16.04, 16.12, 16.2, 16.36, 16.44, 16.52, 16.44, 16.8, 16.8, 17.04, 16.8, 16.8, 16.64, 16.76, 16.76, 16.84, 16.68]
-297.2
-14.86
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3741, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.685789585113525, 'TIME_S_1KI': 2.8563992475577455, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 251.80008142471314, 'W': 19.08240471242988, 'J_1KI': 67.30822812742933, 'W_1KI': 5.100883376752173, 'W_D': 4.222404712429881, 'J_D': 55.71634531497957, 'W_D_1KI': 1.1286834302138147, 'J_D_1KI': 0.3017063432808914}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..d9869d7
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4703, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.008824825286865, "TIME_S_1KI": 2.1281787848791973, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 826.1763758516313, "W": 65.54, "J_1KI": 175.6700777911187, "W_1KI": 13.935785668722094, "W_D": 29.863750000000003, "J_D": 376.45292560786015, "W_D_1KI": 6.349936210929195, "J_D_1KI": 1.3501884352390376}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..b8adeec
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_010.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.2232527732849121}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.8848, 0.2757, 0.1921,  ..., 0.8912, 0.0855, 0.7985])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.2232527732849121 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4703', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.008824825286865}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.4541, 0.5049, 0.2858,  ..., 0.2191, 0.6676, 0.5763])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.008824825286865 seconds
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.4541, 0.5049, 0.2858,  ..., 0.2191, 0.6676, 0.5763])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.008824825286865 seconds
+
+[40.34, 38.98, 38.95, 38.74, 39.28, 39.15, 39.27, 43.92, 39.21, 38.9]
+[65.54]
+12.605681657791138
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4703, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.008824825286865, 'TIME_S_1KI': 2.1281787848791973, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 826.1763758516313, 'W': 65.54}
+[40.34, 38.98, 38.95, 38.74, 39.28, 39.15, 39.27, 43.92, 39.21, 38.9, 39.42, 38.8, 38.88, 39.13, 44.24, 38.8, 38.81, 39.29, 39.11, 39.27]
+713.5250000000001
+35.67625
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4703, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.008824825286865, 'TIME_S_1KI': 2.1281787848791973, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 826.1763758516313, 'W': 65.54, 'J_1KI': 175.6700777911187, 'W_1KI': 13.935785668722094, 'W_D': 29.863750000000003, 'J_D': 376.45292560786015, 'W_D_1KI': 6.349936210929195, 'J_D_1KI': 1.3501884352390376}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..ababd7f
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_020.json
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..7c32592
--- /dev/null
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..778ee6a
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_030.output
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index 0000000..e425e9c
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..a75089c
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_040.output
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index 0000000..7f53e4e
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+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_040.output
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..af2f102
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_050.json
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..992382c
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_050.output
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..b18e6b9
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_060.json
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..1897035
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_060.output
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+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx']
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..1b79241
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_070.json
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..59de5c8
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_070.output
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+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx']
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..19c3499
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_080.json
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+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3611, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 10.018825054168701, "TIME_S_1KI": 2.774529231284603, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 831.5180498886108, "W": 65.86, "J_1KI": 230.27362223445328, "W_1KI": 18.238715037385766, "W_D": 30.576999999999998, "J_D": 386.05112984275814, "W_D_1KI": 8.467737468845195, "J_D_1KI": 2.3449840678053713}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..8ac058b
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_080.output
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+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.29074978828430176}
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+                       [    0,     1,     2,  ..., 31373, 31373, 31378]]),
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..d69d1e8
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_090.json
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..ac401bb
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+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_090.output
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..1e8a855
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+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_100.json
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..0ce6b93
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+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_100.output
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..d569f8e
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_110.json
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..0e321ad
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_110.output
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+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx']
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..cb35461
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3357, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.2005615234375, "TIME_S_1KI": 3.0385944365318736, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 853.6973136162758, "W": 65.47, "J_1KI": 254.3036382532844, "W_1KI": 19.50253202263926, "W_D": 29.643249999999995, "J_D": 386.53372371858353, "W_D_1KI": 8.830280011915399, "J_D_1KI": 2.6304081060218643}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..573b968
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_coo_10_10_10_as-caida_G_120.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.3127403259277344}
+
+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.1266, 0.8526, 0.4094,  ..., 0.2128, 0.0846, 0.8122])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.3127403259277344 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '3357', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.2005615234375}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.1467, 0.6509, 0.7619,  ..., 0.0273, 0.2393, 0.9346])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.2005615234375 seconds
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.1467, 0.6509, 0.7619,  ..., 0.0273, 0.2393, 0.9346])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 10.2005615234375 seconds
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+[65.47]
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diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.json
deleted file mode 100644
index 4a37c59..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 130157, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.980890989303589, "TIME_S_1KI": 0.08436650344817097, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1360.8988891363142, "W": 103.84999999999998, "J_1KI": 10.455825573240887, "W_1KI": 0.797882557219358, "W_D": 68.19524999999999, "J_D": 893.6623973940609, "W_D_1KI": 0.5239460805027774, "J_D_1KI": 0.004025492908585611}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.output
deleted file mode 100644
index 8a7f427..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_005.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.027045488357543945}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.0726, 0.8672, 0.2478,  ..., 0.8634, 0.5840, 0.1396])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 0.027045488357543945 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '38823', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 3.131915807723999}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.8327, 0.6571, 0.9655,  ..., 0.9197, 0.6003, 0.8237])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 3.131915807723999 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '130157', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.980890989303589}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.5182, 0.8575, 0.3280,  ..., 0.2900, 0.6283, 0.6352])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.980890989303589 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.5182, 0.8575, 0.3280,  ..., 0.2900, 0.6283, 0.6352])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.980890989303589 seconds
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-[40.02, 39.58, 40.96, 39.71, 39.45, 39.25, 39.49, 39.49, 39.68, 39.5]
-[103.85]
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-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130157, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.980890989303589, 'TIME_S_1KI': 0.08436650344817097, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1360.8988891363142, 'W': 103.84999999999998}
-[40.02, 39.58, 40.96, 39.71, 39.45, 39.25, 39.49, 39.49, 39.68, 39.5, 39.99, 40.15, 39.53, 39.12, 39.3, 39.41, 39.19, 39.32, 39.94, 39.54]
-713.095
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-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 130157, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.980890989303589, 'TIME_S_1KI': 0.08436650344817097, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1360.8988891363142, 'W': 103.84999999999998, 'J_1KI': 10.455825573240887, 'W_1KI': 0.797882557219358, 'W_D': 68.19524999999999, 'J_D': 893.6623973940609, 'W_D_1KI': 0.5239460805027774, 'J_D_1KI': 0.004025492908585611}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.json
deleted file mode 100644
index 86d232e..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 127569, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 11.044095754623413, "TIME_S_1KI": 0.08657350731465649, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1376.8041901683807, "W": 104.67999999999999, "J_1KI": 10.792623522708343, "W_1KI": 0.8205755316730553, "W_D": 68.81824999999999, "J_D": 905.1323553692697, "W_D_1KI": 0.539459037854024, "J_D_1KI": 0.004228762770375437}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.output
deleted file mode 100644
index eb5a0a8..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_015.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.024904251098632812}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.5342, 0.2839, 0.0573,  ..., 0.3701, 0.8163, 0.3579])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 0.024904251098632812 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '42161', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 3.470177412033081}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.2040, 0.7311, 0.0868,  ..., 0.2631, 0.7558, 0.0829])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 3.470177412033081 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '127569', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 11.044095754623413}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.8849, 0.5798, 0.8579,  ..., 0.4043, 0.5210, 0.4997])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 11.044095754623413 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.8849, 0.5798, 0.8579,  ..., 0.4043, 0.5210, 0.4997])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 11.044095754623413 seconds
-
-[40.38, 41.29, 40.48, 39.34, 39.37, 39.3, 39.47, 39.55, 40.0, 39.59]
-[104.68]
-13.152504682540894
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 127569, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 11.044095754623413, 'TIME_S_1KI': 0.08657350731465649, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1376.8041901683807, 'W': 104.67999999999999}
-[40.38, 41.29, 40.48, 39.34, 39.37, 39.3, 39.47, 39.55, 40.0, 39.59, 40.5, 39.43, 39.41, 41.1, 40.42, 39.23, 40.12, 39.53, 39.35, 39.22]
-717.235
-35.86175
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 127569, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 11.044095754623413, 'TIME_S_1KI': 0.08657350731465649, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1376.8041901683807, 'W': 104.67999999999999, 'J_1KI': 10.792623522708343, 'W_1KI': 0.8205755316730553, 'W_D': 68.81824999999999, 'J_D': 905.1323553692697, 'W_D_1KI': 0.539459037854024, 'J_D_1KI': 0.004228762770375437}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.json
deleted file mode 100644
index eb322b1..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122117, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.6493821144104, "TIME_S_1KI": 0.0872063849784256, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1405.5062645673752, "W": 105.55, "J_1KI": 11.509505347882564, "W_1KI": 0.8643350229697749, "W_D": 69.588, "J_D": 926.6354328632353, "W_D_1KI": 0.5698469500560938, "J_D_1KI": 0.0046664014842822355}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.output
deleted file mode 100644
index abe0184..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_025.output
+++ /dev/null
@@ -1,105 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.027940988540649414}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.7800, 0.8216, 0.9425,  ..., 0.9095, 0.6124, 0.7757])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 0.027940988540649414 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37579', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 3.4802803993225098}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.1792, 0.6829, 0.8674,  ..., 0.8857, 0.9544, 0.5010])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 3.4802803993225098 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '113375', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 9.748293399810791}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.9918, 0.4166, 0.8271,  ..., 0.2758, 0.1683, 0.2261])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 9.748293399810791 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122117', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.6493821144104}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.2449, 0.0211, 0.8597,  ..., 0.1539, 0.4018, 0.0751])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.6493821144104 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.2449, 0.0211, 0.8597,  ..., 0.1539, 0.4018, 0.0751])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.6493821144104 seconds
-
-[41.05, 40.38, 40.06, 39.47, 40.24, 39.63, 39.65, 39.45, 39.9, 40.39]
-[105.55]
-13.316023349761963
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122117, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.6493821144104, 'TIME_S_1KI': 0.0872063849784256, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1405.5062645673752, 'W': 105.55}
-[41.05, 40.38, 40.06, 39.47, 40.24, 39.63, 39.65, 39.45, 39.9, 40.39, 40.87, 39.32, 39.97, 39.77, 39.91, 40.1, 39.85, 39.52, 41.22, 39.29]
-719.24
-35.962
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122117, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.6493821144104, 'TIME_S_1KI': 0.0872063849784256, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1405.5062645673752, 'W': 105.55, 'J_1KI': 11.509505347882564, 'W_1KI': 0.8643350229697749, 'W_D': 69.588, 'J_D': 926.6354328632353, 'W_D_1KI': 0.5698469500560938, 'J_D_1KI': 0.0046664014842822355}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.json
deleted file mode 100644
index ee00d05..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 119218, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.408023834228516, "TIME_S_1KI": 0.08730245293687627, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1334.6079625511168, "W": 104.82, "J_1KI": 11.194685052182697, "W_1KI": 0.8792296465298864, "W_D": 69.02749999999999, "J_D": 878.884288637638, "W_D_1KI": 0.579002331862638, "J_D_1KI": 0.004856668723369274}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.output
deleted file mode 100644
index c85480e..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_035.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.02867746353149414}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.6240, 0.5141, 0.7011,  ..., 0.5180, 0.0910, 0.3078])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 0.02867746353149414 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36614', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 3.22471284866333}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.8117, 0.1193, 0.9490,  ..., 0.3605, 0.3665, 0.8073])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 3.22471284866333 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '119218', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.408023834228516}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.4188, 0.0252, 0.5651,  ..., 0.9342, 0.6089, 0.7497])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.408023834228516 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.4188, 0.0252, 0.5651,  ..., 0.9342, 0.6089, 0.7497])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.408023834228516 seconds
-
-[40.18, 39.58, 40.1, 39.73, 39.46, 39.77, 39.41, 39.32, 39.48, 39.48]
-[104.82]
-12.732378959655762
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 119218, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.408023834228516, 'TIME_S_1KI': 0.08730245293687627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1334.6079625511168, 'W': 104.82}
-[40.18, 39.58, 40.1, 39.73, 39.46, 39.77, 39.41, 39.32, 39.48, 39.48, 42.99, 39.89, 39.53, 39.6, 39.67, 39.64, 39.89, 39.62, 39.62, 40.43]
-715.85
-35.792500000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 119218, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.408023834228516, 'TIME_S_1KI': 0.08730245293687627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1334.6079625511168, 'W': 104.82, 'J_1KI': 11.194685052182697, 'W_1KI': 0.8792296465298864, 'W_D': 69.02749999999999, 'J_D': 878.884288637638, 'W_D_1KI': 0.579002331862638, 'J_D_1KI': 0.004856668723369274}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.json
deleted file mode 100644
index df8b678..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123262, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.604638814926147, "TIME_S_1KI": 0.08603331776967879, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1498.7498827266693, "W": 104.66, "J_1KI": 12.159058612765241, "W_1KI": 0.8490856873975758, "W_D": 69.0005, "J_D": 988.099477193594, "W_D_1KI": 0.5597872823741299, "J_D_1KI": 0.004541442475167772}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.output
deleted file mode 100644
index 793de8a..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_045.output
+++ /dev/null
@@ -1,105 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.022446870803833008}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.7240, 0.4288, 0.8026,  ..., 0.9422, 0.3141, 0.8470])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 0.022446870803833008 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '46777', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 4.238167762756348}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.6263, 0.4951, 0.8826,  ..., 0.4290, 0.9805, 0.9078])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 4.238167762756348 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '115889', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 9.87186861038208}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.6856, 0.8550, 0.0184,  ..., 0.3333, 0.3446, 0.1554])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 9.87186861038208 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123262', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.604638814926147}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.7577, 0.3700, 0.4727,  ..., 0.6682, 0.7731, 0.8829])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.604638814926147 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.7577, 0.3700, 0.4727,  ..., 0.6682, 0.7731, 0.8829])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.604638814926147 seconds
-
-[40.75, 39.54, 39.62, 39.96, 39.38, 39.41, 39.91, 39.55, 39.67, 39.71]
-[104.66]
-14.320178508758545
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123262, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.604638814926147, 'TIME_S_1KI': 0.08603331776967879, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1498.7498827266693, 'W': 104.66}
-[40.75, 39.54, 39.62, 39.96, 39.38, 39.41, 39.91, 39.55, 39.67, 39.71, 39.94, 39.22, 39.33, 39.47, 39.36, 39.85, 40.29, 39.23, 39.64, 39.12]
-713.1899999999999
-35.659499999999994
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123262, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.604638814926147, 'TIME_S_1KI': 0.08603331776967879, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1498.7498827266693, 'W': 104.66, 'J_1KI': 12.159058612765241, 'W_1KI': 0.8490856873975758, 'W_D': 69.0005, 'J_D': 988.099477193594, 'W_D_1KI': 0.5597872823741299, 'J_D_1KI': 0.004541442475167772}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.json
deleted file mode 100644
index d19ae4f..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 115993, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 11.341620922088623, "TIME_S_1KI": 0.0977784945823336, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1311.9969903469087, "W": 105.11000000000001, "J_1KI": 11.311001442732826, "W_1KI": 0.906175372651798, "W_D": 68.70625000000001, "J_D": 857.6005443632604, "W_D_1KI": 0.5923310027329237, "J_D_1KI": 0.005106609905191897}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.output
deleted file mode 100644
index 9ccce60..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_055.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.028304576873779297}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.7892, 0.3566, 0.7127,  ..., 0.2432, 0.3071, 0.5377])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 0.028304576873779297 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37096', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 3.358018398284912}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.1850, 0.6800, 0.5942,  ..., 0.9886, 0.1400, 0.4140])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 3.358018398284912 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '115993', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 11.341620922088623}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.2569, 0.5049, 0.9878,  ..., 0.1193, 0.2877, 0.6010])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 11.341620922088623 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.2569, 0.5049, 0.9878,  ..., 0.1193, 0.2877, 0.6010])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 11.341620922088623 seconds
-
-[40.22, 39.34, 39.44, 39.29, 39.78, 39.4, 39.25, 39.27, 39.2, 39.57]
-[105.11]
-12.482132911682129
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 115993, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 11.341620922088623, 'TIME_S_1KI': 0.0977784945823336, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1311.9969903469087, 'W': 105.11000000000001}
-[40.22, 39.34, 39.44, 39.29, 39.78, 39.4, 39.25, 39.27, 39.2, 39.57, 40.43, 40.81, 39.88, 39.99, 39.29, 39.57, 39.3, 49.45, 45.11, 39.19]
-728.075
-36.40375
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 115993, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 11.341620922088623, 'TIME_S_1KI': 0.0977784945823336, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1311.9969903469087, 'W': 105.11000000000001, 'J_1KI': 11.311001442732826, 'W_1KI': 0.906175372651798, 'W_D': 68.70625000000001, 'J_D': 857.6005443632604, 'W_D_1KI': 0.5923310027329237, 'J_D_1KI': 0.005106609905191897}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.json
deleted file mode 100644
index 02301d3..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 119305, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.269909381866455, "TIME_S_1KI": 0.08608113140158799, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1369.542292098999, "W": 104.99, "J_1KI": 11.479336927194996, "W_1KI": 0.8800134110054063, "W_D": 69.32875, "J_D": 904.3590359401703, "W_D_1KI": 0.5811051506642639, "J_D_1KI": 0.004870752698246209}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.output
deleted file mode 100644
index a82eb47..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_065.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.05011296272277832}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.1460, 0.2426, 0.2871,  ..., 0.8209, 0.8175, 0.1827])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 0.05011296272277832 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '20952', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 1.843977928161621}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.4486, 0.9899, 0.6798,  ..., 0.6606, 0.4190, 0.7337])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 1.843977928161621 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '119305', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.269909381866455}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.1419, 0.9123, 0.3177,  ..., 0.8018, 0.7036, 0.8347])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.269909381866455 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.1419, 0.9123, 0.3177,  ..., 0.8018, 0.7036, 0.8347])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.269909381866455 seconds
-
-[40.89, 39.97, 39.48, 39.83, 39.9, 39.69, 39.45, 39.42, 39.72, 39.16]
-[104.99]
-13.044502258300781
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 119305, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.269909381866455, 'TIME_S_1KI': 0.08608113140158799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1369.542292098999, 'W': 104.99}
-[40.89, 39.97, 39.48, 39.83, 39.9, 39.69, 39.45, 39.42, 39.72, 39.16, 39.96, 39.46, 39.25, 39.6, 39.18, 39.57, 39.73, 39.28, 40.1, 39.18]
-713.2249999999999
-35.661249999999995
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 119305, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.269909381866455, 'TIME_S_1KI': 0.08608113140158799, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1369.542292098999, 'W': 104.99, 'J_1KI': 11.479336927194996, 'W_1KI': 0.8800134110054063, 'W_D': 69.32875, 'J_D': 904.3590359401703, 'W_D_1KI': 0.5811051506642639, 'J_D_1KI': 0.004870752698246209}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.json
deleted file mode 100644
index 52bd678..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 117871, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.46876573562622, "TIME_S_1KI": 0.08881544854651459, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1384.9325737524032, "W": 105.46, "J_1KI": 11.749561586415686, "W_1KI": 0.8947069253675628, "W_D": 69.29524999999998, "J_D": 910.0061533407567, "W_D_1KI": 0.5878905752899355, "J_D_1KI": 0.004987576038974264}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.output
deleted file mode 100644
index edf1a35..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_075.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.029708385467529297}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.7657, 0.6949, 0.8038,  ..., 0.0810, 0.5586, 0.1756])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 0.029708385467529297 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '35343', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 3.1483521461486816}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.7560, 0.3404, 0.1077,  ..., 0.6219, 0.8552, 0.3773])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 3.1483521461486816 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '117871', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.46876573562622}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.9284, 0.7225, 0.5215,  ..., 0.9584, 0.4959, 0.3353])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.46876573562622 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.9284, 0.7225, 0.5215,  ..., 0.9584, 0.4959, 0.3353])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.46876573562622 seconds
-
-[40.06, 40.12, 39.51, 39.49, 39.39, 39.9, 39.43, 39.81, 44.85, 39.81]
-[105.46]
-13.132302045822144
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117871, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.46876573562622, 'TIME_S_1KI': 0.08881544854651459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.9325737524032, 'W': 105.46}
-[40.06, 40.12, 39.51, 39.49, 39.39, 39.9, 39.43, 39.81, 44.85, 39.81, 40.59, 39.7, 39.35, 39.63, 39.73, 44.15, 39.7, 39.24, 39.48, 39.17]
-723.2950000000001
-36.164750000000005
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 117871, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.46876573562622, 'TIME_S_1KI': 0.08881544854651459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1384.9325737524032, 'W': 105.46, 'J_1KI': 11.749561586415686, 'W_1KI': 0.8947069253675628, 'W_D': 69.29524999999998, 'J_D': 910.0061533407567, 'W_D_1KI': 0.5878905752899355, 'J_D_1KI': 0.004987576038974264}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.json
deleted file mode 100644
index dac1835..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 121444, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.505080699920654, "TIME_S_1KI": 0.08650143852245194, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1336.2899490594864, "W": 106.5, "J_1KI": 11.003342685184005, "W_1KI": 0.8769473996245183, "W_D": 70.737, "J_D": 887.5600199682711, "W_D_1KI": 0.5824659925562399, "J_D_1KI": 0.004796169366590691}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.output
deleted file mode 100644
index c674a30..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_085.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.027776479721069336}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.7671, 0.5798, 0.8911,  ..., 0.4919, 0.5874, 0.4864])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 0.027776479721069336 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37801', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 3.268259286880493}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.7766, 0.5291, 0.5743,  ..., 0.9245, 0.7238, 0.3581])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 3.268259286880493 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '121444', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.505080699920654}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.5279, 0.9609, 0.7308,  ..., 0.9106, 0.1679, 0.0523])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.505080699920654 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.5279, 0.9609, 0.7308,  ..., 0.9106, 0.1679, 0.0523])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.505080699920654 seconds
-
-[40.27, 40.61, 39.36, 39.54, 39.41, 39.69, 39.72, 39.77, 40.14, 39.67]
-[106.5]
-12.54732346534729
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121444, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.505080699920654, 'TIME_S_1KI': 0.08650143852245194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1336.2899490594864, 'W': 106.5}
-[40.27, 40.61, 39.36, 39.54, 39.41, 39.69, 39.72, 39.77, 40.14, 39.67, 41.71, 39.67, 39.95, 39.27, 39.45, 39.23, 39.37, 40.22, 39.21, 39.65]
-715.26
-35.763
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 121444, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.505080699920654, 'TIME_S_1KI': 0.08650143852245194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1336.2899490594864, 'W': 106.5, 'J_1KI': 11.003342685184005, 'W_1KI': 0.8769473996245183, 'W_D': 70.737, 'J_D': 887.5600199682711, 'W_D_1KI': 0.5824659925562399, 'J_D_1KI': 0.004796169366590691}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.json
deleted file mode 100644
index 336d888..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 123705, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.90046238899231, "TIME_S_1KI": 0.08811658695276917, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1428.9193895983694, "W": 106.12999999999998, "J_1KI": 11.5510237225526, "W_1KI": 0.85792813548361, "W_D": 69.78774999999999, "J_D": 939.6124482374786, "W_D_1KI": 0.5641465583444485, "J_D_1KI": 0.00456041840139403}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.output
deleted file mode 100644
index ea67830..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_095.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.0280454158782959}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.6080, 0.8018, 0.6987,  ..., 0.3141, 0.8100, 0.8933])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 0.0280454158782959 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '37439', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 3.1777875423431396}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.8331, 0.2134, 0.1814,  ..., 0.8302, 0.3682, 0.5485])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 3.1777875423431396 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '123705', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.90046238899231}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.6564, 0.4288, 0.1493,  ..., 0.5365, 0.7866, 0.0555])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.90046238899231 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.6564, 0.4288, 0.1493,  ..., 0.5365, 0.7866, 0.0555])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.90046238899231 seconds
-
-[45.18, 51.54, 39.34, 39.19, 39.19, 39.24, 39.49, 39.13, 40.19, 40.24]
-[106.13]
-13.46385931968689
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123705, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.90046238899231, 'TIME_S_1KI': 0.08811658695276917, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1428.9193895983694, 'W': 106.12999999999998}
-[45.18, 51.54, 39.34, 39.19, 39.19, 39.24, 39.49, 39.13, 40.19, 40.24, 39.83, 39.8, 39.94, 39.19, 39.68, 40.58, 39.51, 39.26, 39.27, 39.36]
-726.845
-36.34225
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 123705, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.90046238899231, 'TIME_S_1KI': 0.08811658695276917, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1428.9193895983694, 'W': 106.12999999999998, 'J_1KI': 11.5510237225526, 'W_1KI': 0.85792813548361, 'W_D': 69.78774999999999, 'J_D': 939.6124482374786, 'W_D_1KI': 0.5641465583444485, 'J_D_1KI': 0.00456041840139403}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.json
deleted file mode 100644
index 7178369..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 122486, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.397178411483765, "TIME_S_1KI": 0.08488462690824881, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1412.024871377945, "W": 106.03999999999999, "J_1KI": 11.528051135459929, "W_1KI": 0.8657315938148032, "W_D": 70.02574999999999, "J_D": 932.4603983109591, "W_D_1KI": 0.5717041131231323, "J_D_1KI": 0.004667505781257714}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.output
deleted file mode 100644
index 4c2c51c..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_105.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.028494596481323242}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.0686, 0.0328, 0.2033,  ..., 0.1197, 0.3793, 0.1423])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 0.028494596481323242 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36849', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 3.158832311630249}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.1470, 0.7207, 0.2182,  ..., 0.8892, 0.7915, 0.0167])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 3.158832311630249 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '122486', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.397178411483765}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.7336, 0.8024, 0.7188,  ..., 0.1561, 0.7889, 0.4305])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.397178411483765 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.7336, 0.8024, 0.7188,  ..., 0.1561, 0.7889, 0.4305])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.397178411483765 seconds
-
-[50.54, 39.93, 39.56, 39.45, 39.38, 39.37, 39.74, 39.95, 39.8, 41.73]
-[106.04]
-13.315964460372925
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122486, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.397178411483765, 'TIME_S_1KI': 0.08488462690824881, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1412.024871377945, 'W': 106.03999999999999}
-[50.54, 39.93, 39.56, 39.45, 39.38, 39.37, 39.74, 39.95, 39.8, 41.73, 39.97, 39.3, 39.55, 39.68, 39.55, 39.47, 40.65, 39.24, 39.69, 39.71]
-720.2850000000001
-36.014250000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 122486, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.397178411483765, 'TIME_S_1KI': 0.08488462690824881, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1412.024871377945, 'W': 106.03999999999999, 'J_1KI': 11.528051135459929, 'W_1KI': 0.8657315938148032, 'W_D': 70.02574999999999, 'J_D': 932.4603983109591, 'W_D_1KI': 0.5717041131231323, 'J_D_1KI': 0.004667505781257714}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.json
deleted file mode 100644
index a790206..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 124105, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.59231972694397, "TIME_S_1KI": 0.08534966139111212, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4742.8229215764995, "W": 99.33, "J_1KI": 38.21621144656943, "W_1KI": 0.8003706538817936, "W_D": 61.29225, "J_D": 2926.5910421322587, "W_D_1KI": 0.49387413883405185, "J_D_1KI": 0.003979486232094209}
diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.output
deleted file mode 100644
index c33a124..0000000
--- a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_10_10_10_as-caida_G_115.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.02862715721130371}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.8751, 0.4686, 0.5237,  ..., 0.8724, 0.2861, 0.0906])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 0.02862715721130371 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '36678', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 3.1031546592712402}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.7984, 0.5145, 0.9517,  ..., 0.9171, 0.3029, 0.5667])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 3.1031546592712402 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '124105', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.59231972694397}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.9062, 0.9528, 0.4977,  ..., 0.5925, 0.1787, 0.9051])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.59231972694397 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.9062, 0.9528, 0.4977,  ..., 0.5925, 0.1787, 0.9051])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.59231972694397 seconds
-
-[40.47, 39.18, 39.36, 39.46, 39.2, 39.2, 39.25, 49.28, 64.87, 63.39]
-[99.33]
-47.74814176559448
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 124105, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.59231972694397, 'TIME_S_1KI': 0.08534966139111212, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4742.8229215764995, 'W': 99.33}
-[40.47, 39.18, 39.36, 39.46, 39.2, 39.2, 39.25, 49.28, 64.87, 63.39, 40.46, 39.73, 40.14, 40.06, 39.55, 39.41, 40.08, 39.85, 40.03, 39.89]
-760.7549999999999
-38.037749999999996
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 124105, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.59231972694397, 'TIME_S_1KI': 0.08534966139111212, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4742.8229215764995, 'W': 99.33, 'J_1KI': 38.21621144656943, 'W_1KI': 0.8003706538817936, 'W_D': 61.29225, 'J_D': 2926.5910421322587, 'W_D_1KI': 0.49387413883405185, 'J_D_1KI': 0.003979486232094209}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_010.json
new file mode 100644
index 0000000..4889c6d
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 3789, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.054757833480835, "TIME_S_1KI": 2.6536705815468022, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 730.6265703058242, "W": 54.23, "J_1KI": 192.8283373728752, "W_1KI": 14.312483504882554, "W_D": 37.41225, "J_D": 504.04543435227873, "W_D_1KI": 9.873911322248613, "J_D_1KI": 2.6059412304694147}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_010.output
new file mode 100644
index 0000000..7647f48
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_010.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.2771122455596924}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.0036, 0.3684, 0.4760,  ..., 0.9250, 0.8163, 0.0588])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 0.2771122455596924 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '3789', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.054757833480835}
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.7678, 0.6724, 0.7645,  ..., 0.9086, 0.3480, 0.4542])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.054757833480835 seconds
+
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.7678, 0.6724, 0.7645,  ..., 0.9086, 0.3480, 0.4542])
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_020.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_020.json
new file mode 100644
index 0000000..fd3084a
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_020.json
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_020.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_020.output
new file mode 100644
index 0000000..3c93c2b
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+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_020.output
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_030.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_030.json
new file mode 100644
index 0000000..0124c4c
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_030.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_030.output
new file mode 100644
index 0000000..cbb7d45
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+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_030.output
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_040.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_040.json
new file mode 100644
index 0000000..631489b
--- /dev/null
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_040.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_040.output
new file mode 100644
index 0000000..e94af54
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_040.output
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_050.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_050.json
new file mode 100644
index 0000000..2a8c27d
--- /dev/null
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_050.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_050.output
new file mode 100644
index 0000000..72165a4
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+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_050.output
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_060.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_060.json
new file mode 100644
index 0000000..3125bf9
--- /dev/null
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_060.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_060.output
new file mode 100644
index 0000000..f90405d
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+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_060.output
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_070.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_070.json
new file mode 100644
index 0000000..5229b46
--- /dev/null
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_070.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_070.output
new file mode 100644
index 0000000..871f570
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_070.output
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_080.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_080.json
new file mode 100644
index 0000000..5143c4f
--- /dev/null
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_080.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_080.output
new file mode 100644
index 0000000..b2ef0b6
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_080.output
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_090.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_090.json
new file mode 100644
index 0000000..c681870
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_090.json
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_090.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_090.output
new file mode 100644
index 0000000..2fa8743
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_090.output
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_100.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_100.json
new file mode 100644
index 0000000..a39f480
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_100.json
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_100.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_100.output
new file mode 100644
index 0000000..593a45d
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_100.output
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_110.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_110.json
new file mode 100644
index 0000000..591e509
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_110.json
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_110.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_110.output
new file mode 100644
index 0000000..0b927e6
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_110.output
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+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.3818802833557129}
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_120.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_120.json
new file mode 100644
index 0000000..01779a0
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_120.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2697, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.159295082092285, "TIME_S_1KI": 3.7668873126037394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.4488546848297, "W": 53.9, "J_1KI": 271.949890502347, "W_1KI": 19.985168705969595, "W_D": 36.5805, "J_D": 497.7722788274288, "W_D_1KI": 13.563403781979977, "J_D_1KI": 5.0290707385910185}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_120.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_120.output
new file mode 100644
index 0000000..c6b075b
--- /dev/null
+++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_coo_10_10_10_as-caida_G_120.output
@@ -0,0 +1,59 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.3892490863800049}
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+tensor(indices=tensor([[  106,   329,  1040,  ...,   155,   160, 12170],
+                       [    2,     2,     2,  ..., 31355, 31360, 31378]]),
+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=106510, layout=torch.sparse_coo)
+tensor([0.2262, 0.4017, 0.5915,  ..., 0.3746, 0.6358, 0.6857])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_120
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 106510
+Density: 0.0001081713341839054
+Time: 0.3892490863800049 seconds
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'coo', '2697', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 10.159295082092285}
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+NNZ: 106510
+Density: 0.0001081713341839054
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+       values=tensor([3., 3., 3.,  ..., 3., 3., 3.]),
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diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.json
deleted file mode 100644
index cb8197c..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 114732, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.352796077728271, "TIME_S_1KI": 0.09023459956880618, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1152.0286125135422, "W": 84.58, "J_1KI": 10.04104009791115, "W_1KI": 0.7371962486490254, "W_D": 67.46775, "J_D": 918.9498512876629, "W_D_1KI": 0.5880464909528291, "J_D_1KI": 0.005125392139532381}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.output
deleted file mode 100644
index 5c696d9..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_005.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 0.022900104522705078}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.6590, 0.2806, 0.2495,  ..., 0.2832, 0.5182, 0.5123])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 0.022900104522705078 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '45851', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 4.196166276931763}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
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-tensor([0.0852, 0.1228, 0.9411,  ..., 0.5747, 0.5628, 0.7446])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 4.196166276931763 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '114732', '-m', 'matrices/as-caida_pruned/as-caida_G_005.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_005", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 70026, "MATRIX_DENSITY": 7.111825976492498e-05, "TIME_S": 10.352796077728271}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
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-tensor([0.5031, 0.9770, 0.1362,  ..., 0.3615, 0.8578, 0.7650])
-Matrix Type: SuiteSparse
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-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.352796077728271 seconds
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-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    63,    63,  ..., 70025, 70025, 70026]),
-       col_indices=tensor([  111,   761,   822,  ...,   978,   978, 12170]),
-       values=tensor([4., 3., 3.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=70026, layout=torch.sparse_csr)
-tensor([0.5031, 0.9770, 0.1362,  ..., 0.3615, 0.8578, 0.7650])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_005
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 70026
-Density: 7.111825976492498e-05
-Time: 10.352796077728271 seconds
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-[19.42, 18.64, 18.8, 18.86, 18.73, 18.71, 18.65, 18.39, 18.57, 18.62]
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-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 114732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.352796077728271, 'TIME_S_1KI': 0.09023459956880618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1152.0286125135422, 'W': 84.58}
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-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 114732, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_005', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 70026, 'MATRIX_DENSITY': 7.111825976492498e-05, 'TIME_S': 10.352796077728271, 'TIME_S_1KI': 0.09023459956880618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1152.0286125135422, 'W': 84.58, 'J_1KI': 10.04104009791115, 'W_1KI': 0.7371962486490254, 'W_D': 67.46775, 'J_D': 918.9498512876629, 'W_D_1KI': 0.5880464909528291, 'J_D_1KI': 0.005125392139532381}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.json
deleted file mode 100644
index 9a89d63..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 107283, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.024356126785278, "TIME_S_1KI": 0.09343843970419617, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1167.401763653755, "W": 84.69, "J_1KI": 10.881516770166336, "W_1KI": 0.7894074550487962, "W_D": 67.7505, "J_D": 933.9007343065739, "W_D_1KI": 0.6315119823271163, "J_D_1KI": 0.0058864124076239135}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.output
deleted file mode 100644
index 8223898..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_015.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 0.024006128311157227}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.9343, 0.0575, 0.7588,  ..., 0.4632, 0.2744, 0.8237])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 0.024006128311157227 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43738', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 4.2806923389434814}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.8682, 0.4247, 0.3572,  ..., 0.4764, 0.2418, 0.9826])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 4.2806923389434814 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '107283', '-m', 'matrices/as-caida_pruned/as-caida_G_015.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_015", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 77124, "MATRIX_DENSITY": 7.832697378273889e-05, "TIME_S": 10.024356126785278}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.4742, 0.5931, 0.8061,  ..., 0.3283, 0.2105, 0.2866])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.024356126785278 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     4,  ..., 77124, 77124, 77124]),
-       col_indices=tensor([1040, 2054, 4842,  ...,  160,  160, 8230]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=77124, layout=torch.sparse_csr)
-tensor([0.4742, 0.5931, 0.8061,  ..., 0.3283, 0.2105, 0.2866])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_015
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 77124
-Density: 7.832697378273889e-05
-Time: 10.024356126785278 seconds
-
-[19.22, 18.5, 18.53, 20.77, 19.04, 18.5, 18.59, 19.04, 18.95, 18.38]
-[84.69]
-13.784410953521729
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 107283, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.024356126785278, 'TIME_S_1KI': 0.09343843970419617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1167.401763653755, 'W': 84.69}
-[19.22, 18.5, 18.53, 20.77, 19.04, 18.5, 18.59, 19.04, 18.95, 18.38, 19.57, 18.55, 19.15, 18.4, 18.43, 18.65, 18.7, 18.52, 18.6, 18.57]
-338.78999999999996
-16.9395
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 107283, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_015', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 77124, 'MATRIX_DENSITY': 7.832697378273889e-05, 'TIME_S': 10.024356126785278, 'TIME_S_1KI': 0.09343843970419617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1167.401763653755, 'W': 84.69, 'J_1KI': 10.881516770166336, 'W_1KI': 0.7894074550487962, 'W_D': 67.7505, 'J_D': 933.9007343065739, 'W_D_1KI': 0.6315119823271163, 'J_D_1KI': 0.0058864124076239135}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.json
deleted file mode 100644
index ac04a12..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 109001, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.287229776382446, "TIME_S_1KI": 0.09437738898159143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1204.7143850159644, "W": 85.57, "J_1KI": 11.052324153135883, "W_1KI": 0.7850386693700058, "W_D": 68.3185, "J_D": 961.8356867209673, "W_D_1KI": 0.6267694791790901, "J_D_1KI": 0.005750125954615922}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.output
deleted file mode 100644
index c5dbd7f..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_025.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 0.023378610610961914}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.2787, 0.0070, 0.8156,  ..., 0.2081, 0.1934, 0.2181])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 0.023378610610961914 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44912', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 4.326327562332153}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.1269, 0.3329, 0.3331,  ..., 0.2645, 0.1870, 0.6544])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 4.326327562332153 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '109001', '-m', 'matrices/as-caida_pruned/as-caida_G_025.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_025", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 85850, "MATRIX_DENSITY": 8.718908121010495e-05, "TIME_S": 10.287229776382446}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.2367, 0.6225, 0.7020,  ..., 0.6877, 0.6724, 0.8482])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.287229776382446 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ..., 85845, 85847, 85850]),
-       col_indices=tensor([  346, 13811, 21783,  ..., 15310, 17998, 31377]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 3.]), size=(31379, 31379),
-       nnz=85850, layout=torch.sparse_csr)
-tensor([0.2367, 0.6225, 0.7020,  ..., 0.6877, 0.6724, 0.8482])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_025
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 85850
-Density: 8.718908121010495e-05
-Time: 10.287229776382446 seconds
-
-[19.11, 18.88, 18.79, 18.69, 18.99, 18.57, 22.71, 18.57, 18.78, 18.52]
-[85.57]
-14.078700304031372
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109001, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.287229776382446, 'TIME_S_1KI': 0.09437738898159143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1204.7143850159644, 'W': 85.57}
-[19.11, 18.88, 18.79, 18.69, 18.99, 18.57, 22.71, 18.57, 18.78, 18.52, 19.61, 18.91, 22.51, 18.3, 18.93, 18.54, 18.82, 18.5, 18.7, 18.44]
-345.03
-17.2515
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 109001, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_025', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 85850, 'MATRIX_DENSITY': 8.718908121010495e-05, 'TIME_S': 10.287229776382446, 'TIME_S_1KI': 0.09437738898159143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1204.7143850159644, 'W': 85.57, 'J_1KI': 11.052324153135883, 'W_1KI': 0.7850386693700058, 'W_D': 68.3185, 'J_D': 961.8356867209673, 'W_D_1KI': 0.6267694791790901, 'J_D_1KI': 0.005750125954615922}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.json
deleted file mode 100644
index 5edced6..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103439, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.351110458374023, "TIME_S_1KI": 0.10006970734804109, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1118.8645426464082, "W": 85.18, "J_1KI": 10.81666047280434, "W_1KI": 0.8234805054186526, "W_D": 67.80525, "J_D": 890.64205248034, "W_D_1KI": 0.6555095273542861, "J_D_1KI": 0.006337160329801005}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.output
deleted file mode 100644
index 6408866..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_035.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 0.02468276023864746}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.4003, 0.8236, 0.0269,  ..., 0.2308, 0.8303, 0.1459])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 0.02468276023864746 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42539', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 4.318082571029663}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.2117, 0.5564, 0.7291,  ..., 0.0848, 0.9734, 0.5692])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 4.318082571029663 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103439', '-m', 'matrices/as-caida_pruned/as-caida_G_035.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_035", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 87560, "MATRIX_DENSITY": 8.892575364888514e-05, "TIME_S": 10.351110458374023}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.7311, 0.5094, 0.4269,  ..., 0.7263, 0.3700, 0.5742])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.351110458374023 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 87559, 87559, 87560]),
-       col_indices=tensor([ 1809, 21783,   106,  ..., 10144,   882, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=87560, layout=torch.sparse_csr)
-tensor([0.7311, 0.5094, 0.4269,  ..., 0.7263, 0.3700, 0.5742])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_035
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 87560
-Density: 8.892575364888514e-05
-Time: 10.351110458374023 seconds
-
-[19.14, 18.98, 18.66, 18.56, 18.83, 18.87, 22.69, 18.77, 18.81, 18.9]
-[85.18]
-13.13529634475708
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103439, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.351110458374023, 'TIME_S_1KI': 0.10006970734804109, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1118.8645426464082, 'W': 85.18}
-[19.14, 18.98, 18.66, 18.56, 18.83, 18.87, 22.69, 18.77, 18.81, 18.9, 19.57, 18.61, 18.66, 22.69, 19.25, 19.17, 18.61, 18.71, 19.31, 19.02]
-347.49499999999995
-17.37475
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103439, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_035', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 87560, 'MATRIX_DENSITY': 8.892575364888514e-05, 'TIME_S': 10.351110458374023, 'TIME_S_1KI': 0.10006970734804109, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1118.8645426464082, 'W': 85.18, 'J_1KI': 10.81666047280434, 'W_1KI': 0.8234805054186526, 'W_D': 67.80525, 'J_D': 890.64205248034, 'W_D_1KI': 0.6555095273542861, 'J_D_1KI': 0.006337160329801005}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.json
deleted file mode 100644
index cee2dd2..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104342, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.794602870941162, "TIME_S_1KI": 0.10345405369785093, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1213.257156124115, "W": 85.72, "J_1KI": 11.627696959269661, "W_1KI": 0.8215292020471142, "W_D": 68.65825, "J_D": 971.7698686357736, "W_D_1KI": 0.6580116348162772, "J_D_1KI": 0.006306296935234874}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.output
deleted file mode 100644
index 8f96d81..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_045.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 0.024647951126098633}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.2181, 0.0875, 0.6140,  ..., 0.7443, 0.8424, 0.1920])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 0.024647951126098633 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42599', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 4.286748647689819}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.9399, 0.2522, 0.4169,  ..., 0.3599, 0.7670, 0.0171])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 4.286748647689819 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104342', '-m', 'matrices/as-caida_pruned/as-caida_G_045.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_045", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89152, "MATRIX_DENSITY": 9.054258553341032e-05, "TIME_S": 10.794602870941162}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.4314, 0.9415, 0.5407,  ..., 0.5093, 0.0658, 0.7203])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.794602870941162 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 89150, 89150, 89152]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,  2232, 16085]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=89152, layout=torch.sparse_csr)
-tensor([0.4314, 0.9415, 0.5407,  ..., 0.5093, 0.0658, 0.7203])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_045
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 89152
-Density: 9.054258553341032e-05
-Time: 10.794602870941162 seconds
-
-[19.11, 18.76, 19.16, 18.8, 18.66, 18.43, 18.88, 18.35, 18.59, 18.42]
-[85.72]
-14.153723239898682
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104342, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.794602870941162, 'TIME_S_1KI': 0.10345405369785093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1213.257156124115, 'W': 85.72}
-[19.11, 18.76, 19.16, 18.8, 18.66, 18.43, 18.88, 18.35, 18.59, 18.42, 19.33, 19.07, 18.75, 18.46, 18.8, 18.47, 18.71, 22.15, 19.49, 18.55]
-341.235
-17.06175
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104342, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_045', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89152, 'MATRIX_DENSITY': 9.054258553341032e-05, 'TIME_S': 10.794602870941162, 'TIME_S_1KI': 0.10345405369785093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1213.257156124115, 'W': 85.72, 'J_1KI': 11.627696959269661, 'W_1KI': 0.8215292020471142, 'W_D': 68.65825, 'J_D': 971.7698686357736, 'W_D_1KI': 0.6580116348162772, 'J_D_1KI': 0.006306296935234874}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.json
deleted file mode 100644
index 16ff754..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 108819, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.897866487503052, "TIME_S_1KI": 0.10014672518129235, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1284.6524841690064, "W": 86.48, "J_1KI": 11.805406079535802, "W_1KI": 0.7947141583730782, "W_D": 69.0845, "J_D": 1026.2439239428045, "W_D_1KI": 0.6348569643168932, "J_D_1KI": 0.005834063576368954}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.output
deleted file mode 100644
index 1351c73..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_055.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 0.023081541061401367}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.2528, 0.0970, 0.7933,  ..., 0.5516, 0.4644, 0.6686])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 0.023081541061401367 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '45490', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 4.389326810836792}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.3779, 0.3653, 0.4471,  ..., 0.3867, 0.0629, 0.2299])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 4.389326810836792 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '108819', '-m', 'matrices/as-caida_pruned/as-caida_G_055.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_055", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 91476, "MATRIX_DENSITY": 9.290283509348351e-05, "TIME_S": 10.897866487503052}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.8117, 0.8203, 0.5161,  ..., 0.5522, 0.8518, 0.2789])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.897866487503052 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 91475, 91475, 91476]),
-       col_indices=tensor([21783,   106,   329,  ...,   160,   882, 17255]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=91476, layout=torch.sparse_csr)
-tensor([0.8117, 0.8203, 0.5161,  ..., 0.5522, 0.8518, 0.2789])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_055
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 91476
-Density: 9.290283509348351e-05
-Time: 10.897866487503052 seconds
-
-[19.82, 19.58, 22.76, 18.73, 18.98, 18.92, 19.19, 18.75, 18.85, 18.86]
-[86.48]
-14.854908466339111
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 108819, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.897866487503052, 'TIME_S_1KI': 0.10014672518129235, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1284.6524841690064, 'W': 86.48}
-[19.82, 19.58, 22.76, 18.73, 18.98, 18.92, 19.19, 18.75, 18.85, 18.86, 19.16, 18.57, 19.11, 18.51, 18.92, 18.66, 20.05, 18.75, 19.25, 22.82]
-347.91
-17.395500000000002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 108819, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_055', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 91476, 'MATRIX_DENSITY': 9.290283509348351e-05, 'TIME_S': 10.897866487503052, 'TIME_S_1KI': 0.10014672518129235, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1284.6524841690064, 'W': 86.48, 'J_1KI': 11.805406079535802, 'W_1KI': 0.7947141583730782, 'W_D': 69.0845, 'J_D': 1026.2439239428045, 'W_D_1KI': 0.6348569643168932, 'J_D_1KI': 0.005834063576368954}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.json
deleted file mode 100644
index 322da9f..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 101100, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.192413091659546, "TIME_S_1KI": 0.10081516411137038, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1177.807462463379, "W": 86.32, "J_1KI": 11.649925444741632, "W_1KI": 0.8538081107814045, "W_D": 56.87149999999999, "J_D": 775.9925521488188, "W_D_1KI": 0.5625272007912957, "J_D_1KI": 0.005564067267965339}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.output
deleted file mode 100644
index 1d544ec..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_065.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 0.023540258407592773}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.6962, 0.9656, 0.9232,  ..., 0.4983, 0.2135, 0.8824])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 0.023540258407592773 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44604', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 4.632462501525879}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.2472, 0.8203, 0.9803,  ..., 0.4259, 0.4111, 0.8965])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 4.632462501525879 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '101100', '-m', 'matrices/as-caida_pruned/as-caida_G_065.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_065", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 95068, "MATRIX_DENSITY": 9.655086281283934e-05, "TIME_S": 10.192413091659546}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.2591, 0.4101, 0.9221,  ..., 0.4342, 0.1148, 0.3704])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.192413091659546 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 95068, 95068, 95068]),
-       col_indices=tensor([ 106,  329, 1040,  ...,  155,  160,  882]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=95068, layout=torch.sparse_csr)
-tensor([0.2591, 0.4101, 0.9221,  ..., 0.4342, 0.1148, 0.3704])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_065
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 95068
-Density: 9.655086281283934e-05
-Time: 10.192413091659546 seconds
-
-[43.11, 45.01, 42.05, 51.61, 51.92, 51.75, 47.55, 42.25, 42.99, 42.64]
-[86.32]
-13.644664764404297
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 101100, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.192413091659546, 'TIME_S_1KI': 0.10081516411137038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1177.807462463379, 'W': 86.32}
-[43.11, 45.01, 42.05, 51.61, 51.92, 51.75, 47.55, 42.25, 42.99, 42.64, 19.89, 18.77, 19.55, 18.49, 19.73, 18.5, 18.75, 18.62, 19.33, 18.56]
-588.97
-29.448500000000003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 101100, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_065', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 95068, 'MATRIX_DENSITY': 9.655086281283934e-05, 'TIME_S': 10.192413091659546, 'TIME_S_1KI': 0.10081516411137038, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1177.807462463379, 'W': 86.32, 'J_1KI': 11.649925444741632, 'W_1KI': 0.8538081107814045, 'W_D': 56.87149999999999, 'J_D': 775.9925521488188, 'W_D_1KI': 0.5625272007912957, 'J_D_1KI': 0.005564067267965339}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.json
deleted file mode 100644
index 139ccda..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 105268, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.531576871871948, "TIME_S_1KI": 0.10004537819538652, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1219.4668582105637, "W": 86.09, "J_1KI": 11.584402270495913, "W_1KI": 0.8178173804005017, "W_D": 69.07325, "J_D": 978.4241975129843, "W_D_1KI": 0.6561656913781967, "J_D_1KI": 0.006233287336875372}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.output
deleted file mode 100644
index ad13e41..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_075.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 0.024872303009033203}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.9027, 0.7284, 0.4730,  ..., 0.3675, 0.6758, 0.7746])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 0.024872303009033203 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42215', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 4.210744380950928}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.9390, 0.3678, 0.4837,  ..., 0.7294, 0.6703, 0.0534])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 4.210744380950928 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '105268', '-m', 'matrices/as-caida_pruned/as-caida_G_075.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_075", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 97492, "MATRIX_DENSITY": 9.901267216465406e-05, "TIME_S": 10.531576871871948}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.7677, 0.2796, 0.1915,  ..., 0.1541, 0.8730, 0.0067])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.531576871871948 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 97491, 97491, 97492]),
-       col_indices=tensor([22754, 22754,   106,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=97492, layout=torch.sparse_csr)
-tensor([0.7677, 0.2796, 0.1915,  ..., 0.1541, 0.8730, 0.0067])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_075
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 97492
-Density: 9.901267216465406e-05
-Time: 10.531576871871948 seconds
-
-[19.05, 18.57, 18.7, 18.82, 18.8, 18.65, 18.86, 19.11, 18.93, 18.94]
-[86.09]
-14.16502332687378
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105268, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.531576871871948, 'TIME_S_1KI': 0.10004537819538652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.4668582105637, 'W': 86.09}
-[19.05, 18.57, 18.7, 18.82, 18.8, 18.65, 18.86, 19.11, 18.93, 18.94, 18.9, 18.76, 19.26, 19.08, 19.08, 19.0, 18.81, 19.15, 18.76, 19.1]
-340.33500000000004
-17.016750000000002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 105268, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_075', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 97492, 'MATRIX_DENSITY': 9.901267216465406e-05, 'TIME_S': 10.531576871871948, 'TIME_S_1KI': 0.10004537819538652, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.4668582105637, 'W': 86.09, 'J_1KI': 11.584402270495913, 'W_1KI': 0.8178173804005017, 'W_D': 69.07325, 'J_D': 978.4241975129843, 'W_D_1KI': 0.6561656913781967, 'J_D_1KI': 0.006233287336875372}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.json
deleted file mode 100644
index d8ea646..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 110476, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.772056579589844, "TIME_S_1KI": 0.0975058526701713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1209.966497516632, "W": 86.6, "J_1KI": 10.952301834938194, "W_1KI": 0.7838806618632101, "W_D": 69.526, "J_D": 971.4102852926254, "W_D_1KI": 0.6293312574676853, "J_D_1KI": 0.005696542755600178}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.output
deleted file mode 100644
index 1ebf518..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_085.output
+++ /dev/null
@@ -1,85 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 0.02397441864013672}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.7886, 0.1701, 0.5772,  ..., 0.5950, 0.8361, 0.6037])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 0.02397441864013672 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '43796', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 4.1625049114227295}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.6182, 0.4960, 0.6196,  ..., 0.3101, 0.0930, 0.9002])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 4.1625049114227295 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '110476', '-m', 'matrices/as-caida_pruned/as-caida_G_085.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_085", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 99166, "MATRIX_DENSITY": 0.0001007127830784073, "TIME_S": 10.772056579589844}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.8448, 0.2084, 0.2325,  ..., 0.3995, 0.9349, 0.2762])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.772056579589844 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 99165, 99165, 99166]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 31211, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=99166, layout=torch.sparse_csr)
-tensor([0.8448, 0.2084, 0.2325,  ..., 0.3995, 0.9349, 0.2762])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_085
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 99166
-Density: 0.0001007127830784073
-Time: 10.772056579589844 seconds
-
-[22.78, 18.74, 18.96, 19.11, 18.72, 18.74, 18.69, 19.0, 18.58, 18.61]
-[86.6]
-13.971899509429932
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 110476, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.772056579589844, 'TIME_S_1KI': 0.0975058526701713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.966497516632, 'W': 86.6}
-[22.78, 18.74, 18.96, 19.11, 18.72, 18.74, 18.69, 19.0, 18.58, 18.61, 19.3, 19.21, 18.84, 18.59, 18.51, 19.52, 19.13, 18.42, 18.76, 19.23]
-341.48
-17.074
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 110476, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_085', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 99166, 'MATRIX_DENSITY': 0.0001007127830784073, 'TIME_S': 10.772056579589844, 'TIME_S_1KI': 0.0975058526701713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.966497516632, 'W': 86.6, 'J_1KI': 10.952301834938194, 'W_1KI': 0.7838806618632101, 'W_D': 69.526, 'J_D': 971.4102852926254, 'W_D_1KI': 0.6293312574676853, 'J_D_1KI': 0.005696542755600178}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.json
deleted file mode 100644
index fbaff5d..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 107497, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.770461082458496, "TIME_S_1KI": 0.10019313173817405, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1238.6108507156373, "W": 86.48, "J_1KI": 11.522282954088368, "W_1KI": 0.8044875670948957, "W_D": 69.43700000000001, "J_D": 994.5122761464121, "W_D_1KI": 0.6459436077285879, "J_D_1KI": 0.006008945437813036}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.output
deleted file mode 100644
index 7ff6446..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_095.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 0.0250704288482666}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.4833, 0.8951, 0.5308,  ..., 0.3731, 0.7013, 0.1331])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 0.0250704288482666 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '41882', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 4.090898036956787}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.0579, 0.7468, 0.4378,  ..., 0.4871, 0.7748, 0.6891])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 4.090898036956787 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '107497', '-m', 'matrices/as-caida_pruned/as-caida_G_095.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_095", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102290, "MATRIX_DENSITY": 0.00010388551097241275, "TIME_S": 10.770461082458496}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2379, 0.0163, 0.9504,  ..., 0.9694, 0.0397, 0.3577])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.770461082458496 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 102289, 102289,
-                            102290]),
-       col_indices=tensor([  106,   329,  1040,  ...,   882, 25970, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=102290, layout=torch.sparse_csr)
-tensor([0.2379, 0.0163, 0.9504,  ..., 0.9694, 0.0397, 0.3577])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_095
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 102290
-Density: 0.00010388551097241275
-Time: 10.770461082458496 seconds
-
-[18.93, 21.33, 19.05, 18.6, 18.75, 19.05, 18.88, 18.52, 18.7, 18.69]
-[86.48]
-14.322512149810791
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 107497, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.770461082458496, 'TIME_S_1KI': 0.10019313173817405, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1238.6108507156373, 'W': 86.48}
-[18.93, 21.33, 19.05, 18.6, 18.75, 19.05, 18.88, 18.52, 18.7, 18.69, 19.38, 18.53, 19.04, 18.57, 19.18, 18.64, 18.84, 18.71, 18.71, 18.52]
-340.86
-17.043
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 107497, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_095', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102290, 'MATRIX_DENSITY': 0.00010388551097241275, 'TIME_S': 10.770461082458496, 'TIME_S_1KI': 0.10019313173817405, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1238.6108507156373, 'W': 86.48, 'J_1KI': 11.522282954088368, 'W_1KI': 0.8044875670948957, 'W_D': 69.43700000000001, 'J_D': 994.5122761464121, 'W_D_1KI': 0.6459436077285879, 'J_D_1KI': 0.006008945437813036}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.json
deleted file mode 100644
index 6f2de89..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 103831, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.351726531982422, "TIME_S_1KI": 0.09969784102996622, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1209.3511731219292, "W": 86.07, "J_1KI": 11.647303532874856, "W_1KI": 0.8289431865242557, "W_D": 69.082, "J_D": 970.6564161915778, "W_D_1KI": 0.6653311631401025, "J_D_1KI": 0.0064078277502875106}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.output
deleted file mode 100644
index bcbcdde..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_105.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 0.023554086685180664}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.2517, 0.3763, 0.9196,  ..., 0.6094, 0.1457, 0.6521])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 0.023554086685180664 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '44578', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 4.50795316696167}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.4299, 0.9586, 0.9105,  ..., 0.3781, 0.8739, 0.8459])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 4.50795316696167 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '103831', '-m', 'matrices/as-caida_pruned/as-caida_G_105.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_105", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104726, "MATRIX_DENSITY": 0.00010635950749923647, "TIME_S": 10.351726531982422}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.8719, 0.2400, 0.7607,  ..., 0.7182, 0.0947, 0.1009])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.351726531982422 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 104725, 104725,
-                            104726]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=104726, layout=torch.sparse_csr)
-tensor([0.8719, 0.2400, 0.7607,  ..., 0.7182, 0.0947, 0.1009])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_105
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 104726
-Density: 0.00010635950749923647
-Time: 10.351726531982422 seconds
-
-[19.38, 18.67, 18.72, 18.67, 19.24, 18.61, 18.54, 18.91, 19.05, 18.66]
-[86.07]
-14.050786256790161
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103831, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.351726531982422, 'TIME_S_1KI': 0.09969784102996622, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.3511731219292, 'W': 86.07}
-[19.38, 18.67, 18.72, 18.67, 19.24, 18.61, 18.54, 18.91, 19.05, 18.66, 19.08, 18.59, 19.94, 18.67, 18.85, 18.81, 19.2, 18.59, 18.8, 18.68]
-339.76
-16.988
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 103831, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_105', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104726, 'MATRIX_DENSITY': 0.00010635950749923647, 'TIME_S': 10.351726531982422, 'TIME_S_1KI': 0.09969784102996622, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1209.3511731219292, 'W': 86.07, 'J_1KI': 11.647303532874856, 'W_1KI': 0.8289431865242557, 'W_D': 69.082, 'J_D': 970.6564161915778, 'W_D_1KI': 0.6653311631401025, 'J_D_1KI': 0.0064078277502875106}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.json
deleted file mode 100644
index b47cb22..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.json
+++ /dev/null
@@ -1 +0,0 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 104289, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.945078134536743, "TIME_S_1KI": 0.10494949740180406, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1223.009027416706, "W": 86.63, "J_1KI": 11.727114340119341, "W_1KI": 0.8306724582650136, "W_D": 69.74199999999999, "J_D": 984.5907375054358, "W_D_1KI": 0.6687378342874127, "J_D_1KI": 0.006412352542333446}
diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.output
deleted file mode 100644
index 312abf7..0000000
--- a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_10_10_10_as-caida_G_115.output
+++ /dev/null
@@ -1,89 +0,0 @@
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 0.024857282638549805}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.9051, 0.6149, 0.4220,  ..., 0.0889, 0.4273, 0.7147])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 0.024857282638549805 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '42241', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 4.252889633178711}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.2480, 0.5973, 0.7725,  ..., 0.3227, 0.0475, 0.0987])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 4.252889633178711 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '104289', '-m', 'matrices/as-caida_pruned/as-caida_G_115.mtx']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_115", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106312, "MATRIX_DENSITY": 0.00010797024579625715, "TIME_S": 10.945078134536743}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.4120, 0.5717, 0.6301,  ..., 0.0346, 0.1378, 0.3165])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.945078134536743 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 106311, 106311,
-                            106312]),
-       col_indices=tensor([  106,   329,  1040,  ...,   160,   882, 12170]),
-       values=tensor([1., 1., 1.,  ..., 1., 1., 1.]), size=(31379, 31379),
-       nnz=106312, layout=torch.sparse_csr)
-tensor([0.4120, 0.5717, 0.6301,  ..., 0.0346, 0.1378, 0.3165])
-Matrix Type: SuiteSparse
-Matrix: as-caida_G_115
-Matrix Format: csr
-Shape: torch.Size([31379, 31379])
-Rows: 31379
-Size: 984641641
-NNZ: 106312
-Density: 0.00010797024579625715
-Time: 10.945078134536743 seconds
-
-[19.1, 18.45, 19.14, 18.48, 19.1, 19.01, 18.81, 18.56, 18.71, 18.72]
-[86.63]
-14.117615461349487
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104289, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.945078134536743, 'TIME_S_1KI': 0.10494949740180406, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.009027416706, 'W': 86.63}
-[19.1, 18.45, 19.14, 18.48, 19.1, 19.01, 18.81, 18.56, 18.71, 18.72, 20.23, 18.52, 18.94, 18.59, 18.76, 18.47, 18.57, 18.57, 18.84, 18.43]
-337.76
-16.887999999999998
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 104289, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_115', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106312, 'MATRIX_DENSITY': 0.00010797024579625715, 'TIME_S': 10.945078134536743, 'TIME_S_1KI': 0.10494949740180406, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.009027416706, 'W': 86.63, 'J_1KI': 11.727114340119341, 'W_1KI': 0.8306724582650136, 'W_D': 69.74199999999999, 'J_D': 984.5907375054358, 'W_D_1KI': 0.6687378342874127, 'J_D_1KI': 0.006412352542333446}