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pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_120.json create mode 100644 pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_120.output diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_005.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_005.json new file mode 100644 index 0000000..457a354 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_005.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 11868, "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": 20.50465679168701, "TIME_S_1KI": 1.7277263895927715, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 595.5481514930726, "W": 24.482217423939822, "J_1KI": 50.1810036647348, "W_1KI": 2.062876426014478, "W_D": 6.179217423939821, "J_D": 150.3140606415273, "W_D_1KI": 0.5206620680771672, "J_D_1KI": 0.04387108763710543} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_005.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_005.output new file mode 100644 index 0000000..ba6cf0e --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_005.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 1.769423484802246} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.9190, 0.7017, 0.0047, ..., 0.0989, 0.5520, 0.9034]) +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: 1.769423484802246 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 11868 -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": 20.50465679168701} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.1433, 0.5564, 0.9502, ..., 0.1610, 0.5318, 0.0201]) +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: 20.50465679168701 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([0.1433, 0.5564, 0.9502, ..., 0.1610, 0.5318, 0.0201]) +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: 20.50465679168701 seconds + +[20.2, 20.44, 20.4, 20.48, 20.44, 20.52, 20.32, 20.4, 20.68, 20.68] +[20.8, 20.6, 20.92, 25.16, 26.4, 33.24, 34.12, 31.92, 30.84, 24.28, 24.16, 24.2, 24.2, 24.16, 24.04, 24.16, 24.48, 24.6, 24.52, 24.8, 24.84, 24.8, 24.56, 24.4] +24.325743913650513 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 11868, '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': 20.50465679168701, 'TIME_S_1KI': 1.7277263895927715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 595.5481514930726, 'W': 24.482217423939822} +[20.2, 20.44, 20.4, 20.48, 20.44, 20.52, 20.32, 20.4, 20.68, 20.68, 20.04, 19.88, 19.88, 19.88, 20.12, 20.4, 20.64, 20.36, 20.52, 20.48] +366.06 +18.303 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 11868, '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': 20.50465679168701, 'TIME_S_1KI': 1.7277263895927715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 595.5481514930726, 'W': 24.482217423939822, 'J_1KI': 50.1810036647348, 'W_1KI': 2.062876426014478, 'W_D': 6.179217423939821, 'J_D': 150.3140606415273, 'W_D_1KI': 0.5206620680771672, 'J_D_1KI': 0.04387108763710543} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_010.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_010.json new file mode 100644 index 0000000..e44a83a --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_010.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 11106, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 20.421429872512817, "TIME_S_1KI": 1.838774524807565, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 604.79751707077, "W": 23.847910243049302, "J_1KI": 54.45682667664055, "W_1KI": 2.1472996797271113, "W_D": 5.517910243049304, "J_D": 139.93756184101088, "W_D_1KI": 0.49684046848994273, "J_D_1KI": 0.044736220825674654} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_010.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_010.output new file mode 100644 index 0000000..93f33c2 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_010.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_010.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 1.8907017707824707} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 28, 28, ..., 74993, 74993, 74994]), + col_indices=tensor([ 1040, 2020, 2054, ..., 160, 160, 12170]), + values=tensor([1., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=74994, layout=torch.sparse_csr) +tensor([0.9147, 0.8941, 0.7248, ..., 0.0711, 0.2516, 0.9374]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_010 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 74994 +Density: 7.616375021864427e-05 +Time: 1.8907017707824707 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 11106 -m matrices/as-caida_pruned/as-caida_G_010.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 20.421429872512817} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 28, 28, ..., 74993, 74993, 74994]), + col_indices=tensor([ 1040, 2020, 2054, ..., 160, 160, 12170]), + values=tensor([1., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=74994, layout=torch.sparse_csr) +tensor([0.3281, 0.7131, 0.6291, ..., 0.9063, 0.8724, 0.7956]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_010 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 74994 +Density: 7.616375021864427e-05 +Time: 20.421429872512817 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, 28, 28, ..., 74993, 74993, 74994]), + col_indices=tensor([ 1040, 2020, 2054, ..., 160, 160, 12170]), + values=tensor([1., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=74994, layout=torch.sparse_csr) +tensor([0.3281, 0.7131, 0.6291, ..., 0.9063, 0.8724, 0.7956]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_010 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 74994 +Density: 7.616375021864427e-05 +Time: 20.421429872512817 seconds + +[20.4, 20.28, 20.36, 20.52, 20.48, 20.6, 20.56, 20.36, 20.32, 20.12] +[20.04, 20.32, 20.32, 21.2, 22.36, 26.6, 27.8, 28.68, 28.68, 28.44, 25.24, 24.84, 24.8, 25.08, 25.08, 24.84, 24.96, 24.96, 24.72, 24.84, 25.12, 24.92, 24.84, 24.76, 24.84] +25.36060857772827 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 11106, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 20.421429872512817, 'TIME_S_1KI': 1.838774524807565, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 604.79751707077, 'W': 23.847910243049302} +[20.4, 20.28, 20.36, 20.52, 20.48, 20.6, 20.56, 20.36, 20.32, 20.12, 20.28, 20.4, 20.32, 20.32, 20.4, 20.4, 20.56, 20.28, 20.0, 20.08] +366.59999999999997 +18.33 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 11106, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 20.421429872512817, 'TIME_S_1KI': 1.838774524807565, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 604.79751707077, 'W': 23.847910243049302, 'J_1KI': 54.45682667664055, 'W_1KI': 2.1472996797271113, 'W_D': 5.517910243049304, 'J_D': 139.93756184101088, 'W_D_1KI': 0.49684046848994273, 'J_D_1KI': 0.044736220825674654} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_015.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_015.json new file mode 100644 index 0000000..0c19865 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_015.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 10628, "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": 20.489109754562378, "TIME_S_1KI": 1.9278424684383118, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 592.0631146240235, "W": 24.361970542226484, "J_1KI": 55.707857981183984, "W_1KI": 2.292244123280625, "W_D": 5.985970542226486, "J_D": 145.47560334396368, "W_D_1KI": 0.5632264341575542, "J_D_1KI": 0.05299458356770364} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_015.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_015.output new file mode 100644 index 0000000..ce5da8f --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_015.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 1.9757425785064697} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.7519, 0.6257, 0.7169, ..., 0.6184, 0.4198, 0.1096]) +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: 1.9757425785064697 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 10628 -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": 20.489109754562378} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.7931, 0.3419, 0.7780, ..., 0.8001, 0.8539, 0.6736]) +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: 20.489109754562378 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([0.7931, 0.3419, 0.7780, ..., 0.8001, 0.8539, 0.6736]) +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: 20.489109754562378 seconds + +[20.48, 20.4, 20.32, 20.36, 20.68, 20.4, 20.36, 20.24, 20.32, 20.2] +[20.2, 20.32, 20.56, 24.04, 25.84, 32.64, 33.32, 33.6, 30.12, 26.64, 24.0, 23.92, 24.12, 24.12, 24.32, 24.28, 24.44, 24.56, 24.24, 24.52, 24.44, 24.2, 24.2, 24.32] +24.302759647369385 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 10628, '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': 20.489109754562378, 'TIME_S_1KI': 1.9278424684383118, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 592.0631146240235, 'W': 24.361970542226484} +[20.48, 20.4, 20.32, 20.36, 20.68, 20.4, 20.36, 20.24, 20.32, 20.2, 20.52, 20.44, 20.44, 20.2, 20.36, 20.4, 20.36, 20.52, 20.8, 20.64] +367.52 +18.375999999999998 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 10628, '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': 20.489109754562378, 'TIME_S_1KI': 1.9278424684383118, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 592.0631146240235, 'W': 24.361970542226484, 'J_1KI': 55.707857981183984, 'W_1KI': 2.292244123280625, 'W_D': 5.985970542226486, 'J_D': 145.47560334396368, 'W_D_1KI': 0.5632264341575542, 'J_D_1KI': 0.05299458356770364} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_020.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_020.json new file mode 100644 index 0000000..b169c51 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_020.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 10071, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 20.918866634368896, "TIME_S_1KI": 2.077138976702303, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 573.2929569816588, "W": 24.61671638273014, "J_1KI": 56.925127294375805, "W_1KI": 2.4443169876606237, "W_D": 6.3057163827301395, "J_D": 146.85235572195043, "W_D_1KI": 0.626126142660127, "J_D_1KI": 0.062171198754853246} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_020.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_020.output new file mode 100644 index 0000000..f98303d --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_020.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_020.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 2.085054636001587} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.5085, 0.4122, 0.0237, ..., 0.5225, 0.0817, 0.0530]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 2.085054636001587 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 10071 -m matrices/as-caida_pruned/as-caida_G_020.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 20.918866634368896} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.4326, 0.5436, 0.4160, ..., 0.2480, 0.2557, 0.8019]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 20.918866634368896 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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.4326, 0.5436, 0.4160, ..., 0.2480, 0.2557, 0.8019]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 20.918866634368896 seconds + +[20.52, 20.32, 20.24, 20.16, 20.0, 20.04, 20.32, 20.44, 20.6, 20.44] +[20.6, 20.44, 20.44, 23.72, 25.68, 32.48, 33.28, 33.84, 30.04, 26.88, 24.32, 24.48, 24.44, 24.56, 24.56, 24.8, 24.92, 25.0, 24.96, 24.84, 25.0, 24.72, 24.84] +23.288766384124756 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 10071, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 20.918866634368896, 'TIME_S_1KI': 2.077138976702303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 573.2929569816588, 'W': 24.61671638273014} +[20.52, 20.32, 20.24, 20.16, 20.0, 20.04, 20.32, 20.44, 20.6, 20.44, 20.32, 20.36, 20.28, 20.44, 20.48, 20.48, 20.48, 20.4, 20.32, 20.44] +366.22 +18.311 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 10071, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 20.918866634368896, 'TIME_S_1KI': 2.077138976702303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 573.2929569816588, 'W': 24.61671638273014, 'J_1KI': 56.925127294375805, 'W_1KI': 2.4443169876606237, 'W_D': 6.3057163827301395, 'J_D': 146.85235572195043, 'W_D_1KI': 0.626126142660127, 'J_D_1KI': 0.062171198754853246} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_025.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_025.json new file mode 100644 index 0000000..6984136 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_025.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 9843, "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": 20.97947359085083, "TIME_S_1KI": 2.1314105039978495, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 582.6910747146605, "W": 23.96309690957501, "J_1KI": 59.198524303023525, "W_1KI": 2.434531840858987, "W_D": 5.643096909575011, "J_D": 137.21858303070056, "W_D_1KI": 0.5733106684522007, "J_D_1KI": 0.058245521533292766} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_025.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_025.output new file mode 100644 index 0000000..be87417 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_025.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 2.1334664821624756} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.1155, 0.2832, 0.4927, ..., 0.9506, 0.2786, 0.8718]) +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: 2.1334664821624756 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 9843 -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": 20.97947359085083} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.9708, 0.2572, 0.8092, ..., 0.0249, 0.0177, 0.4715]) +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: 20.97947359085083 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([0.9708, 0.2572, 0.8092, ..., 0.0249, 0.0177, 0.4715]) +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: 20.97947359085083 seconds + +[19.8, 20.16, 20.16, 20.24, 20.4, 20.36, 20.44, 20.44, 20.6, 20.68] +[20.56, 20.52, 20.4, 22.8, 23.4, 29.48, 30.2, 30.32, 29.48, 24.36, 24.36, 24.52, 24.88, 24.8, 24.72, 24.76, 24.6, 24.2, 24.32, 24.44, 24.76, 24.96, 25.4, 25.4] +24.316184043884277 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9843, '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': 20.97947359085083, 'TIME_S_1KI': 2.1314105039978495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.6910747146605, 'W': 23.96309690957501} +[19.8, 20.16, 20.16, 20.24, 20.4, 20.36, 20.44, 20.44, 20.6, 20.68, 20.36, 20.48, 20.6, 20.4, 20.52, 20.6, 20.4, 20.0, 20.12, 20.12] +366.40000000000003 +18.32 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9843, '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': 20.97947359085083, 'TIME_S_1KI': 2.1314105039978495, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.6910747146605, 'W': 23.96309690957501, 'J_1KI': 59.198524303023525, 'W_1KI': 2.434531840858987, 'W_D': 5.643096909575011, 'J_D': 137.21858303070056, 'W_D_1KI': 0.5733106684522007, 'J_D_1KI': 0.058245521533292766} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_030.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_030.json new file mode 100644 index 0000000..32f8be9 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_030.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 9604, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 20.284237384796143, "TIME_S_1KI": 2.1120613686793153, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 606.7543531799317, "W": 24.93372016805261, "J_1KI": 63.17725460015949, "W_1KI": 2.5961807755156823, "W_D": 6.76272016805261, "J_D": 164.56869948196425, "W_D_1KI": 0.704156618914266, "J_D_1KI": 0.07331909817932798} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_030.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_030.output new file mode 100644 index 0000000..6484bed --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_030.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_030.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 2.1865053176879883} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.6109, 0.9417, 0.2480, ..., 0.3925, 0.8143, 0.3245]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 2.1865053176879883 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 9604 -m matrices/as-caida_pruned/as-caida_G_030.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 20.284237384796143} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.8685, 0.7727, 0.7628, ..., 0.3143, 0.3501, 0.5646]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 20.284237384796143 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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.8685, 0.7727, 0.7628, ..., 0.3143, 0.3501, 0.5646]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 20.284237384796143 seconds + +[20.04, 19.8, 19.96, 20.12, 20.08, 20.32, 20.4, 20.6, 20.48, 20.4] +[20.2, 20.28, 23.2, 23.2, 24.64, 31.64, 32.88, 33.36, 30.64, 30.28, 25.0, 25.32, 25.12, 25.16, 24.88, 24.88, 24.76, 24.96, 25.04, 24.84, 25.04, 25.16, 25.24, 25.32] +24.33469009399414 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9604, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 20.284237384796143, 'TIME_S_1KI': 2.1120613686793153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 606.7543531799317, 'W': 24.93372016805261} +[20.04, 19.8, 19.96, 20.12, 20.08, 20.32, 20.4, 20.6, 20.48, 20.4, 20.16, 20.0, 20.12, 20.04, 20.36, 20.36, 20.04, 20.0, 20.2, 20.48] +363.41999999999996 +18.171 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9604, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 20.284237384796143, 'TIME_S_1KI': 2.1120613686793153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 606.7543531799317, 'W': 24.93372016805261, 'J_1KI': 63.17725460015949, 'W_1KI': 2.5961807755156823, 'W_D': 6.76272016805261, 'J_D': 164.56869948196425, 'W_D_1KI': 0.704156618914266, 'J_D_1KI': 0.07331909817932798} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_035.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_035.json new file mode 100644 index 0000000..cc5530d --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_035.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 9472, "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": 20.188151359558105, "TIME_S_1KI": 2.1313504391425364, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 612.7760334014894, "W": 25.19675156600016, "J_1KI": 64.69341568850183, "W_1KI": 2.6601300217483277, "W_D": 6.96475156600016, "J_D": 169.3802801151277, "W_D_1KI": 0.7352989406672467, "J_D_1KI": 0.07762868883733601} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_035.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_035.output new file mode 100644 index 0000000..c9c67fe --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_035.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 2.2169973850250244} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.2444, 0.3577, 0.2903, ..., 0.6956, 0.4519, 0.7042]) +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: 2.2169973850250244 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 9472 -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": 20.188151359558105} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.3089, 0.5002, 0.8484, ..., 0.9388, 0.8606, 0.9713]) +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: 20.188151359558105 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([0.3089, 0.5002, 0.8484, ..., 0.9388, 0.8606, 0.9713]) +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: 20.188151359558105 seconds + +[20.32, 20.32, 20.32, 20.2, 20.04, 20.04, 19.88, 20.24, 20.36, 20.28] +[20.68, 20.6, 20.84, 25.76, 30.24, 33.32, 34.32, 31.76, 31.76, 30.56, 24.76, 24.52, 24.48, 24.48, 24.52, 25.2, 25.36, 25.48, 25.36, 24.84, 24.84, 24.6, 24.64, 24.64] +24.31964421272278 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9472, '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': 20.188151359558105, 'TIME_S_1KI': 2.1313504391425364, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 612.7760334014894, 'W': 25.19675156600016} +[20.32, 20.32, 20.32, 20.2, 20.04, 20.04, 19.88, 20.24, 20.36, 20.28, 20.4, 20.36, 20.28, 20.16, 20.12, 20.4, 20.56, 20.44, 20.28, 20.28] +364.64 +18.232 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9472, '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': 20.188151359558105, 'TIME_S_1KI': 2.1313504391425364, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 612.7760334014894, 'W': 25.19675156600016, 'J_1KI': 64.69341568850183, 'W_1KI': 2.6601300217483277, 'W_D': 6.96475156600016, 'J_D': 169.3802801151277, 'W_D_1KI': 0.7352989406672467, 'J_D_1KI': 0.07762868883733601} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_040.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_040.json new file mode 100644 index 0000000..11a6926 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_040.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 9338, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 20.749686002731323, "TIME_S_1KI": 2.222069608345612, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 604.0135382080078, "W": 24.829919965337524, "J_1KI": 64.68339453930263, "W_1KI": 2.659019058185642, "W_D": 6.3889199653375215, "J_D": 155.41709997367855, "W_D_1KI": 0.6841850466199959, "J_D_1KI": 0.0732689062561572} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_040.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_040.output new file mode 100644 index 0000000..c5aca25 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_040.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_040.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 2.2487361431121826} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.1229, 0.7875, 0.3967, ..., 0.2389, 0.2107, 0.7413]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 2.2487361431121826 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 9338 -m matrices/as-caida_pruned/as-caida_G_040.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 20.749686002731323} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.4417, 0.5816, 0.3830, ..., 0.9748, 0.5625, 0.5118]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 20.749686002731323 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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.4417, 0.5816, 0.3830, ..., 0.9748, 0.5625, 0.5118]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 20.749686002731323 seconds + +[20.28, 20.52, 20.44, 20.32, 20.24, 20.44, 20.36, 20.56, 20.72, 20.76] +[20.6, 20.44, 20.44, 21.56, 23.72, 30.96, 32.52, 33.28, 31.88, 30.6, 25.08, 24.84, 25.16, 25.08, 25.08, 25.24, 25.2, 25.2, 25.32, 25.72, 25.56, 25.36, 25.12, 24.64] +24.32603645324707 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9338, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 20.749686002731323, 'TIME_S_1KI': 2.222069608345612, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 604.0135382080078, 'W': 24.829919965337524} +[20.28, 20.52, 20.44, 20.32, 20.24, 20.44, 20.36, 20.56, 20.72, 20.76, 20.72, 20.52, 20.76, 20.6, 20.6, 20.32, 20.4, 20.4, 20.44, 20.6] +368.82000000000005 +18.441000000000003 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9338, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 20.749686002731323, 'TIME_S_1KI': 2.222069608345612, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 604.0135382080078, 'W': 24.829919965337524, 'J_1KI': 64.68339453930263, 'W_1KI': 2.659019058185642, 'W_D': 6.3889199653375215, 'J_D': 155.41709997367855, 'W_D_1KI': 0.6841850466199959, 'J_D_1KI': 0.0732689062561572} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_045.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_045.json new file mode 100644 index 0000000..4baec83 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_045.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 9478, "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": 20.637967348098755, "TIME_S_1KI": 2.1774601548954164, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 628.3440246582034, "W": 24.768273200628375, "J_1KI": 66.29500154655025, "W_1KI": 2.6132383625900375, "W_D": 6.418273200628374, "J_D": 162.8245772957804, "W_D_1KI": 0.6771759021553464, "J_D_1KI": 0.07144713042364913} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_045.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_045.output new file mode 100644 index 0000000..32d567d --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_045.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 2.21557879447937} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.8853, 0.7709, 0.2706, ..., 0.2431, 0.7108, 0.8769]) +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: 2.21557879447937 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 9478 -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": 20.637967348098755} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.1963, 0.0254, 0.9151, ..., 0.5981, 0.5083, 0.3851]) +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: 20.637967348098755 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([0.1963, 0.0254, 0.9151, ..., 0.5981, 0.5083, 0.3851]) +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: 20.637967348098755 seconds + +[20.8, 20.56, 20.24, 20.28, 20.4, 20.36, 20.48, 20.72, 20.44, 20.2] +[20.2, 20.2, 20.2, 21.48, 23.16, 29.44, 30.2, 30.84, 30.12, 30.0, 25.2, 25.64, 25.68, 25.68, 25.8, 26.16, 26.36, 26.04, 25.88, 25.72, 25.44, 25.44, 25.72, 25.6, 25.6] +25.36890721321106 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9478, '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': 20.637967348098755, 'TIME_S_1KI': 2.1774601548954164, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 628.3440246582034, 'W': 24.768273200628375} +[20.8, 20.56, 20.24, 20.28, 20.4, 20.36, 20.48, 20.72, 20.44, 20.2, 20.6, 20.44, 20.4, 20.4, 20.2, 20.32, 20.24, 20.2, 20.36, 20.32] +367.0 +18.35 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9478, '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': 20.637967348098755, 'TIME_S_1KI': 2.1774601548954164, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 628.3440246582034, 'W': 24.768273200628375, 'J_1KI': 66.29500154655025, 'W_1KI': 2.6132383625900375, 'W_D': 6.418273200628374, 'J_D': 162.8245772957804, 'W_D_1KI': 0.6771759021553464, 'J_D_1KI': 0.07144713042364913} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_050.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_050.json new file mode 100644 index 0000000..8b623e1 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_050.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 9122, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 20.173283338546753, "TIME_S_1KI": 2.2114978446115714, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 598.2593908691406, "W": 24.587966612408714, "J_1KI": 65.58423491220572, "W_1KI": 2.6954578614787015, "W_D": 6.4099666124087165, "J_D": 155.96339386177067, "W_D_1KI": 0.7026931169051432, "J_D_1KI": 0.07703279071531936} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_050.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_050.output new file mode 100644 index 0000000..9d3e435 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_050.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_050.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 2.3019206523895264} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.4612, 0.9596, 0.7932, ..., 0.5334, 0.6140, 0.6916]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 2.3019206523895264 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 9122 -m matrices/as-caida_pruned/as-caida_G_050.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 20.173283338546753} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.9154, 0.6769, 0.9760, ..., 0.3153, 0.3040, 0.9723]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 20.173283338546753 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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.9154, 0.6769, 0.9760, ..., 0.3153, 0.3040, 0.9723]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 20.173283338546753 seconds + +[19.8, 19.84, 19.88, 19.88, 20.08, 20.16, 20.24, 20.6, 20.48, 20.8] +[21.12, 21.08, 24.36, 26.16, 27.76, 31.28, 31.28, 32.04, 29.04, 28.04, 24.44, 24.44, 24.52, 24.4, 24.48, 24.08, 24.12, 24.2, 24.24, 24.24, 24.28, 24.52, 24.48, 24.32] +24.33138942718506 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9122, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 20.173283338546753, 'TIME_S_1KI': 2.2114978446115714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 598.2593908691406, 'W': 24.587966612408714} +[19.8, 19.84, 19.88, 19.88, 20.08, 20.16, 20.24, 20.6, 20.48, 20.8, 20.28, 20.24, 20.4, 20.4, 20.36, 20.28, 20.12, 19.92, 20.16, 20.16] +363.55999999999995 +18.177999999999997 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9122, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 20.173283338546753, 'TIME_S_1KI': 2.2114978446115714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 598.2593908691406, 'W': 24.587966612408714, 'J_1KI': 65.58423491220572, 'W_1KI': 2.6954578614787015, 'W_D': 6.4099666124087165, 'J_D': 155.96339386177067, 'W_D_1KI': 0.7026931169051432, 'J_D_1KI': 0.07703279071531936} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_055.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_055.json new file mode 100644 index 0000000..2de2cb3 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_055.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 9445, "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": 21.90527892112732, "TIME_S_1KI": 2.3192460477636128, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 600.4606732463836, "W": 24.72266919997052, "J_1KI": 63.574449258484236, "W_1KI": 2.6175404129137663, "W_D": 6.193669199970518, "J_D": 150.43095660901056, "W_D_1KI": 0.6557616940148775, "J_D_1KI": 0.06942950704233748} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_055.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_055.output new file mode 100644 index 0000000..14950ed --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_055.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 2.3901093006134033} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.5831, 0.1214, 0.4741, ..., 0.1774, 0.8133, 0.4631]) +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: 2.3901093006134033 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8786 -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": 19.53348445892334} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.2333, 0.7547, 0.3714, ..., 0.2114, 0.9053, 0.3045]) +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: 19.53348445892334 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 9445 -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": 21.90527892112732} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.7742, 0.0253, 0.0492, ..., 0.9424, 0.6447, 0.9766]) +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: 21.90527892112732 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([0.7742, 0.0253, 0.0492, ..., 0.9424, 0.6447, 0.9766]) +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: 21.90527892112732 seconds + +[20.28, 20.24, 20.28, 20.56, 20.72, 20.8, 20.8, 20.96, 20.96, 20.76] +[20.6, 20.56, 20.6, 22.4, 23.28, 29.48, 30.4, 30.72, 29.88, 29.88, 25.44, 25.32, 25.32, 25.6, 25.48, 25.2, 25.44, 25.72, 25.52, 25.8, 26.04, 26.04, 25.84, 25.64] +24.2878577709198 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9445, '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': 21.90527892112732, 'TIME_S_1KI': 2.3192460477636128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 600.4606732463836, 'W': 24.72266919997052} +[20.28, 20.24, 20.28, 20.56, 20.72, 20.8, 20.8, 20.96, 20.96, 20.76, 20.32, 20.56, 20.8, 20.72, 20.64, 20.52, 20.44, 20.36, 20.28, 20.52] +370.58000000000004 +18.529000000000003 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9445, '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': 21.90527892112732, 'TIME_S_1KI': 2.3192460477636128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 600.4606732463836, 'W': 24.72266919997052, 'J_1KI': 63.574449258484236, 'W_1KI': 2.6175404129137663, 'W_D': 6.193669199970518, 'J_D': 150.43095660901056, 'W_D_1KI': 0.6557616940148775, 'J_D_1KI': 0.06942950704233748} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_060.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_060.json new file mode 100644 index 0000000..7c22e50 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_060.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 9065, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 21.8990478515625, "TIME_S_1KI": 2.4157802373483177, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 595.6967398834229, "W": 24.49063819578549, "J_1KI": 65.71392607649453, "W_1KI": 2.7016699609250403, "W_D": 6.139638195785491, "J_D": 149.33716418719297, "W_D_1KI": 0.6772904794027017, "J_D_1KI": 0.07471489017128535} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_060.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_060.output new file mode 100644 index 0000000..90bc000 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_060.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_060.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 2.316600799560547} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.0765, 0.2337, 0.8130, ..., 0.9153, 0.0336, 0.4203]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 2.316600799560547 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 9065 -m matrices/as-caida_pruned/as-caida_G_060.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 21.8990478515625} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.7137, 0.0725, 0.7669, ..., 0.5328, 0.1897, 0.1802]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 21.8990478515625 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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.7137, 0.0725, 0.7669, ..., 0.5328, 0.1897, 0.1802]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 21.8990478515625 seconds + +[20.2, 20.2, 20.04, 20.24, 20.08, 20.2, 20.48, 20.88, 20.64, 20.68] +[20.56, 19.96, 20.72, 23.48, 23.48, 30.04, 31.32, 32.08, 30.72, 30.36, 24.96, 24.96, 25.16, 25.0, 24.68, 24.52, 24.52, 24.52, 24.56, 24.56, 24.84, 24.88, 25.28, 25.28] +24.32344698905945 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9065, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 21.8990478515625, 'TIME_S_1KI': 2.4157802373483177, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 595.6967398834229, 'W': 24.49063819578549} +[20.2, 20.2, 20.04, 20.24, 20.08, 20.2, 20.48, 20.88, 20.64, 20.68, 20.24, 20.28, 20.72, 20.92, 20.6, 20.52, 20.52, 20.4, 19.84, 19.8] +367.02 +18.351 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9065, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 21.8990478515625, 'TIME_S_1KI': 2.4157802373483177, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 595.6967398834229, 'W': 24.49063819578549, 'J_1KI': 65.71392607649453, 'W_1KI': 2.7016699609250403, 'W_D': 6.139638195785491, 'J_D': 149.33716418719297, 'W_D_1KI': 0.6772904794027017, 'J_D_1KI': 0.07471489017128535} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_065.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_065.json new file mode 100644 index 0000000..fb83e5e --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_065.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 9003, "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": 20.59884262084961, "TIME_S_1KI": 2.287997625330402, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 595.7279040813446, "W": 24.476738666267384, "J_1KI": 66.16993269813891, "W_1KI": 2.7187313857900013, "W_D": 6.194738666267384, "J_D": 150.77084951162337, "W_D_1KI": 0.6880749379392851, "J_D_1KI": 0.07642729511710375} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_065.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_065.output new file mode 100644 index 0000000..c5066e2 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_065.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 2.3325326442718506} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.6782, 0.7733, 0.7625, ..., 0.9659, 0.3257, 0.6368]) +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: 2.3325326442718506 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 9003 -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": 20.59884262084961} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.8368, 0.9690, 0.7119, ..., 0.9803, 0.0517, 0.2837]) +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: 20.59884262084961 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([0.8368, 0.9690, 0.7119, ..., 0.9803, 0.0517, 0.2837]) +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: 20.59884262084961 seconds + +[20.6, 20.4, 20.32, 20.12, 20.24, 20.48, 20.8, 20.92, 20.96, 20.84] +[20.84, 20.68, 20.28, 23.6, 26.16, 31.6, 32.72, 33.6, 29.72, 27.6, 24.84, 25.0, 24.84, 24.84, 24.72, 24.64, 24.2, 24.08, 24.2, 24.08, 24.2, 24.52, 24.56, 24.6] +24.33853268623352 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9003, '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': 20.59884262084961, 'TIME_S_1KI': 2.287997625330402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 595.7279040813446, 'W': 24.476738666267384} +[20.6, 20.4, 20.32, 20.12, 20.24, 20.48, 20.8, 20.92, 20.96, 20.84, 20.2, 20.04, 20.0, 20.0, 19.92, 19.84, 20.12, 20.2, 20.36, 20.2] +365.64 +18.282 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 9003, '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': 20.59884262084961, 'TIME_S_1KI': 2.287997625330402, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 595.7279040813446, 'W': 24.476738666267384, 'J_1KI': 66.16993269813891, 'W_1KI': 2.7187313857900013, 'W_D': 6.194738666267384, 'J_D': 150.77084951162337, 'W_D_1KI': 0.6880749379392851, 'J_D_1KI': 0.07642729511710375} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_070.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_070.json new file mode 100644 index 0000000..a1f8bbd --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_070.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 10632, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 21.13743305206299, "TIME_S_1KI": 1.9880956595243593, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 577.3553030776978, "W": 23.727390421920692, "J_1KI": 54.30354618864728, "W_1KI": 2.231695863611803, "W_D": 5.278390421920694, "J_D": 128.438342675686, "W_D_1KI": 0.4964626055230149, "J_D_1KI": 0.04669512843519704} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_070.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_070.output new file mode 100644 index 0000000..80bc2f8 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_070.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_070.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 1.9750430583953857} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.2425, 0.1579, 0.4433, ..., 0.4904, 0.0503, 0.2838]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 1.9750430583953857 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 10632 -m matrices/as-caida_pruned/as-caida_G_070.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 21.13743305206299} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.2555, 0.5888, 0.5922, ..., 0.9980, 0.6558, 0.7472]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 21.13743305206299 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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.2555, 0.5888, 0.5922, ..., 0.9980, 0.6558, 0.7472]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 21.13743305206299 seconds + +[20.56, 20.44, 20.48, 20.48, 20.28, 20.28, 20.36, 20.28, 20.6, 20.6] +[20.6, 20.68, 20.76, 23.92, 25.08, 28.64, 29.68, 29.96, 27.36, 27.36, 24.44, 24.24, 24.44, 24.44, 24.2, 24.16, 24.12, 24.08, 23.8, 23.8, 23.68, 24.04, 24.12, 24.08] +24.33286142349243 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 10632, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 21.13743305206299, 'TIME_S_1KI': 1.9880956595243593, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 577.3553030776978, 'W': 23.727390421920692} +[20.56, 20.44, 20.48, 20.48, 20.28, 20.28, 20.36, 20.28, 20.6, 20.6, 20.08, 20.2, 20.64, 20.64, 20.88, 20.72, 20.56, 20.64, 20.64, 20.48] +368.97999999999996 +18.448999999999998 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 10632, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 21.13743305206299, 'TIME_S_1KI': 1.9880956595243593, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 577.3553030776978, 'W': 23.727390421920692, 'J_1KI': 54.30354618864728, 'W_1KI': 2.231695863611803, 'W_D': 5.278390421920694, 'J_D': 128.438342675686, 'W_D_1KI': 0.4964626055230149, 'J_D_1KI': 0.04669512843519704} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_075.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_075.json new file mode 100644 index 0000000..6f51493 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_075.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8584, "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": 20.15437150001526, "TIME_S_1KI": 2.3478997553605847, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 602.6740414428712, "W": 23.734224847984613, "J_1KI": 70.20899830415554, "W_1KI": 2.764937657034554, "W_D": 5.527224847984613, "J_D": 140.35069434261328, "W_D_1KI": 0.6438985144436874, "J_D_1KI": 0.07501147651953488} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_075.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_075.output new file mode 100644 index 0000000..d2e1801 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_075.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 2.446396589279175} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.6358, 0.8814, 0.7109, ..., 0.5208, 0.0272, 0.7061]) +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: 2.446396589279175 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8584 -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": 20.15437150001526} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.5045, 0.1890, 0.1651, ..., 0.8675, 0.9058, 0.0413]) +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: 20.15437150001526 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([0.5045, 0.1890, 0.1651, ..., 0.8675, 0.9058, 0.0413]) +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: 20.15437150001526 seconds + +[20.16, 20.08, 19.92, 20.24, 20.4, 20.36, 20.44, 20.68, 20.6, 20.36] +[20.48, 20.56, 21.16, 22.32, 22.32, 25.96, 27.12, 27.92, 27.96, 27.6, 24.84, 24.64, 24.44, 24.44, 24.76, 24.92, 24.92, 24.8, 24.8, 24.96, 25.0, 24.88, 25.04, 25.08, 24.64] +25.39261531829834 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8584, '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': 20.15437150001526, 'TIME_S_1KI': 2.3478997553605847, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 602.6740414428712, 'W': 23.734224847984613} +[20.16, 20.08, 19.92, 20.24, 20.4, 20.36, 20.44, 20.68, 20.6, 20.36, 20.04, 20.0, 20.0, 20.04, 20.12, 20.12, 20.36, 20.28, 20.2, 20.04] +364.14 +18.207 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8584, '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': 20.15437150001526, 'TIME_S_1KI': 2.3478997553605847, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 602.6740414428712, 'W': 23.734224847984613, 'J_1KI': 70.20899830415554, 'W_1KI': 2.764937657034554, 'W_D': 5.527224847984613, 'J_D': 140.35069434261328, 'W_D_1KI': 0.6438985144436874, 'J_D_1KI': 0.07501147651953488} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_080.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_080.json new file mode 100644 index 0000000..c495b17 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_080.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8794, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 23.002132892608643, "TIME_S_1KI": 2.615662143803575, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 576.0093463134767, "W": 23.661706671572514, "J_1KI": 65.5002668084463, "W_1KI": 2.6906648478021964, "W_D": 5.470706671572515, "J_D": 133.17628425979632, "W_D_1KI": 0.6220953686118393, "J_D_1KI": 0.07074088794767333} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_080.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_080.output new file mode 100644 index 0000000..3a349ec --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_080.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_080.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 2.387787103652954} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.1559, 0.4913, 0.6290, ..., 0.0122, 0.8386, 0.3778]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 2.387787103652954 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8794 -m matrices/as-caida_pruned/as-caida_G_080.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 23.002132892608643} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.4057, 0.4897, 0.2712, ..., 0.1739, 0.4440, 0.2669]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 23.002132892608643 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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.4057, 0.4897, 0.2712, ..., 0.1739, 0.4440, 0.2669]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 23.002132892608643 seconds + +[20.68, 20.36, 20.16, 20.2, 20.08, 20.08, 20.2, 20.2, 20.24, 20.36] +[20.36, 20.48, 20.56, 21.56, 22.68, 25.84, 27.12, 27.52, 27.12, 24.92, 24.92, 25.04, 24.88, 24.84, 25.12, 25.16, 25.16, 25.24, 25.52, 25.48, 25.32, 25.08, 25.08, 25.12] +24.343524932861328 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8794, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 23.002132892608643, 'TIME_S_1KI': 2.615662143803575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 576.0093463134767, 'W': 23.661706671572514} +[20.68, 20.36, 20.16, 20.2, 20.08, 20.08, 20.2, 20.2, 20.24, 20.36, 20.16, 20.2, 20.28, 20.28, 20.24, 20.32, 20.12, 20.04, 20.16, 20.12] +363.82 +18.191 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8794, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 23.002132892608643, 'TIME_S_1KI': 2.615662143803575, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 576.0093463134767, 'W': 23.661706671572514, 'J_1KI': 65.5002668084463, 'W_1KI': 2.6906648478021964, 'W_D': 5.470706671572515, 'J_D': 133.17628425979632, 'W_D_1KI': 0.6220953686118393, 'J_D_1KI': 0.07074088794767333} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_085.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_085.json new file mode 100644 index 0000000..306bffd --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_085.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8691, "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": 20.764369010925293, "TIME_S_1KI": 2.3891806479030366, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 571.7078634071349, "W": 23.505529827290687, "J_1KI": 65.78159744645437, "W_1KI": 2.704582881980288, "W_D": 5.296529827290687, "J_D": 128.823633131504, "W_D_1KI": 0.6094269735692885, "J_D_1KI": 0.07012161702557686} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_085.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_085.output new file mode 100644 index 0000000..71ac056 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_085.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 2.4162235260009766} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.9352, 0.7507, 0.1402, ..., 0.1494, 0.4151, 0.0213]) +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: 2.4162235260009766 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8691 -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": 20.764369010925293} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.9390, 0.2795, 0.0601, ..., 0.8952, 0.5532, 0.1504]) +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: 20.764369010925293 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([0.9390, 0.2795, 0.0601, ..., 0.8952, 0.5532, 0.1504]) +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: 20.764369010925293 seconds + +[20.4, 20.4, 20.24, 20.32, 20.4, 20.36, 20.2, 20.12, 20.08, 20.08] +[20.08, 20.12, 20.12, 22.72, 23.68, 27.52, 28.64, 27.72, 27.68, 24.8, 24.72, 24.36, 24.32, 24.32, 24.28, 24.24, 24.24, 24.6, 24.52, 24.8, 24.76, 24.56, 24.72, 24.6] +24.32227087020874 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8691, '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': 20.764369010925293, 'TIME_S_1KI': 2.3891806479030366, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 571.7078634071349, 'W': 23.505529827290687} +[20.4, 20.4, 20.24, 20.32, 20.4, 20.36, 20.2, 20.12, 20.08, 20.08, 20.12, 20.08, 20.08, 20.24, 20.4, 20.48, 20.24, 20.12, 20.08, 20.08] +364.18 +18.209 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8691, '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': 20.764369010925293, 'TIME_S_1KI': 2.3891806479030366, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 571.7078634071349, 'W': 23.505529827290687, 'J_1KI': 65.78159744645437, 'W_1KI': 2.704582881980288, 'W_D': 5.296529827290687, 'J_D': 128.823633131504, 'W_D_1KI': 0.6094269735692885, 'J_D_1KI': 0.07012161702557686} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_090.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_090.json new file mode 100644 index 0000000..7209267 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_090.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8443, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 20.46173071861267, "TIME_S_1KI": 2.423514238850251, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 617.1725379943847, "W": 25.383726169427987, "J_1KI": 73.09872533393163, "W_1KI": 3.0064818393258306, "W_D": 7.037726169427991, "J_D": 171.11322792816162, "W_D_1KI": 0.8335575233244097, "J_D_1KI": 0.09872764696487145} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_090.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_090.output new file mode 100644 index 0000000..b9ad71a --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_090.output @@ -0,0 +1,68 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_090.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 2.4871225357055664} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.3922, 0.8557, 0.2310, ..., 0.9809, 0.9954, 0.7931]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 2.4871225357055664 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8443 -m matrices/as-caida_pruned/as-caida_G_090.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 20.46173071861267} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.4649, 0.8948, 0.1887, ..., 0.0336, 0.7739, 0.8142]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 20.46173071861267 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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.4649, 0.8948, 0.1887, ..., 0.0336, 0.7739, 0.8142]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 20.46173071861267 seconds + +[20.36, 20.16, 20.56, 20.64, 20.64, 20.48, 20.44, 20.52, 20.4, 20.52] +[20.56, 20.24, 20.68, 25.32, 29.76, 33.36, 34.24, 34.24, 31.84, 30.84, 24.56, 24.52, 24.64, 25.0, 25.0, 25.08, 25.36, 25.28, 25.4, 25.4, 25.4, 25.48, 25.04, 24.84] +24.313709259033203 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8443, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 20.46173071861267, 'TIME_S_1KI': 2.423514238850251, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 617.1725379943847, 'W': 25.383726169427987} +[20.36, 20.16, 20.56, 20.64, 20.64, 20.48, 20.44, 20.52, 20.4, 20.52, 20.2, 20.24, 20.44, 20.56, 20.56, 20.44, 20.2, 20.08, 20.0, 20.04] +366.91999999999996 +18.345999999999997 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8443, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 20.46173071861267, 'TIME_S_1KI': 2.423514238850251, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 617.1725379943847, 'W': 25.383726169427987, 'J_1KI': 73.09872533393163, 'W_1KI': 3.0064818393258306, 'W_D': 7.037726169427991, 'J_D': 171.11322792816162, 'W_D_1KI': 0.8335575233244097, 'J_D_1KI': 0.09872764696487145} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_095.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_095.json new file mode 100644 index 0000000..7e43f41 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_095.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8506, "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": 20.99284601211548, "TIME_S_1KI": 2.4680044688590965, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 586.6492254638672, "W": 24.123598992038087, "J_1KI": 68.96887202725925, "W_1KI": 2.8360685389181852, "W_D": 5.700598992038092, "J_D": 138.62989450550094, "W_D_1KI": 0.670185632734316, "J_D_1KI": 0.07878975226126451} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_095.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_095.output new file mode 100644 index 0000000..4c7e24b --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_095.output @@ -0,0 +1,68 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 2.4687836170196533} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.5566, 0.3690, 0.1680, ..., 0.0525, 0.8406, 0.9386]) +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: 2.4687836170196533 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8506 -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": 20.99284601211548} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.9560, 0.2133, 0.6850, ..., 0.4190, 0.4977, 0.6761]) +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: 20.99284601211548 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([0.9560, 0.2133, 0.6850, ..., 0.4190, 0.4977, 0.6761]) +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: 20.99284601211548 seconds + +[20.24, 20.32, 20.32, 20.32, 20.28, 20.24, 20.4, 20.32, 20.36, 20.48] +[20.36, 20.24, 21.48, 22.76, 26.32, 27.48, 27.48, 28.48, 27.92, 27.44, 25.08, 25.08, 25.36, 25.36, 25.28, 24.84, 24.92, 24.8, 24.8, 25.24, 25.32, 25.2, 25.16, 24.96] +24.31847858428955 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8506, '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': 20.99284601211548, 'TIME_S_1KI': 2.4680044688590965, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 586.6492254638672, 'W': 24.123598992038087} +[20.24, 20.32, 20.32, 20.32, 20.28, 20.24, 20.4, 20.32, 20.36, 20.48, 20.56, 20.64, 20.76, 20.56, 20.6, 20.76, 20.72, 20.48, 20.48, 20.52] +368.4599999999999 +18.422999999999995 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8506, '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': 20.99284601211548, 'TIME_S_1KI': 2.4680044688590965, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 586.6492254638672, 'W': 24.123598992038087, 'J_1KI': 68.96887202725925, 'W_1KI': 2.8360685389181852, 'W_D': 5.700598992038092, 'J_D': 138.62989450550094, 'W_D_1KI': 0.670185632734316, 'J_D_1KI': 0.07878975226126451} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_100.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_100.json new file mode 100644 index 0000000..05f4e47 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_100.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8467, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 20.76715898513794, "TIME_S_1KI": 2.452717489682053, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 624.7324462318422, "W": 24.68048649134715, "J_1KI": 73.78439190171753, "W_1KI": 2.914903329555586, "W_D": 6.425486491347147, "J_D": 162.64711375832567, "W_D_1KI": 0.7588858499287997, "J_D_1KI": 0.0896286583121294} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_100.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_100.output new file mode 100644 index 0000000..7d6d707 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_100.output @@ -0,0 +1,68 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_100.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 2.4800076484680176} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.5380, 0.6798, 0.7075, ..., 0.5675, 0.9961, 0.3743]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 2.4800076484680176 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8467 -m matrices/as-caida_pruned/as-caida_G_100.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 20.76715898513794} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.6117, 0.2465, 0.6631, ..., 0.0512, 0.3459, 0.0433]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 20.76715898513794 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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.6117, 0.2465, 0.6631, ..., 0.0512, 0.3459, 0.0433]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 20.76715898513794 seconds + +[20.28, 20.28, 20.04, 19.88, 19.64, 19.64, 19.8, 20.08, 20.2, 20.04] +[20.08, 20.08, 23.48, 25.8, 25.8, 32.84, 33.72, 34.6, 30.6, 28.72, 23.88, 23.84, 24.0, 24.24, 24.48, 24.48, 24.48, 24.6, 24.52, 24.24, 24.28, 24.24, 24.16, 24.04, 24.08] +25.312809228897095 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8467, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 20.76715898513794, 'TIME_S_1KI': 2.452717489682053, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 624.7324462318422, 'W': 24.68048649134715} +[20.28, 20.28, 20.04, 19.88, 19.64, 19.64, 19.8, 20.08, 20.2, 20.04, 20.72, 20.56, 20.68, 20.6, 20.48, 20.48, 20.52, 20.8, 20.56, 20.68] +365.1 +18.255000000000003 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8467, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 20.76715898513794, 'TIME_S_1KI': 2.452717489682053, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 624.7324462318422, 'W': 24.68048649134715, 'J_1KI': 73.78439190171753, 'W_1KI': 2.914903329555586, 'W_D': 6.425486491347147, 'J_D': 162.64711375832567, 'W_D_1KI': 0.7588858499287997, 'J_D_1KI': 0.0896286583121294} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_105.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_105.json new file mode 100644 index 0000000..dfcb24a --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_105.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8307, "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": 20.873857975006104, "TIME_S_1KI": 2.5128034157946435, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 597.9551256275176, "W": 23.607426160996845, "J_1KI": 71.98207844318257, "W_1KI": 2.8418714531114535, "W_D": 5.241426160996845, "J_D": 132.76066680002205, "W_D_1KI": 0.6309649886838623, "J_D_1KI": 0.0759558190301989} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_105.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_105.output new file mode 100644 index 0000000..6c0f8cf --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_105.output @@ -0,0 +1,68 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 2.527773141860962} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.1824, 0.7823, 0.6330, ..., 0.4083, 0.5524, 0.9351]) +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: 2.527773141860962 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8307 -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": 20.873857975006104} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.9834, 0.9632, 0.1557, ..., 0.5772, 0.3745, 0.8872]) +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: 20.873857975006104 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([0.9834, 0.9632, 0.1557, ..., 0.5772, 0.3745, 0.8872]) +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: 20.873857975006104 seconds + +[20.96, 20.88, 21.0, 21.72, 20.72, 20.36, 20.32, 20.28, 20.12, 20.32] +[20.4, 20.48, 21.0, 22.72, 23.48, 27.08, 28.0, 28.0, 27.4, 27.12, 24.52, 24.36, 24.36, 24.28, 24.28, 24.24, 24.28, 24.28, 24.32, 24.32, 24.4, 24.6, 24.68, 24.96, 24.84] +25.329111337661743 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8307, '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': 20.873857975006104, 'TIME_S_1KI': 2.5128034157946435, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 597.9551256275176, 'W': 23.607426160996845} +[20.96, 20.88, 21.0, 21.72, 20.72, 20.36, 20.32, 20.28, 20.12, 20.32, 20.4, 20.44, 20.0, 19.88, 19.84, 19.88, 20.16, 20.32, 20.32, 20.48] +367.32 +18.366 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8307, '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': 20.873857975006104, 'TIME_S_1KI': 2.5128034157946435, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 597.9551256275176, 'W': 23.607426160996845, 'J_1KI': 71.98207844318257, 'W_1KI': 2.8418714531114535, 'W_D': 5.241426160996845, 'J_D': 132.76066680002205, 'W_D_1KI': 0.6309649886838623, 'J_D_1KI': 0.0759558190301989} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_110.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_110.json new file mode 100644 index 0000000..1e98f60 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_110.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8349, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 21.81509804725647, "TIME_S_1KI": 2.6128995145833596, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 606.0642761802675, "W": 24.910639760816423, "J_1KI": 72.59124160741017, "W_1KI": 2.9836674764422595, "W_D": 6.720639760816418, "J_D": 163.5100387310982, "W_D_1KI": 0.8049634400307124, "J_D_1KI": 0.0964143538185067} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_110.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_110.output new file mode 100644 index 0000000..5717556 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_110.output @@ -0,0 +1,68 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_110.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 2.5151610374450684} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.3789, 0.4285, 0.7113, ..., 0.5357, 0.8339, 0.3006]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 2.5151610374450684 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8349 -m matrices/as-caida_pruned/as-caida_G_110.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 21.81509804725647} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.7764, 0.6906, 0.1766, ..., 0.9379, 0.5296, 0.5222]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 21.81509804725647 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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.7764, 0.6906, 0.1766, ..., 0.9379, 0.5296, 0.5222]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 21.81509804725647 seconds + +[19.96, 19.8, 19.72, 19.72, 20.04, 20.36, 20.32, 20.56, 20.8, 20.8] +[20.56, 20.56, 20.2, 23.44, 25.88, 32.52, 33.72, 34.12, 30.6, 28.04, 25.16, 25.24, 24.88, 24.88, 24.96, 24.92, 25.04, 25.28, 25.08, 25.04, 25.16, 25.2, 24.96, 25.2] +24.329534769058228 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8349, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 21.81509804725647, 'TIME_S_1KI': 2.6128995145833596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 606.0642761802675, 'W': 24.910639760816423} +[19.96, 19.8, 19.72, 19.72, 20.04, 20.36, 20.32, 20.56, 20.8, 20.8, 20.36, 20.2, 20.2, 20.2, 20.36, 20.28, 20.32, 20.24, 20.12, 20.0] +363.80000000000007 +18.190000000000005 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8349, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 21.81509804725647, 'TIME_S_1KI': 2.6128995145833596, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 606.0642761802675, 'W': 24.910639760816423, 'J_1KI': 72.59124160741017, 'W_1KI': 2.9836674764422595, 'W_D': 6.720639760816418, 'J_D': 163.5100387310982, 'W_D_1KI': 0.8049634400307124, 'J_D_1KI': 0.0964143538185067} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_115.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_115.json new file mode 100644 index 0000000..10adf93 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_115.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8049, "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": 20.547222137451172, "TIME_S_1KI": 2.552767068884479, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 603.5182037734984, "W": 24.810850099333518, "J_1KI": 74.98051978798588, "W_1KI": 3.0824760963266886, "W_D": 6.508850099333518, "J_D": 158.32627680444708, "W_D_1KI": 0.8086532611918894, "J_D_1KI": 0.1004663015519803} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_115.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_115.output new file mode 100644 index 0000000..c1fb25c --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_115.output @@ -0,0 +1,68 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -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": 2.608731508255005} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.1425, 0.7093, 0.4708, ..., 0.9603, 0.3645, 0.2733]) +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: 2.608731508255005 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8049 -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": 20.547222137451172} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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([0.9041, 0.6122, 0.9574, ..., 0.6613, 0.0965, 0.8368]) +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: 20.547222137451172 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([0.9041, 0.6122, 0.9574, ..., 0.6613, 0.0965, 0.8368]) +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: 20.547222137451172 seconds + +[20.2, 20.16, 20.0, 20.24, 20.2, 20.28, 20.32, 20.36, 20.36, 20.4] +[20.4, 20.32, 20.04, 23.64, 25.32, 32.2, 33.56, 33.8, 30.52, 27.4, 24.68, 24.64, 24.64, 24.64, 25.0, 24.92, 24.96, 25.2, 25.28, 25.28, 25.52, 25.56, 25.48, 25.12] +24.324769258499146 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8049, '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': 20.547222137451172, 'TIME_S_1KI': 2.552767068884479, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 603.5182037734984, 'W': 24.810850099333518} +[20.2, 20.16, 20.0, 20.24, 20.2, 20.28, 20.32, 20.36, 20.36, 20.4, 20.68, 20.76, 20.72, 20.72, 20.36, 20.2, 20.16, 20.24, 20.2, 20.24] +366.03999999999996 +18.302 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8049, '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': 20.547222137451172, 'TIME_S_1KI': 2.552767068884479, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 603.5182037734984, 'W': 24.810850099333518, 'J_1KI': 74.98051978798588, 'W_1KI': 3.0824760963266886, 'W_D': 6.508850099333518, 'J_D': 158.32627680444708, 'W_D_1KI': 0.8086532611918894, 'J_D_1KI': 0.1004663015519803} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_120.json b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_120.json new file mode 100644 index 0000000..3be5033 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_120.json @@ -0,0 +1 @@ +{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8105, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 20.750195264816284, "TIME_S_1KI": 2.560172148650991, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 630.7465198040009, "W": 24.92526992228312, "J_1KI": 77.8219025051204, "W_1KI": 3.075295487018275, "W_D": 6.416269922283121, "J_D": 162.3669447200298, "W_D_1KI": 0.7916434203927355, "J_D_1KI": 0.09767346334271874} diff --git a/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_120.output b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_120.output new file mode 100644 index 0000000..2d0e45a --- /dev/null +++ b/pytorch/output_as-caida_maxcore/altra_max_csr_20_10_10_as-caida_G_120.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 1000 -m matrices/as-caida_pruned/as-caida_G_120.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 2.724186420440674} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.1163, 0.7095, 0.6781, ..., 0.7446, 0.8524, 0.8056]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 2.724186420440674 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 7708 -m matrices/as-caida_pruned/as-caida_G_120.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 19.970391988754272} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.8144, 0.1674, 0.8184, ..., 0.8996, 0.3640, 0.9980]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 19.970391988754272 seconds + +['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py suitesparse csr 8105 -m matrices/as-caida_pruned/as-caida_G_120.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 20.750195264816284} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.2321, 0.6812, 0.5513, ..., 0.9888, 0.4472, 0.0437]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 20.750195264816284 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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.2321, 0.6812, 0.5513, ..., 0.9888, 0.4472, 0.0437]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 20.750195264816284 seconds + +[19.92, 20.0, 20.36, 20.36, 20.64, 20.92, 21.12, 21.0, 20.76, 20.36] +[20.44, 20.28, 23.0, 24.16, 31.28, 32.24, 32.24, 33.32, 30.72, 30.24, 24.48, 24.64, 24.6, 24.64, 24.68, 24.68, 24.48, 24.4, 24.4, 24.48, 24.56, 24.28, 24.48, 24.56, 24.44] +25.305504083633423 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8105, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 20.750195264816284, 'TIME_S_1KI': 2.560172148650991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 630.7465198040009, 'W': 24.92526992228312} +[19.92, 20.0, 20.36, 20.36, 20.64, 20.92, 21.12, 21.0, 20.76, 20.36, 20.04, 20.08, 20.08, 20.4, 20.6, 20.76, 20.76, 20.76, 21.0, 20.84] +370.18 +18.509 +{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8105, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 20.750195264816284, 'TIME_S_1KI': 2.560172148650991, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 630.7465198040009, 'W': 24.92526992228312, 'J_1KI': 77.8219025051204, 'W_1KI': 3.075295487018275, 'W_D': 6.416269922283121, 'J_D': 162.3669447200298, 'W_D_1KI': 0.7916434203927355, 'J_D_1KI': 0.09767346334271874} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_005.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_005.json new file mode 100644 index 0000000..637e99c --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_005.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 276410, "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": 25.263006687164307, "TIME_S_1KI": 0.09139686222337942, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2936.388348619938, "W": 107.27, "J_1KI": 10.623307219781983, "W_1KI": 0.388082920299555, "W_D": 70.633, "J_D": 1933.494157062292, "W_D_1KI": 0.2555370645056257, "J_D_1KI": 0.0009244855993112612} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_005.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_005.output new file mode 100644 index 0000000..b8aab2f --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_005.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.09953641891479492} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9728, 0.9533, 0.8589, ..., 0.8433, 0.2350, 0.3886]) +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: 0.09953641891479492 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '210978', '-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": 16.028836250305176} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0730, 0.6524, 0.0309, ..., 0.2548, 0.9366, 0.1429]) +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: 16.028836250305176 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '276410', '-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": 25.263006687164307} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8011, 0.5871, 0.2879, ..., 0.3075, 0.3462, 0.5441]) +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: 25.263006687164307 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([0.8011, 0.5871, 0.2879, ..., 0.3075, 0.3462, 0.5441]) +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: 25.263006687164307 seconds + +[40.1, 39.29, 39.65, 39.22, 39.19, 39.86, 39.23, 39.23, 39.15, 39.14] +[107.27] +27.373807668685913 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 276410, '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': 25.263006687164307, 'TIME_S_1KI': 0.09139686222337942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2936.388348619938, 'W': 107.27} +[40.1, 39.29, 39.65, 39.22, 39.19, 39.86, 39.23, 39.23, 39.15, 39.14, 39.83, 39.44, 41.31, 44.88, 39.26, 39.36, 55.72, 39.24, 39.62, 39.11] +732.74 +36.637 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 276410, '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': 25.263006687164307, 'TIME_S_1KI': 0.09139686222337942, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2936.388348619938, 'W': 107.27, 'J_1KI': 10.623307219781983, 'W_1KI': 0.388082920299555, 'W_D': 70.633, 'J_D': 1933.494157062292, 'W_D_1KI': 0.2555370645056257, 'J_D_1KI': 0.0009244855993112612} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_010.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_010.json new file mode 100644 index 0000000..4ad2bdc --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_010.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 203597, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 21.505177974700928, "TIME_S_1KI": 0.10562620261939482, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2116.265174906254, "W": 105.79, "J_1KI": 10.394382898108782, "W_1KI": 0.5196049057697314, "W_D": 69.99100000000001, "J_D": 1400.1277611954215, "W_D_1KI": 0.3437722559762669, "J_D_1KI": 0.0016884937203213552} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_010.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_010.output new file mode 100644 index 0000000..7caef0a --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_010.output @@ -0,0 +1,65 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.10314488410949707} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 28, 28, ..., 74993, 74993, 74994]), + col_indices=tensor([ 1040, 2020, 2054, ..., 160, 160, 12170]), + values=tensor([1., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=74994, layout=torch.sparse_csr) +tensor([0.5444, 0.3450, 0.0743, ..., 0.3559, 0.8180, 0.5488]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_010 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 74994 +Density: 7.616375021864427e-05 +Time: 0.10314488410949707 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '203597', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 21.505177974700928} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 28, 28, ..., 74993, 74993, 74994]), + col_indices=tensor([ 1040, 2020, 2054, ..., 160, 160, 12170]), + values=tensor([1., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=74994, layout=torch.sparse_csr) +tensor([0.1211, 0.6988, 0.4577, ..., 0.8664, 0.9808, 0.0173]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_010 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 74994 +Density: 7.616375021864427e-05 +Time: 21.505177974700928 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, 28, 28, ..., 74993, 74993, 74994]), + col_indices=tensor([ 1040, 2020, 2054, ..., 160, 160, 12170]), + values=tensor([1., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=74994, layout=torch.sparse_csr) +tensor([0.1211, 0.6988, 0.4577, ..., 0.8664, 0.9808, 0.0173]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_010 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 74994 +Density: 7.616375021864427e-05 +Time: 21.505177974700928 seconds + +[40.77, 40.16, 39.31, 39.46, 39.4, 39.45, 39.01, 40.43, 39.43, 39.2] +[105.79] +20.00439715385437 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 203597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 21.505177974700928, 'TIME_S_1KI': 0.10562620261939482, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2116.265174906254, 'W': 105.79} +[40.77, 40.16, 39.31, 39.46, 39.4, 39.45, 39.01, 40.43, 39.43, 39.2, 39.65, 39.75, 38.91, 45.15, 39.07, 39.19, 39.47, 39.23, 39.35, 38.8] +715.98 +35.799 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 203597, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 21.505177974700928, 'TIME_S_1KI': 0.10562620261939482, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2116.265174906254, 'W': 105.79, 'J_1KI': 10.394382898108782, 'W_1KI': 0.5196049057697314, 'W_D': 69.99100000000001, 'J_D': 1400.1277611954215, 'W_D_1KI': 0.3437722559762669, 'J_D_1KI': 0.0016884937203213552} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_015.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_015.json new file mode 100644 index 0000000..4bc7029 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_015.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 238527, "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": 21.601000785827637, "TIME_S_1KI": 0.0905599818294266, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2453.5633573436735, "W": 107.46999999999998, "J_1KI": 10.286312901028703, "W_1KI": 0.4505569600087201, "W_D": 71.32874999999999, "J_D": 1628.4507985961434, "W_D_1KI": 0.2990384736319158, "J_D_1KI": 0.0012536881511607315} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_015.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_015.output new file mode 100644 index 0000000..a810e79 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_015.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.11880779266357422} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1155, 0.9087, 0.1286, ..., 0.0633, 0.1640, 0.2660]) +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: 0.11880779266357422 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '176756', '-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": 15.56164002418518} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1012, 0.9992, 0.7546, ..., 0.1559, 0.7607, 0.7528]) +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: 15.56164002418518 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '238527', '-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": 21.601000785827637} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3942, 0.0726, 0.4805, ..., 0.0241, 0.4217, 0.4391]) +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: 21.601000785827637 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([0.3942, 0.0726, 0.4805, ..., 0.0241, 0.4217, 0.4391]) +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: 21.601000785827637 seconds + +[40.23, 39.56, 39.22, 39.64, 39.02, 39.24, 39.06, 39.38, 39.16, 39.4] +[107.47] +22.83021640777588 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 238527, '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': 21.601000785827637, 'TIME_S_1KI': 0.0905599818294266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2453.5633573436735, 'W': 107.46999999999998} +[40.23, 39.56, 39.22, 39.64, 39.02, 39.24, 39.06, 39.38, 39.16, 39.4, 41.7, 38.99, 39.59, 53.73, 39.02, 39.01, 39.05, 38.86, 39.89, 39.48] +722.825 +36.14125 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 238527, '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': 21.601000785827637, 'TIME_S_1KI': 0.0905599818294266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2453.5633573436735, 'W': 107.46999999999998, 'J_1KI': 10.286312901028703, 'W_1KI': 0.4505569600087201, 'W_D': 71.32874999999999, 'J_D': 1628.4507985961434, 'W_D_1KI': 0.2990384736319158, 'J_D_1KI': 0.0012536881511607315} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_020.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_020.json new file mode 100644 index 0000000..d0ebf0d --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_020.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 252661, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 22.093090534210205, "TIME_S_1KI": 0.08744163339102673, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2827.9676496505735, "W": 106.8, "J_1KI": 11.19273512592198, "W_1KI": 0.42270077297248093, "W_D": 71.72475, "J_D": 1899.2066730269194, "W_D_1KI": 0.28387740886009316, "J_D_1KI": 0.0011235505632451908} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_020.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_020.output new file mode 100644 index 0000000..c7d6a8a --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_020.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.10638713836669922} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.7616, 0.6085, 0.4474, ..., 0.0727, 0.0513, 0.0850]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 0.10638713836669922 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '197392', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 16.40624761581421} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.3330, 0.2122, 0.6427, ..., 0.2808, 0.4380, 0.9268]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 16.40624761581421 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '252661', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 22.093090534210205} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.8862, 0.2712, 0.1599, ..., 0.5989, 0.7417, 0.3691]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 22.093090534210205 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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.8862, 0.2712, 0.1599, ..., 0.5989, 0.7417, 0.3691]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 22.093090534210205 seconds + +[39.71, 38.73, 38.66, 38.74, 38.71, 38.81, 39.09, 38.65, 39.04, 38.91] +[106.8] +26.479097843170166 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 252661, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 22.093090534210205, 'TIME_S_1KI': 0.08744163339102673, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2827.9676496505735, 'W': 106.8} +[39.71, 38.73, 38.66, 38.74, 38.71, 38.81, 39.09, 38.65, 39.04, 38.91, 39.78, 38.72, 39.56, 38.58, 39.39, 39.1, 38.77, 39.29, 39.08, 38.77] +701.505 +35.07525 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 252661, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 22.093090534210205, 'TIME_S_1KI': 0.08744163339102673, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2827.9676496505735, 'W': 106.8, 'J_1KI': 11.19273512592198, 'W_1KI': 0.42270077297248093, 'W_D': 71.72475, 'J_D': 1899.2066730269194, 'W_D_1KI': 0.28387740886009316, 'J_D_1KI': 0.0011235505632451908} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_025.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_025.json new file mode 100644 index 0000000..ae1d660 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_025.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 240875, "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": 48.700708627700806, "TIME_S_1KI": 0.20218249560021093, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2510.559543380737, "W": 108.38, "J_1KI": 10.422665462919511, "W_1KI": 0.4499429164504411, "W_D": 72.928, "J_D": 1689.3346224365234, "W_D_1KI": 0.3027628437986507, "J_D_1KI": 0.0012569292944417257} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_025.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_025.output new file mode 100644 index 0000000..4cc18af --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_025.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 2.1525959968566895} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1548, 0.6807, 0.1439, ..., 0.0147, 0.6028, 0.1765]) +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: 2.1525959968566895 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '9755', '-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": 0.850459098815918} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3242, 0.6133, 0.1194, ..., 0.5981, 0.5897, 0.0971]) +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: 0.850459098815918 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '240875', '-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": 48.700708627700806} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6774, 0.9329, 0.5853, ..., 0.8274, 0.5489, 0.8291]) +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: 48.700708627700806 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([0.6774, 0.9329, 0.5853, ..., 0.8274, 0.5489, 0.8291]) +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: 48.700708627700806 seconds + +[40.43, 39.93, 39.14, 39.8, 38.99, 39.48, 39.06, 38.95, 39.29, 38.89] +[108.38] +23.164417266845703 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 240875, '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': 48.700708627700806, 'TIME_S_1KI': 0.20218249560021093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2510.559543380737, 'W': 108.38} +[40.43, 39.93, 39.14, 39.8, 38.99, 39.48, 39.06, 38.95, 39.29, 38.89, 40.82, 39.02, 39.15, 39.35, 39.01, 39.32, 39.03, 39.3, 39.12, 42.06] +709.04 +35.452 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 240875, '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': 48.700708627700806, 'TIME_S_1KI': 0.20218249560021093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2510.559543380737, 'W': 108.38, 'J_1KI': 10.422665462919511, 'W_1KI': 0.4499429164504411, 'W_D': 72.928, 'J_D': 1689.3346224365234, 'W_D_1KI': 0.3027628437986507, 'J_D_1KI': 0.0012569292944417257} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_030.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_030.json new file mode 100644 index 0000000..ebe6ab4 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_030.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 245946, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 21.173834562301636, "TIME_S_1KI": 0.08609139633212833, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2654.1946601867676, "W": 108.5, "J_1KI": 10.791778114654305, "W_1KI": 0.4411537491969782, "W_D": 73.0335, "J_D": 1786.5910204124452, "W_D_1KI": 0.29694933034080656, "J_D_1KI": 0.001207376132731602} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_030.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_030.output new file mode 100644 index 0000000..f65b12d --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_030.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.11014795303344727} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.7332, 0.7503, 0.1338, ..., 0.4921, 0.4099, 0.1438]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 0.11014795303344727 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '190652', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 16.278696298599243} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.4532, 0.7021, 0.2379, ..., 0.4729, 0.4308, 0.9482]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 16.278696298599243 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '245946', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 21.173834562301636} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.3998, 0.0719, 0.5574, ..., 0.0984, 0.7335, 0.9981]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 21.173834562301636 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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.3998, 0.0719, 0.5574, ..., 0.0984, 0.7335, 0.9981]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 21.173834562301636 seconds + +[40.2, 39.33, 39.22, 39.59, 39.19, 39.41, 39.13, 39.18, 39.7, 39.19] +[108.5] +24.462623596191406 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 245946, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 21.173834562301636, 'TIME_S_1KI': 0.08609139633212833, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2654.1946601867676, 'W': 108.5} +[40.2, 39.33, 39.22, 39.59, 39.19, 39.41, 39.13, 39.18, 39.7, 39.19, 39.75, 39.67, 39.11, 39.3, 39.17, 39.45, 39.49, 39.54, 39.42, 39.72] +709.3299999999999 +35.466499999999996 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 245946, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 21.173834562301636, 'TIME_S_1KI': 0.08609139633212833, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2654.1946601867676, 'W': 108.5, 'J_1KI': 10.791778114654305, 'W_1KI': 0.4411537491969782, 'W_D': 73.0335, 'J_D': 1786.5910204124452, 'W_D_1KI': 0.29694933034080656, 'J_D_1KI': 0.001207376132731602} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_035.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_035.json new file mode 100644 index 0000000..d978ca7 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_035.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 235955, "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": 21.57011389732361, "TIME_S_1KI": 0.09141621875918547, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2532.983486351967, "W": 108.50999999999999, "J_1KI": 10.735027807641146, "W_1KI": 0.459875823779958, "W_D": 72.94724999999998, "J_D": 1702.8308877042527, "W_D_1KI": 0.3091574664660634, "J_D_1KI": 0.0013102390984131016} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_035.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_035.output new file mode 100644 index 0000000..2a7445c --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_035.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.10548853874206543} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7539, 0.5110, 0.4867, ..., 0.4276, 0.9205, 0.2155]) +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: 0.10548853874206543 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '199073', '-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": 17.71744680404663} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3160, 0.4163, 0.7745, ..., 0.9698, 0.5064, 0.9398]) +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: 17.71744680404663 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '235955', '-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": 21.57011389732361} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4189, 0.3756, 0.7284, ..., 0.6039, 0.3176, 0.7868]) +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: 21.57011389732361 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([0.4189, 0.3756, 0.7284, ..., 0.6039, 0.3176, 0.7868]) +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: 21.57011389732361 seconds + +[40.52, 39.16, 39.66, 39.15, 39.15, 39.09, 39.3, 39.32, 39.15, 44.41] +[108.51] +23.343318462371826 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 235955, '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': 21.57011389732361, 'TIME_S_1KI': 0.09141621875918547, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2532.983486351967, 'W': 108.50999999999999} +[40.52, 39.16, 39.66, 39.15, 39.15, 39.09, 39.3, 39.32, 39.15, 44.41, 39.78, 39.5, 39.15, 38.99, 39.53, 38.97, 39.96, 38.99, 40.12, 39.42] +711.2550000000001 +35.56275000000001 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 235955, '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': 21.57011389732361, 'TIME_S_1KI': 0.09141621875918547, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2532.983486351967, 'W': 108.50999999999999, 'J_1KI': 10.735027807641146, 'W_1KI': 0.459875823779958, 'W_D': 72.94724999999998, 'J_D': 1702.8308877042527, 'W_D_1KI': 0.3091574664660634, 'J_D_1KI': 0.0013102390984131016} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_040.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_040.json new file mode 100644 index 0000000..dfb4d91 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_040.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 246041, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 21.844791889190674, "TIME_S_1KI": 0.08878516950098021, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2588.3357152462004, "W": 108.77999999999999, "J_1KI": 10.519936576612029, "W_1KI": 0.44212143504537854, "W_D": 73.46974999999999, "J_D": 1748.155707990527, "W_D_1KI": 0.29860775236647547, "J_D_1KI": 0.0012136503768334361} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_040.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_040.output new file mode 100644 index 0000000..a6359f7 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_040.output @@ -0,0 +1,105 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.10488057136535645} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.6314, 0.4358, 0.9289, ..., 0.8653, 0.8137, 0.1981]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 0.10488057136535645 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '200227', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 18.238918781280518} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.2930, 0.0363, 0.8927, ..., 0.0958, 0.8570, 0.3046]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 18.238918781280518 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '230538', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 19.67674446105957} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.6913, 0.2101, 0.6607, ..., 0.9927, 0.2277, 0.7104]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 19.67674446105957 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '246041', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 21.844791889190674} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.8541, 0.2196, 0.7858, ..., 0.4806, 0.8499, 0.4712]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 21.844791889190674 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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.8541, 0.2196, 0.7858, ..., 0.4806, 0.8499, 0.4712]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 21.844791889190674 seconds + +[40.03, 38.91, 40.66, 39.33, 39.14, 38.85, 39.49, 39.08, 39.33, 38.84] +[108.78] +23.79422426223755 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 246041, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 21.844791889190674, 'TIME_S_1KI': 0.08878516950098021, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2588.3357152462004, 'W': 108.77999999999999} +[40.03, 38.91, 40.66, 39.33, 39.14, 38.85, 39.49, 39.08, 39.33, 38.84, 39.77, 38.86, 39.28, 38.9, 38.94, 38.78, 38.83, 39.29, 39.53, 39.37] +706.2049999999999 +35.310249999999996 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 246041, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 21.844791889190674, 'TIME_S_1KI': 0.08878516950098021, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2588.3357152462004, 'W': 108.77999999999999, 'J_1KI': 10.519936576612029, 'W_1KI': 0.44212143504537854, 'W_D': 73.46974999999999, 'J_D': 1748.155707990527, 'W_D_1KI': 0.29860775236647547, 'J_D_1KI': 0.0012136503768334361} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_045.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_045.json new file mode 100644 index 0000000..40924c0 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_045.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 243173, "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": 21.187162399291992, "TIME_S_1KI": 0.08712793936535713, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2563.625514292717, "W": 108.94, "J_1KI": 10.542393745575032, "W_1KI": 0.447993815102828, "W_D": 73.28999999999999, "J_D": 1724.693537199497, "W_D_1KI": 0.30139036817409826, "J_D_1KI": 0.0012394072046407218} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_045.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_045.output new file mode 100644 index 0000000..9fcba18 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_045.output @@ -0,0 +1,105 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.10718226432800293} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0096, 0.2145, 0.6490, ..., 0.2282, 0.3485, 0.9521]) +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: 0.10718226432800293 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '195927', '-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": 18.098947763442993} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.5756, 0.6430, 0.3728, ..., 0.7554, 0.9060, 0.8860]) +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: 18.098947763442993 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '227331', '-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": 19.63188910484314} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9737, 0.4514, 0.8059, ..., 0.3086, 0.9830, 0.5037]) +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: 19.63188910484314 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '243173', '-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": 21.187162399291992} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.5566, 0.0730, 0.0280, ..., 0.4513, 0.9009, 0.5894]) +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: 21.187162399291992 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([0.5566, 0.0730, 0.0280, ..., 0.4513, 0.9009, 0.5894]) +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: 21.187162399291992 seconds + +[39.8, 39.71, 39.11, 39.44, 39.1, 39.43, 39.16, 39.54, 39.55, 39.14] +[108.94] +23.532453775405884 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 243173, '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': 21.187162399291992, 'TIME_S_1KI': 0.08712793936535713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2563.625514292717, 'W': 108.94} +[39.8, 39.71, 39.11, 39.44, 39.1, 39.43, 39.16, 39.54, 39.55, 39.14, 39.82, 39.18, 39.5, 39.07, 38.94, 39.22, 39.0, 44.95, 38.94, 39.56] +713.0 +35.65 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 243173, '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': 21.187162399291992, 'TIME_S_1KI': 0.08712793936535713, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2563.625514292717, 'W': 108.94, 'J_1KI': 10.542393745575032, 'W_1KI': 0.447993815102828, 'W_D': 73.28999999999999, 'J_D': 1724.693537199497, 'W_D_1KI': 0.30139036817409826, 'J_D_1KI': 0.0012394072046407218} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_050.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_050.json new file mode 100644 index 0000000..c96b1d4 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_050.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 240158, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 22.33744502067566, "TIME_S_1KI": 0.09301145504491068, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2562.788330435753, "W": 107.15, "J_1KI": 10.67125946433495, "W_1KI": 0.44616460829953614, "W_D": 72.1175, "J_D": 1724.889289969206, "W_D_1KI": 0.3002918911716454, "J_D_1KI": 0.0012503930377986384} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_050.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_050.output new file mode 100644 index 0000000..4cc161a --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_050.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.13907456398010254} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.7147, 0.9892, 0.5644, ..., 0.7484, 0.7920, 0.9091]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 0.13907456398010254 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '150998', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 13.203584909439087} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.4141, 0.2334, 0.4747, ..., 0.7663, 0.6533, 0.8550]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 13.203584909439087 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '240158', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 22.33744502067566} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.5149, 0.8136, 0.7771, ..., 0.9064, 0.0587, 0.9445]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 22.33744502067566 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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.5149, 0.8136, 0.7771, ..., 0.9064, 0.0587, 0.9445]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 22.33744502067566 seconds + +[44.15, 39.09, 38.82, 38.46, 38.4, 38.77, 38.41, 38.29, 38.84, 38.75] +[107.15] +23.917763233184814 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 240158, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 22.33744502067566, 'TIME_S_1KI': 0.09301145504491068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2562.788330435753, 'W': 107.15} +[44.15, 39.09, 38.82, 38.46, 38.4, 38.77, 38.41, 38.29, 38.84, 38.75, 39.2, 39.86, 38.62, 38.82, 38.51, 38.9, 39.2, 38.9, 38.42, 38.58] +700.6500000000001 +35.032500000000006 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 240158, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 22.33744502067566, 'TIME_S_1KI': 0.09301145504491068, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2562.788330435753, 'W': 107.15, 'J_1KI': 10.67125946433495, 'W_1KI': 0.44616460829953614, 'W_D': 72.1175, 'J_D': 1724.889289969206, 'W_D_1KI': 0.3002918911716454, 'J_D_1KI': 0.0012503930377986384} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_055.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_055.json new file mode 100644 index 0000000..519deee --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_055.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 243056, "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": 21.788699865341187, "TIME_S_1KI": 0.0896447726669623, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2614.8328268265723, "W": 109.47, "J_1KI": 10.758149672612781, "W_1KI": 0.45039003357251, "W_D": 73.9525, "J_D": 1766.4513074439765, "W_D_1KI": 0.3042611579224541, "J_D_1KI": 0.0012518150464191549} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_055.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_055.output new file mode 100644 index 0000000..e2ae8c5 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_055.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.12037158012390137} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2129, 0.8233, 0.4187, ..., 0.5468, 0.2620, 0.5585]) +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: 0.12037158012390137 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '174459', '-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": 15.073203563690186} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6340, 0.2808, 0.1161, ..., 0.6453, 0.0525, 0.0651]) +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: 15.073203563690186 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '243056', '-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": 21.788699865341187} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0218, 0.7016, 0.7341, ..., 0.8849, 0.1906, 0.8954]) +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: 21.788699865341187 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([0.0218, 0.7016, 0.7341, ..., 0.8849, 0.1906, 0.8954]) +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: 21.788699865341187 seconds + +[40.42, 39.66, 39.33, 39.53, 39.2, 39.11, 39.99, 39.13, 39.14, 39.51] +[109.47] +23.886296033859253 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 243056, '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': 21.788699865341187, 'TIME_S_1KI': 0.0896447726669623, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2614.8328268265723, 'W': 109.47} +[40.42, 39.66, 39.33, 39.53, 39.2, 39.11, 39.99, 39.13, 39.14, 39.51, 40.07, 39.67, 39.37, 39.27, 39.27, 39.47, 39.3, 39.93, 39.46, 39.04] +710.35 +35.5175 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 243056, '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': 21.788699865341187, 'TIME_S_1KI': 0.0896447726669623, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2614.8328268265723, 'W': 109.47, 'J_1KI': 10.758149672612781, 'W_1KI': 0.45039003357251, 'W_D': 73.9525, 'J_D': 1766.4513074439765, 'W_D_1KI': 0.3042611579224541, 'J_D_1KI': 0.0012518150464191549} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_060.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_060.json new file mode 100644 index 0000000..c3050dd --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_060.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 231053, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 20.448925495147705, "TIME_S_1KI": 0.08850318106732094, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2488.9935939955712, "W": 109.33, "J_1KI": 10.772392455391495, "W_1KI": 0.47318147784274606, "W_D": 73.588, "J_D": 1675.2955327444074, "W_D_1KI": 0.3184896971690478, "J_D_1KI": 0.001378427015312711} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_060.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_060.output new file mode 100644 index 0000000..5d37517 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_060.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.10647058486938477} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.3889, 0.3396, 0.4863, ..., 0.8323, 0.8906, 0.8333]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 0.10647058486938477 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '197237', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 17.926481008529663} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.0027, 0.6119, 0.0510, ..., 0.1297, 0.5480, 0.0399]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 17.926481008529663 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '231053', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 20.448925495147705} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.2183, 0.4339, 0.4856, ..., 0.4687, 0.5468, 0.7375]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 20.448925495147705 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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.2183, 0.4339, 0.4856, ..., 0.4687, 0.5468, 0.7375]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 20.448925495147705 seconds + +[41.56, 39.18, 39.21, 39.16, 39.44, 45.24, 39.35, 39.09, 39.61, 39.47] +[109.33] +22.7658793926239 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 231053, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 20.448925495147705, 'TIME_S_1KI': 0.08850318106732094, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2488.9935939955712, 'W': 109.33} +[41.56, 39.18, 39.21, 39.16, 39.44, 45.24, 39.35, 39.09, 39.61, 39.47, 40.17, 39.63, 39.12, 39.22, 39.1, 39.33, 39.13, 39.09, 39.61, 39.46] +714.8400000000001 +35.742000000000004 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 231053, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 20.448925495147705, 'TIME_S_1KI': 0.08850318106732094, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2488.9935939955712, 'W': 109.33, 'J_1KI': 10.772392455391495, 'W_1KI': 0.47318147784274606, 'W_D': 73.588, 'J_D': 1675.2955327444074, 'W_D_1KI': 0.3184896971690478, 'J_D_1KI': 0.001378427015312711} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_065.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_065.json new file mode 100644 index 0000000..e09d46c --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_065.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 251814, "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": 21.933937549591064, "TIME_S_1KI": 0.08710372556565983, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2850.223569984436, "W": 109.16, "J_1KI": 11.318765318784642, "W_1KI": 0.43349456344762405, "W_D": 73.41199999999999, "J_D": 1916.8249607887267, "W_D_1KI": 0.29153263917018113, "J_D_1KI": 0.0011577300673123064} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_065.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_065.output new file mode 100644 index 0000000..0d99354 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_065.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.1031339168548584} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3467, 0.1204, 0.8088, ..., 0.7162, 0.4097, 0.3295]) +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: 0.1031339168548584 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '203618', '-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": 16.980651140213013} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1656, 0.7627, 0.7205, ..., 0.6513, 0.9973, 0.1356]) +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: 16.980651140213013 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '251814', '-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": 21.933937549591064} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6795, 0.4572, 0.8752, ..., 0.2627, 0.4989, 0.4040]) +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: 21.933937549591064 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([0.6795, 0.4572, 0.8752, ..., 0.2627, 0.4989, 0.4040]) +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: 21.933937549591064 seconds + +[40.24, 39.25, 39.88, 39.1, 44.85, 39.19, 39.6, 39.39, 39.16, 39.14] +[109.16] +26.110512733459473 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 251814, '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': 21.933937549591064, 'TIME_S_1KI': 0.08710372556565983, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2850.223569984436, 'W': 109.16} +[40.24, 39.25, 39.88, 39.1, 44.85, 39.19, 39.6, 39.39, 39.16, 39.14, 39.92, 39.63, 39.78, 40.29, 39.17, 39.06, 39.09, 39.17, 39.2, 39.0] +714.96 +35.748000000000005 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 251814, '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': 21.933937549591064, 'TIME_S_1KI': 0.08710372556565983, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2850.223569984436, 'W': 109.16, 'J_1KI': 11.318765318784642, 'W_1KI': 0.43349456344762405, 'W_D': 73.41199999999999, 'J_D': 1916.8249607887267, 'W_D_1KI': 0.29153263917018113, 'J_D_1KI': 0.0011577300673123064} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_070.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_070.json new file mode 100644 index 0000000..c4e2eea --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_070.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 246006, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 22.265681505203247, "TIME_S_1KI": 0.0905086928985604, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2452.5237454128264, "W": 108.92, "J_1KI": 9.969365565932645, "W_1KI": 0.4427534287781599, "W_D": 73.40100000000001, "J_D": 1652.751518885851, "W_D_1KI": 0.29837077144459895, "J_D_1KI": 0.0012128597328707386} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_070.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_070.output new file mode 100644 index 0000000..b44b9fd --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_070.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.10181021690368652} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.7820, 0.8357, 0.3400, ..., 0.6426, 0.8336, 0.2071]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 0.10181021690368652 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '206266', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 17.607593774795532} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.9459, 0.2541, 0.2751, ..., 0.1790, 0.8403, 0.0106]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 17.607593774795532 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '246006', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 22.265681505203247} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.6268, 0.6626, 0.9417, ..., 0.0422, 0.6229, 0.6981]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 22.265681505203247 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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.6268, 0.6626, 0.9417, ..., 0.0422, 0.6229, 0.6981]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 22.265681505203247 seconds + +[45.03, 39.14, 39.55, 40.05, 39.08, 39.41, 39.08, 39.05, 39.67, 38.97] +[108.92] +22.516743898391724 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 246006, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 22.265681505203247, 'TIME_S_1KI': 0.0905086928985604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2452.5237454128264, 'W': 108.92} +[45.03, 39.14, 39.55, 40.05, 39.08, 39.41, 39.08, 39.05, 39.67, 38.97, 40.17, 39.34, 39.47, 38.92, 39.05, 39.51, 39.22, 38.87, 39.39, 38.99] +710.38 +35.519 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 246006, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 22.265681505203247, 'TIME_S_1KI': 0.0905086928985604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2452.5237454128264, 'W': 108.92, 'J_1KI': 9.969365565932645, 'W_1KI': 0.4427534287781599, 'W_D': 73.40100000000001, 'J_D': 1652.751518885851, 'W_D_1KI': 0.29837077144459895, 'J_D_1KI': 0.0012128597328707386} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_075.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_075.json new file mode 100644 index 0000000..030fb95 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_075.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 228835, "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": 20.558088302612305, "TIME_S_1KI": 0.08983804183194137, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2547.4816253471377, "W": 108.56, "J_1KI": 11.132395067831135, "W_1KI": 0.47440295409356087, "W_D": 72.922, "J_D": 1711.1961595759392, "W_D_1KI": 0.31866628793672297, "J_D_1KI": 0.0013925592148785063} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_075.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_075.output new file mode 100644 index 0000000..726a183 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_075.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.09981632232666016} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7547, 0.7232, 0.9214, ..., 0.5453, 0.7893, 0.6720]) +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: 0.09981632232666016 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '210386', '-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": 19.3068745136261} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2501, 0.7708, 0.6340, ..., 0.1093, 0.9970, 0.8812]) +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: 19.3068745136261 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '228835', '-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": 20.558088302612305} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6824, 0.0363, 0.8858, ..., 0.8723, 0.9945, 0.3255]) +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: 20.558088302612305 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([0.6824, 0.0363, 0.8858, ..., 0.8723, 0.9945, 0.3255]) +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: 20.558088302612305 seconds + +[41.25, 39.57, 45.05, 39.28, 39.46, 39.08, 39.45, 39.03, 39.72, 39.35] +[108.56] +23.466116666793823 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 228835, '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': 20.558088302612305, 'TIME_S_1KI': 0.08983804183194137, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2547.4816253471377, 'W': 108.56} +[41.25, 39.57, 45.05, 39.28, 39.46, 39.08, 39.45, 39.03, 39.72, 39.35, 40.09, 39.03, 39.05, 38.95, 38.96, 38.88, 38.94, 38.94, 39.29, 39.47] +712.76 +35.638 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 228835, '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': 20.558088302612305, 'TIME_S_1KI': 0.08983804183194137, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2547.4816253471377, 'W': 108.56, 'J_1KI': 11.132395067831135, 'W_1KI': 0.47440295409356087, 'W_D': 72.922, 'J_D': 1711.1961595759392, 'W_D_1KI': 0.31866628793672297, 'J_D_1KI': 0.0013925592148785063} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_080.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_080.json new file mode 100644 index 0000000..c19a2fc --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_080.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 231978, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 21.490782022476196, "TIME_S_1KI": 0.09264146609797566, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2630.7592759251593, "W": 108.57, "J_1KI": 11.340555035068666, "W_1KI": 0.4680185189974911, "W_D": 72.83899999999998, "J_D": 1764.9615446174141, "W_D_1KI": 0.31399098190345626, "J_D_1KI": 0.001353537757474658} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_080.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_080.output new file mode 100644 index 0000000..8431777 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_080.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.12141084671020508} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.5522, 0.8373, 0.3779, ..., 0.6358, 0.6326, 0.2271]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 0.12141084671020508 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '172966', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 15.657852172851562} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.5579, 0.8962, 0.2299, ..., 0.1817, 0.7504, 0.1559]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 15.657852172851562 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '231978', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 21.490782022476196} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.6706, 0.5375, 0.7370, ..., 0.5134, 0.1361, 0.9920]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 21.490782022476196 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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.6706, 0.5375, 0.7370, ..., 0.5134, 0.1361, 0.9920]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 21.490782022476196 seconds + +[40.67, 39.43, 44.71, 39.49, 39.25, 39.48, 39.39, 39.44, 39.05, 39.19] +[108.57] +24.23099637031555 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 231978, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 21.490782022476196, 'TIME_S_1KI': 0.09264146609797566, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2630.7592759251593, 'W': 108.57} +[40.67, 39.43, 44.71, 39.49, 39.25, 39.48, 39.39, 39.44, 39.05, 39.19, 40.12, 39.45, 40.63, 39.92, 38.97, 38.98, 39.04, 38.9, 39.05, 38.9] +714.6200000000001 +35.73100000000001 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 231978, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 21.490782022476196, 'TIME_S_1KI': 0.09264146609797566, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2630.7592759251593, 'W': 108.57, 'J_1KI': 11.340555035068666, 'W_1KI': 0.4680185189974911, 'W_D': 72.83899999999998, 'J_D': 1764.9615446174141, 'W_D_1KI': 0.31399098190345626, 'J_D_1KI': 0.001353537757474658} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_085.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_085.json new file mode 100644 index 0000000..1a97f5e --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_085.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 240739, "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": 20.274125337600708, "TIME_S_1KI": 0.08421620650414227, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2593.0004249954227, "W": 110.26000000000002, "J_1KI": 10.771002724923767, "W_1KI": 0.45800638866157967, "W_D": 74.74925000000002, "J_D": 1757.8889626164441, "W_D_1KI": 0.31049912976293836, "J_D_1KI": 0.0012897749420033248} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_085.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_085.output new file mode 100644 index 0000000..434c43b --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_085.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.11759328842163086} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7707, 0.4663, 0.0430, ..., 0.4487, 0.2025, 0.1450]) +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: 0.11759328842163086 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '178581', '-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": 15.577868700027466} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9390, 0.2041, 0.9410, ..., 0.4323, 0.1487, 0.9127]) +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: 15.577868700027466 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '240739', '-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": 20.274125337600708} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6109, 0.4136, 0.6946, ..., 0.9768, 0.6288, 0.8861]) +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: 20.274125337600708 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([0.6109, 0.4136, 0.6946, ..., 0.9768, 0.6288, 0.8861]) +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: 20.274125337600708 seconds + +[39.65, 39.13, 39.02, 39.3, 39.12, 39.02, 39.24, 39.05, 39.96, 38.96] +[110.26] +23.51714515686035 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 240739, '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': 20.274125337600708, 'TIME_S_1KI': 0.08421620650414227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2593.0004249954227, 'W': 110.26000000000002} +[39.65, 39.13, 39.02, 39.3, 39.12, 39.02, 39.24, 39.05, 39.96, 38.96, 45.56, 39.23, 39.18, 39.55, 39.1, 39.1, 39.15, 39.32, 40.17, 38.98] +710.2149999999999 +35.510749999999994 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 240739, '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': 20.274125337600708, 'TIME_S_1KI': 0.08421620650414227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2593.0004249954227, 'W': 110.26000000000002, 'J_1KI': 10.771002724923767, 'W_1KI': 0.45800638866157967, 'W_D': 74.74925000000002, 'J_D': 1757.8889626164441, 'W_D_1KI': 0.31049912976293836, 'J_D_1KI': 0.0012897749420033248} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_090.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_090.json new file mode 100644 index 0000000..2f432c0 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_090.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 252609, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 21.922589540481567, "TIME_S_1KI": 0.08678467331125006, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2682.2763265132903, "W": 110.86, "J_1KI": 10.618292802367653, "W_1KI": 0.43886005645087867, "W_D": 75.167, "J_D": 1818.6781944346428, "W_D_1KI": 0.2975626363272884, "J_D_1KI": 0.0011779573820698724} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_090.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_090.output new file mode 100644 index 0000000..f47ffb4 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_090.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.09967470169067383} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.2862, 0.4561, 0.2506, ..., 0.8743, 0.1094, 0.9183]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 0.09967470169067383 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '210685', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 17.51469898223877} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.5217, 0.4687, 0.7241, ..., 0.7435, 0.7216, 0.9537]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 17.51469898223877 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '252609', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 21.922589540481567} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.1627, 0.9463, 0.5117, ..., 0.2113, 0.1004, 0.5160]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 21.922589540481567 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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.1627, 0.9463, 0.5117, ..., 0.2113, 0.1004, 0.5160]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 21.922589540481567 seconds + +[40.69, 39.28, 39.34, 39.64, 40.84, 39.2, 39.95, 39.17, 39.41, 39.14] +[110.86] +24.195168018341064 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 252609, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 21.922589540481567, 'TIME_S_1KI': 0.08678467331125006, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2682.2763265132903, 'W': 110.86} +[40.69, 39.28, 39.34, 39.64, 40.84, 39.2, 39.95, 39.17, 39.41, 39.14, 40.12, 39.33, 39.83, 39.09, 39.28, 41.26, 40.54, 39.1, 39.1, 39.05] +713.86 +35.693 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 252609, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 21.922589540481567, 'TIME_S_1KI': 0.08678467331125006, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2682.2763265132903, 'W': 110.86, 'J_1KI': 10.618292802367653, 'W_1KI': 0.43886005645087867, 'W_D': 75.167, 'J_D': 1818.6781944346428, 'W_D_1KI': 0.2975626363272884, 'J_D_1KI': 0.0011779573820698724} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_095.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_095.json new file mode 100644 index 0000000..1d17fb3 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_095.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 244511, "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": 21.708153247833252, "TIME_S_1KI": 0.08878190857602829, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2484.271987452507, "W": 110.49, "J_1KI": 10.160164522056295, "W_1KI": 0.45188151044329294, "W_D": 74.96725, "J_D": 1685.5737093976738, "W_D_1KI": 0.3066007255297308, "J_D_1KI": 0.0012539342832417796} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_095.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_095.output new file mode 100644 index 0000000..ef9fd62 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_095.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.0991964340209961} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8556, 0.9665, 0.3722, ..., 0.6225, 0.9450, 0.0036]) +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: 0.0991964340209961 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '211701', '-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": 18.18206286430359} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6510, 0.8867, 0.3325, ..., 0.9710, 0.7424, 0.5430]) +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: 18.18206286430359 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '244511', '-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": 21.708153247833252} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6961, 0.4709, 0.2900, ..., 0.8863, 0.2086, 0.2521]) +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: 21.708153247833252 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([0.6961, 0.4709, 0.2900, ..., 0.8863, 0.2086, 0.2521]) +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: 21.708153247833252 seconds + +[39.83, 39.41, 44.85, 39.09, 39.11, 39.54, 38.95, 38.94, 39.01, 39.32] +[110.49] +22.484134197235107 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 244511, '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': 21.708153247833252, 'TIME_S_1KI': 0.08878190857602829, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2484.271987452507, 'W': 110.49} +[39.83, 39.41, 44.85, 39.09, 39.11, 39.54, 38.95, 38.94, 39.01, 39.32, 40.21, 39.31, 39.4, 38.98, 38.95, 38.93, 38.83, 38.78, 39.27, 38.85] +710.4549999999999 +35.522749999999995 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 244511, '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': 21.708153247833252, 'TIME_S_1KI': 0.08878190857602829, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2484.271987452507, 'W': 110.49, 'J_1KI': 10.160164522056295, 'W_1KI': 0.45188151044329294, 'W_D': 74.96725, 'J_D': 1685.5737093976738, 'W_D_1KI': 0.3066007255297308, 'J_D_1KI': 0.0012539342832417796} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_100.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_100.json new file mode 100644 index 0000000..ad5ad9e --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_100.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 241218, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 20.706451177597046, "TIME_S_1KI": 0.08584123563580266, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2648.6161953759192, "W": 109.67, "J_1KI": 10.980176418741218, "W_1KI": 0.45465097961180345, "W_D": 73.90275, "J_D": 1784.8091595953106, "W_D_1KI": 0.30637328060095015, "J_D_1KI": 0.001270109529972681} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_100.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_100.output new file mode 100644 index 0000000..6dc5aa0 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_100.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.10281991958618164} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.4680, 0.0916, 0.1498, ..., 0.5442, 0.0289, 0.8913]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 0.10281991958618164 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '204240', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 17.780753135681152} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.3783, 0.9598, 0.1242, ..., 0.4171, 0.3302, 0.6976]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 17.780753135681152 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '241218', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 20.706451177597046} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.7242, 0.2886, 0.2051, ..., 0.5324, 0.1969, 0.5524]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 20.706451177597046 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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.7242, 0.2886, 0.2051, ..., 0.5324, 0.1969, 0.5524]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 20.706451177597046 seconds + +[41.19, 39.42, 39.18, 39.49, 39.54, 39.15, 39.04, 39.04, 39.4, 38.9] +[109.67] +24.150781393051147 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 241218, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 20.706451177597046, 'TIME_S_1KI': 0.08584123563580266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2648.6161953759192, 'W': 109.67} +[41.19, 39.42, 39.18, 39.49, 39.54, 39.15, 39.04, 39.04, 39.4, 38.9, 39.85, 39.01, 40.47, 38.94, 39.0, 38.95, 39.45, 44.44, 41.2, 39.31] +715.3449999999999 +35.76725 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 241218, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 20.706451177597046, 'TIME_S_1KI': 0.08584123563580266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2648.6161953759192, 'W': 109.67, 'J_1KI': 10.980176418741218, 'W_1KI': 0.45465097961180345, 'W_D': 73.90275, 'J_D': 1784.8091595953106, 'W_D_1KI': 0.30637328060095015, 'J_D_1KI': 0.001270109529972681} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_105.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_105.json new file mode 100644 index 0000000..33a84df --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_105.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 247484, "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": 20.726130485534668, "TIME_S_1KI": 0.08374735532614096, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2676.5612843990325, "W": 110.2, "J_1KI": 10.815088185090884, "W_1KI": 0.445281311115062, "W_D": 74.83, "J_D": 1817.4871226096152, "W_D_1KI": 0.30236298104119863, "J_D_1KI": 0.001221747591929978} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_105.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_105.output new file mode 100644 index 0000000..fab7458 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_105.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.10206460952758789} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7941, 0.4112, 0.8474, ..., 0.8075, 0.4406, 0.7975]) +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: 0.10206460952758789 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '205752', '-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": 17.45883798599243} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6509, 0.0632, 0.4912, ..., 0.8214, 0.1083, 0.9290]) +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: 17.45883798599243 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '247484', '-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": 20.726130485534668} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8186, 0.7461, 0.3011, ..., 0.4943, 0.6376, 0.8092]) +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: 20.726130485534668 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([0.8186, 0.7461, 0.3011, ..., 0.4943, 0.6376, 0.8092]) +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: 20.726130485534668 seconds + +[39.73, 39.49, 39.07, 39.13, 39.1, 39.5, 39.55, 39.36, 39.08, 38.97] +[110.2] +24.288214921951294 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 247484, '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': 20.726130485534668, 'TIME_S_1KI': 0.08374735532614096, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2676.5612843990325, 'W': 110.2} +[39.73, 39.49, 39.07, 39.13, 39.1, 39.5, 39.55, 39.36, 39.08, 38.97, 39.81, 39.63, 39.02, 39.43, 39.27, 39.35, 39.73, 38.86, 39.07, 39.01] +707.4000000000001 +35.370000000000005 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 247484, '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': 20.726130485534668, 'TIME_S_1KI': 0.08374735532614096, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2676.5612843990325, 'W': 110.2, 'J_1KI': 10.815088185090884, 'W_1KI': 0.445281311115062, 'W_D': 74.83, 'J_D': 1817.4871226096152, 'W_D_1KI': 0.30236298104119863, 'J_D_1KI': 0.001221747591929978} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_110.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_110.json new file mode 100644 index 0000000..bc50fb4 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_110.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 225094, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 20.045295476913452, "TIME_S_1KI": 0.08905299775610835, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2434.8910455989835, "W": 110.26999999999998, "J_1KI": 10.817218786813436, "W_1KI": 0.48988422614552135, "W_D": 73.64949999999997, "J_D": 1626.2674169116015, "W_D_1KI": 0.3271944165548614, "J_D_1KI": 0.0014535901292564947} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_110.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_110.output new file mode 100644 index 0000000..7269a97 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_110.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.10591554641723633} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.5817, 0.1599, 0.8730, ..., 0.2935, 0.5494, 0.8207]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 0.10591554641723633 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '198271', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 18.49750328063965} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.4052, 0.0572, 0.8332, ..., 0.6912, 0.3718, 0.3744]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 18.49750328063965 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '225094', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 20.045295476913452} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.8447, 0.8415, 0.3714, ..., 0.3604, 0.8377, 0.5951]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 20.045295476913452 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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.8447, 0.8415, 0.3714, ..., 0.3604, 0.8377, 0.5951]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 20.045295476913452 seconds + +[40.62, 39.64, 39.23, 39.67, 39.73, 39.09, 39.6, 39.58, 39.51, 39.98] +[110.27] +22.08117389678955 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 225094, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 20.045295476913452, 'TIME_S_1KI': 0.08905299775610835, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2434.8910455989835, 'W': 110.26999999999998} +[40.62, 39.64, 39.23, 39.67, 39.73, 39.09, 39.6, 39.58, 39.51, 39.98, 39.85, 39.46, 39.55, 39.26, 45.01, 39.42, 39.52, 49.35, 45.03, 39.07] +732.4100000000001 +36.62050000000001 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 225094, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 20.045295476913452, 'TIME_S_1KI': 0.08905299775610835, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2434.8910455989835, 'W': 110.26999999999998, 'J_1KI': 10.817218786813436, 'W_1KI': 0.48988422614552135, 'W_D': 73.64949999999997, 'J_D': 1626.2674169116015, 'W_D_1KI': 0.3271944165548614, 'J_D_1KI': 0.0014535901292564947} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_115.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_115.json new file mode 100644 index 0000000..16a2df5 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_115.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 234073, "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": 21.110437870025635, "TIME_S_1KI": 0.09018741106417927, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2580.228788130283, "W": 109.77, "J_1KI": 11.023179897426372, "W_1KI": 0.4689562657803335, "W_D": 71.584, "J_D": 1682.6373104629517, "W_D_1KI": 0.3058191248029461, "J_D_1KI": 0.0013065117497658683} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_115.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_115.output new file mode 100644 index 0000000..2d46020 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_115.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.10738468170166016} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0691, 0.6464, 0.3795, ..., 0.4403, 0.3685, 0.1679]) +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: 0.10738468170166016 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '195558', '-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": 17.54453992843628} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3421, 0.1187, 0.2163, ..., 0.8976, 0.3011, 0.1448]) +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: 17.54453992843628 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '234073', '-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": 21.110437870025635} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9599, 0.3879, 0.7672, ..., 0.8941, 0.8822, 0.9833]) +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: 21.110437870025635 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([0.9599, 0.3879, 0.7672, ..., 0.8941, 0.8822, 0.9833]) +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: 21.110437870025635 seconds + +[39.49, 39.1, 39.1, 39.5, 39.4, 39.39, 39.24, 38.91, 38.94, 39.59] +[109.77] +23.505773782730103 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 234073, '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': 21.110437870025635, 'TIME_S_1KI': 0.09018741106417927, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2580.228788130283, 'W': 109.77} +[39.49, 39.1, 39.1, 39.5, 39.4, 39.39, 39.24, 38.91, 38.94, 39.59, 39.66, 38.99, 39.64, 39.13, 38.92, 39.41, 39.02, 56.35, 67.32, 63.98] +763.72 +38.186 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 234073, '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': 21.110437870025635, 'TIME_S_1KI': 0.09018741106417927, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2580.228788130283, 'W': 109.77, 'J_1KI': 11.023179897426372, 'W_1KI': 0.4689562657803335, 'W_D': 71.584, 'J_D': 1682.6373104629517, 'W_D_1KI': 0.3058191248029461, 'J_D_1KI': 0.0013065117497658683} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_120.json b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_120.json new file mode 100644 index 0000000..4387ea3 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_120.json @@ -0,0 +1 @@ +{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 246921, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 21.179029941558838, "TIME_S_1KI": 0.08577249379987462, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2789.262641429901, "W": 109.9, "J_1KI": 11.296174247754955, "W_1KI": 0.4450816252971599, "W_D": 74.2125, "J_D": 1883.513683140278, "W_D_1KI": 0.3005515934246176, "J_D_1KI": 0.001217197376588535} diff --git a/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_120.output b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_120.output new file mode 100644 index 0000000..cb66db8 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/epyc_7313p_max_csr_20_10_10_as-caida_G_120.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.13150310516357422} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.0187, 0.2029, 0.6613, ..., 0.2021, 0.3325, 0.5912]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 0.13150310516357422 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '159692', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 13.581367492675781} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.0016, 0.7280, 0.4677, ..., 0.1439, 0.6254, 0.9086]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 13.581367492675781 seconds + +['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'csr', '246921', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 21.179029941558838} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.4228, 0.0866, 0.5391, ..., 0.1734, 0.5961, 0.3644]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 21.179029941558838 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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.4228, 0.0866, 0.5391, ..., 0.1734, 0.5961, 0.3644]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 21.179029941558838 seconds + +[40.0, 44.52, 39.99, 39.55, 39.78, 39.63, 39.26, 39.08, 39.04, 39.39] +[109.9] +25.380005836486816 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 246921, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 21.179029941558838, 'TIME_S_1KI': 0.08577249379987462, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2789.262641429901, 'W': 109.9} +[40.0, 44.52, 39.99, 39.55, 39.78, 39.63, 39.26, 39.08, 39.04, 39.39, 39.72, 39.14, 39.01, 39.02, 39.02, 39.73, 39.52, 39.34, 38.96, 39.21] +713.75 +35.6875 +{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 246921, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 21.179029941558838, 'TIME_S_1KI': 0.08577249379987462, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2789.262641429901, 'W': 109.9, 'J_1KI': 11.296174247754955, 'W_1KI': 0.4450816252971599, 'W_D': 74.2125, 'J_D': 1883.513683140278, 'W_D_1KI': 0.3005515934246176, 'J_D_1KI': 0.001217197376588535} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_005.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_005.json new file mode 100644 index 0000000..0625be0 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_005.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 228681, "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": 20.71348738670349, "TIME_S_1KI": 0.09057808644663742, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1985.7099216771128, "W": 81.43, "J_1KI": 8.683318341607361, "W_1KI": 0.35608555148875515, "W_D": 64.78550000000001, "J_D": 1579.8257415057424, "W_D_1KI": 0.2833007552004758, "J_D_1KI": 0.0012388469317541721} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_005.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_005.output new file mode 100644 index 0000000..c22ba63 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_005.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.10603070259094238} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7270, 0.4992, 0.4890, ..., 0.7263, 0.9568, 0.8257]) +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: 0.10603070259094238 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '198055', '-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": 18.187560081481934} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8953, 0.8779, 0.8892, ..., 0.4124, 0.7171, 0.6644]) +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: 18.187560081481934 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '228681', '-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": 20.71348738670349} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1790, 0.8570, 0.6154, ..., 0.3543, 0.1752, 0.3411]) +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: 20.71348738670349 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([0.1790, 0.8570, 0.6154, ..., 0.3543, 0.1752, 0.3411]) +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: 20.71348738670349 seconds + +[18.75, 18.36, 18.44, 17.9, 21.42, 17.95, 18.08, 18.0, 18.15, 17.98] +[81.43] +24.385483503341675 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 228681, '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': 20.71348738670349, 'TIME_S_1KI': 0.09057808644663742, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1985.7099216771128, 'W': 81.43} +[18.75, 18.36, 18.44, 17.9, 21.42, 17.95, 18.08, 18.0, 18.15, 17.98, 18.41, 17.76, 18.35, 21.01, 17.94, 18.41, 18.12, 18.12, 18.23, 18.16] +332.89 +16.6445 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 228681, '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': 20.71348738670349, 'TIME_S_1KI': 0.09057808644663742, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1985.7099216771128, 'W': 81.43, 'J_1KI': 8.683318341607361, 'W_1KI': 0.35608555148875515, 'W_D': 64.78550000000001, 'J_D': 1579.8257415057424, 'W_D_1KI': 0.2833007552004758, 'J_D_1KI': 0.0012388469317541721} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_010.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_010.json new file mode 100644 index 0000000..6727087 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_010.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 219634, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 21.155344486236572, "TIME_S_1KI": 0.09632089970695144, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2007.079451637268, "W": 81.32, "J_1KI": 9.138291210091644, "W_1KI": 0.3702523288744001, "W_D": 65.02274999999999, "J_D": 1604.842909664869, "W_D_1KI": 0.2960504748809382, "J_D_1KI": 0.0013479264361662504} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_010.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_010.output new file mode 100644 index 0000000..6a2233d --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_010.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.10599613189697266} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 28, 28, ..., 74993, 74993, 74994]), + col_indices=tensor([ 1040, 2020, 2054, ..., 160, 160, 12170]), + values=tensor([1., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=74994, layout=torch.sparse_csr) +tensor([0.8277, 0.3875, 0.9175, ..., 0.7822, 0.3026, 0.5609]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_010 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 74994 +Density: 7.616375021864427e-05 +Time: 0.10599613189697266 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '198120', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 18.942925214767456} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 28, 28, ..., 74993, 74993, 74994]), + col_indices=tensor([ 1040, 2020, 2054, ..., 160, 160, 12170]), + values=tensor([1., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=74994, layout=torch.sparse_csr) +tensor([0.9981, 0.2399, 0.7684, ..., 0.8350, 0.7877, 0.0239]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_010 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 74994 +Density: 7.616375021864427e-05 +Time: 18.942925214767456 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '219634', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 21.155344486236572} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.) + matrix = matrix.to_sparse_csr().type(torch.float32) +tensor(crow_indices=tensor([ 0, 28, 28, ..., 74993, 74993, 74994]), + col_indices=tensor([ 1040, 2020, 2054, ..., 160, 160, 12170]), + values=tensor([1., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=74994, layout=torch.sparse_csr) +tensor([0.3439, 0.2176, 0.6511, ..., 0.3157, 0.5036, 0.0852]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_010 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 74994 +Density: 7.616375021864427e-05 +Time: 21.155344486236572 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, 28, 28, ..., 74993, 74993, 74994]), + col_indices=tensor([ 1040, 2020, 2054, ..., 160, 160, 12170]), + values=tensor([1., 3., 3., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=74994, layout=torch.sparse_csr) +tensor([0.3439, 0.2176, 0.6511, ..., 0.3157, 0.5036, 0.0852]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_010 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 74994 +Density: 7.616375021864427e-05 +Time: 21.155344486236572 seconds + +[18.51, 18.19, 18.29, 17.98, 18.25, 17.99, 18.0, 18.01, 18.19, 18.67] +[81.32] +24.681252479553223 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 219634, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 21.155344486236572, 'TIME_S_1KI': 0.09632089970695144, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2007.079451637268, 'W': 81.32} +[18.51, 18.19, 18.29, 17.98, 18.25, 17.99, 18.0, 18.01, 18.19, 18.67, 18.4, 17.89, 18.25, 18.19, 17.92, 18.04, 17.99, 17.96, 18.08, 17.87] +325.94500000000005 +16.297250000000002 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 219634, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 21.155344486236572, 'TIME_S_1KI': 0.09632089970695144, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2007.079451637268, 'W': 81.32, 'J_1KI': 9.138291210091644, 'W_1KI': 0.3702523288744001, 'W_D': 65.02274999999999, 'J_D': 1604.842909664869, 'W_D_1KI': 0.2960504748809382, 'J_D_1KI': 0.0013479264361662504} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_015.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_015.json new file mode 100644 index 0000000..e81dafb --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_015.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 213306, "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": 21.153538703918457, "TIME_S_1KI": 0.09916991882046663, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1991.634647140503, "W": 81.36, "J_1KI": 9.336983709508887, "W_1KI": 0.3814238699333352, "W_D": 64.90700000000001, "J_D": 1588.8769670839313, "W_D_1KI": 0.3042905497266838, "J_D_1KI": 0.0014265447278870907} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_015.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_015.output new file mode 100644 index 0000000..56a04a0 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_015.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.10793638229370117} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3496, 0.5901, 0.8391, ..., 0.8578, 0.9459, 0.9645]) +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: 0.10793638229370117 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '194559', '-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": 19.15434455871582} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4521, 0.3043, 0.6899, ..., 0.3808, 0.8530, 0.6224]) +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: 19.15434455871582 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '213306', '-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": 21.153538703918457} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1527, 0.5505, 0.7532, ..., 0.0482, 0.5616, 0.6382]) +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: 21.153538703918457 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([0.1527, 0.5505, 0.7532, ..., 0.0482, 0.5616, 0.6382]) +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: 21.153538703918457 seconds + +[18.39, 17.98, 18.02, 18.17, 18.79, 18.08, 18.17, 17.96, 18.03, 17.9] +[81.36] +24.47928524017334 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 213306, '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': 21.153538703918457, 'TIME_S_1KI': 0.09916991882046663, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1991.634647140503, 'W': 81.36} +[18.39, 17.98, 18.02, 18.17, 18.79, 18.08, 18.17, 17.96, 18.03, 17.9, 18.59, 18.34, 18.34, 18.25, 18.21, 19.52, 18.41, 17.85, 18.47, 18.06] +329.05999999999995 +16.452999999999996 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 213306, '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': 21.153538703918457, 'TIME_S_1KI': 0.09916991882046663, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1991.634647140503, 'W': 81.36, 'J_1KI': 9.336983709508887, 'W_1KI': 0.3814238699333352, 'W_D': 64.90700000000001, 'J_D': 1588.8769670839313, 'W_D_1KI': 0.3042905497266838, 'J_D_1KI': 0.0014265447278870907} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_020.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_020.json new file mode 100644 index 0000000..8d59e8c --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_020.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 213182, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 20.133166313171387, "TIME_S_1KI": 0.09444121132727616, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1942.738121509552, "W": 81.0, "J_1KI": 9.113049514075072, "W_1KI": 0.3799570320195889, "W_D": 64.693, "J_D": 1551.6241641335487, "W_D_1KI": 0.30346370706720077, "J_D_1KI": 0.0014234959192952535} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_020.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_020.output new file mode 100644 index 0000000..2ef9f0d --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_020.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 0.10763216018676758} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.3807, 0.4125, 0.9987, ..., 0.0478, 0.5787, 0.1037]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 0.10763216018676758 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '195108', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 19.21956491470337} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.5542, 0.2297, 0.8271, ..., 0.2905, 0.9080, 0.0300]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 19.21956491470337 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '213182', '-m', 'matrices/as-caida_pruned/as-caida_G_020.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_020", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 80948, "MATRIX_DENSITY": 8.221062021893506e-05, "TIME_S": 20.133166313171387} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.9303, 0.8711, 0.9149, ..., 0.8449, 0.1938, 0.2910]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 20.133166313171387 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, ..., 80944, 80946, 80948]), + col_indices=tensor([ 1040, 5699, 106, ..., 31378, 17998, 31377]), + values=tensor([1., 1., 1., ..., 1., 1., 3.]), size=(31379, 31379), + nnz=80948, layout=torch.sparse_csr) +tensor([0.9303, 0.8711, 0.9149, ..., 0.8449, 0.1938, 0.2910]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_020 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 80948 +Density: 8.221062021893506e-05 +Time: 20.133166313171387 seconds + +[18.58, 17.98, 18.33, 17.96, 18.07, 17.89, 18.1, 18.77, 18.12, 18.11] +[81.0] +23.984421253204346 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 213182, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 20.133166313171387, 'TIME_S_1KI': 0.09444121132727616, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1942.738121509552, 'W': 81.0} +[18.58, 17.98, 18.33, 17.96, 18.07, 17.89, 18.1, 18.77, 18.12, 18.11, 18.38, 17.87, 18.34, 17.96, 18.17, 18.08, 18.12, 17.91, 18.05, 17.77] +326.14000000000004 +16.307000000000002 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 213182, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_020', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 80948, 'MATRIX_DENSITY': 8.221062021893506e-05, 'TIME_S': 20.133166313171387, 'TIME_S_1KI': 0.09444121132727616, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1942.738121509552, 'W': 81.0, 'J_1KI': 9.113049514075072, 'W_1KI': 0.3799570320195889, 'W_D': 64.693, 'J_D': 1551.6241641335487, 'W_D_1KI': 0.30346370706720077, 'J_D_1KI': 0.0014234959192952535} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_025.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_025.json new file mode 100644 index 0000000..791ab03 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_025.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 213558, "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": 21.71278405189514, "TIME_S_1KI": 0.10167160233704728, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2011.3843038368227, "W": 81.48, "J_1KI": 9.418445124213669, "W_1KI": 0.3815356952209704, "W_D": 64.8775, "J_D": 1601.5413005912303, "W_D_1KI": 0.3037933488794613, "J_D_1KI": 0.0014225332175777134} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_025.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_025.output new file mode 100644 index 0000000..24b9b80 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_025.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.11403584480285645} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6117, 0.0480, 0.1171, ..., 0.2869, 0.6685, 0.9178]) +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: 0.11403584480285645 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '184152', '-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": 18.10831642150879} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3405, 0.4959, 0.1960, ..., 0.3902, 0.1681, 0.5319]) +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: 18.10831642150879 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '213558', '-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": 21.71278405189514} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2206, 0.7543, 0.2535, ..., 0.3898, 0.0631, 0.5002]) +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: 21.71278405189514 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([0.2206, 0.7543, 0.2535, ..., 0.3898, 0.0631, 0.5002]) +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: 21.71278405189514 seconds + +[18.5, 20.4, 18.03, 18.23, 17.98, 18.16, 18.13, 18.2, 18.02, 17.99] +[81.48] +24.685619831085205 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 213558, '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': 21.71278405189514, 'TIME_S_1KI': 0.10167160233704728, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2011.3843038368227, 'W': 81.48} +[18.5, 20.4, 18.03, 18.23, 17.98, 18.16, 18.13, 18.2, 18.02, 17.99, 18.42, 18.15, 18.03, 21.32, 18.07, 18.05, 18.48, 17.97, 18.26, 18.23] +332.05 +16.6025 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 213558, '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': 21.71278405189514, 'TIME_S_1KI': 0.10167160233704728, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2011.3843038368227, 'W': 81.48, 'J_1KI': 9.418445124213669, 'W_1KI': 0.3815356952209704, 'W_D': 64.8775, 'J_D': 1601.5413005912303, 'W_D_1KI': 0.3037933488794613, 'J_D_1KI': 0.0014225332175777134} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_030.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_030.json new file mode 100644 index 0000000..7f70cb0 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_030.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 218006, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 22.412299394607544, "TIME_S_1KI": 0.10280588329957682, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2045.733797636032, "W": 81.72, "J_1KI": 9.38384171828313, "W_1KI": 0.37485206829169837, "W_D": 65.40225000000001, "J_D": 1637.2441662560107, "W_D_1KI": 0.30000206416337166, "J_D_1KI": 0.0013761183828122697} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_030.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_030.output new file mode 100644 index 0000000..e10c481 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_030.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 0.11330509185791016} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.7671, 0.2178, 0.6713, ..., 0.0313, 0.8710, 0.8431]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 0.11330509185791016 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '185340', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 17.853291749954224} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.0180, 0.9216, 0.0946, ..., 0.6906, 0.6961, 0.5824]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 17.853291749954224 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '218006', '-m', 'matrices/as-caida_pruned/as-caida_G_030.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_030", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 86850, "MATRIX_DENSITY": 8.820467912752026e-05, "TIME_S": 22.412299394607544} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.6548, 0.3565, 0.5234, ..., 0.9677, 0.1150, 0.9054]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 22.412299394607544 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, ..., 86850, 86850, 86850]), + col_indices=tensor([ 1809, 21783, 106, ..., 7018, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=86850, layout=torch.sparse_csr) +tensor([0.6548, 0.3565, 0.5234, ..., 0.9677, 0.1150, 0.9054]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_030 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 86850 +Density: 8.820467912752026e-05 +Time: 22.412299394607544 seconds + +[19.16, 17.95, 18.66, 18.08, 17.98, 18.05, 18.54, 17.85, 18.66, 17.92] +[81.72] +25.033453226089478 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 218006, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 22.412299394607544, 'TIME_S_1KI': 0.10280588329957682, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2045.733797636032, 'W': 81.72} +[19.16, 17.95, 18.66, 18.08, 17.98, 18.05, 18.54, 17.85, 18.66, 17.92, 18.36, 18.02, 18.1, 17.76, 17.96, 18.02, 17.86, 18.16, 18.01, 17.95] +326.35499999999996 +16.317749999999997 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 218006, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_030', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 86850, 'MATRIX_DENSITY': 8.820467912752026e-05, 'TIME_S': 22.412299394607544, 'TIME_S_1KI': 0.10280588329957682, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2045.733797636032, 'W': 81.72, 'J_1KI': 9.38384171828313, 'W_1KI': 0.37485206829169837, 'W_D': 65.40225000000001, 'J_D': 1637.2441662560107, 'W_D_1KI': 0.30000206416337166, 'J_D_1KI': 0.0013761183828122697} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_035.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_035.json new file mode 100644 index 0000000..7e455bf --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_035.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 219377, "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": 21.53654432296753, "TIME_S_1KI": 0.09817138680430278, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2051.958555765152, "W": 82.06, "J_1KI": 9.353571959526988, "W_1KI": 0.37405926783573484, "W_D": 65.7215, "J_D": 1643.404755334139, "W_D_1KI": 0.29958245394913785, "J_D_1KI": 0.001365605573734429} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_035.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_035.output new file mode 100644 index 0000000..2621b58 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_035.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.1190633773803711} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3186, 0.9589, 0.6817, ..., 0.2100, 0.4075, 0.9660]) +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: 0.1190633773803711 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '176376', '-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": 16.883681058883667} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0443, 0.4897, 0.0137, ..., 0.6113, 0.7696, 0.5490]) +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: 16.883681058883667 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '219377', '-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": 21.53654432296753} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4291, 0.2282, 0.0124, ..., 0.2103, 0.7569, 0.0520]) +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: 21.53654432296753 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([0.4291, 0.2282, 0.0124, ..., 0.2103, 0.7569, 0.0520]) +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: 21.53654432296753 seconds + +[18.37, 17.97, 18.32, 17.94, 18.04, 18.04, 18.32, 18.03, 17.96, 18.07] +[82.06] +25.005588054656982 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 219377, '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': 21.53654432296753, 'TIME_S_1KI': 0.09817138680430278, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2051.958555765152, 'W': 82.06} +[18.37, 17.97, 18.32, 17.94, 18.04, 18.04, 18.32, 18.03, 17.96, 18.07, 18.26, 18.32, 18.37, 17.9, 18.02, 18.58, 17.98, 18.55, 18.1, 17.96] +326.77 +16.3385 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 219377, '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': 21.53654432296753, 'TIME_S_1KI': 0.09817138680430278, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2051.958555765152, 'W': 82.06, 'J_1KI': 9.353571959526988, 'W_1KI': 0.37405926783573484, 'W_D': 65.7215, 'J_D': 1643.404755334139, 'W_D_1KI': 0.29958245394913785, 'J_D_1KI': 0.001365605573734429} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_040.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_040.json new file mode 100644 index 0000000..e36688d --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_040.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 215092, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 22.442244291305542, "TIME_S_1KI": 0.1043378846786749, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2069.1111049985884, "W": 81.94, "J_1KI": 9.619656263359811, "W_1KI": 0.3809532665092147, "W_D": 65.52324999999999, "J_D": 1654.5629022528526, "W_D_1KI": 0.3046289494727837, "J_D_1KI": 0.001416272801744294} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_040.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_040.output new file mode 100644 index 0000000..f719ee7 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_040.output @@ -0,0 +1,105 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 0.11539793014526367} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.3295, 0.5506, 0.7748, ..., 0.3676, 0.4718, 0.0614]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 0.11539793014526367 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '181979', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 18.859267950057983} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.7765, 0.6004, 0.3426, ..., 0.7695, 0.3484, 0.0482]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 18.859267950057983 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '202635', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 19.78376579284668} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.2188, 0.3866, 0.7869, ..., 0.2421, 0.7063, 0.8571]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 19.78376579284668 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '215092', '-m', 'matrices/as-caida_pruned/as-caida_G_040.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_040", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 89658, "MATRIX_DENSITY": 9.105647807962247e-05, "TIME_S": 22.442244291305542} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.8589, 0.3970, 0.8621, ..., 0.4884, 0.0084, 0.8369]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 22.442244291305542 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, ..., 89657, 89657, 89658]), + col_indices=tensor([ 106, 329, 1040, ..., 10144, 882, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=89658, layout=torch.sparse_csr) +tensor([0.8589, 0.3970, 0.8621, ..., 0.4884, 0.0084, 0.8369]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_040 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 89658 +Density: 9.105647807962247e-05 +Time: 22.442244291305542 seconds + +[19.17, 17.95, 18.03, 17.98, 20.06, 17.82, 18.45, 18.16, 18.03, 18.19] +[81.94] +25.2515389919281 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 215092, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 22.442244291305542, 'TIME_S_1KI': 0.1043378846786749, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2069.1111049985884, 'W': 81.94} +[19.17, 17.95, 18.03, 17.98, 20.06, 17.82, 18.45, 18.16, 18.03, 18.19, 18.34, 18.13, 18.92, 17.88, 18.0, 18.06, 18.26, 17.84, 17.87, 18.09] +328.33500000000004 +16.41675 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 215092, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_040', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 89658, 'MATRIX_DENSITY': 9.105647807962247e-05, 'TIME_S': 22.442244291305542, 'TIME_S_1KI': 0.1043378846786749, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2069.1111049985884, 'W': 81.94, 'J_1KI': 9.619656263359811, 'W_1KI': 0.3809532665092147, 'W_D': 65.52324999999999, 'J_D': 1654.5629022528526, 'W_D_1KI': 0.3046289494727837, 'J_D_1KI': 0.001416272801744294} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_045.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_045.json new file mode 100644 index 0000000..4b8cf24 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_045.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 206065, "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": 20.589470148086548, "TIME_S_1KI": 0.09991735689266275, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1987.818736553192, "W": 82.1, "J_1KI": 9.64656169923661, "W_1KI": 0.3984179749108291, "W_D": 65.88325, "J_D": 1595.1761117541791, "W_D_1KI": 0.3197207191905467, "J_D_1KI": 0.0015515527585497134} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_045.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_045.output new file mode 100644 index 0000000..7327a33 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_045.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.1187739372253418} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1957, 0.2151, 0.4331, ..., 0.2230, 0.9948, 0.8136]) +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: 0.1187739372253418 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '176806', '-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": 18.018179655075073} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6595, 0.3622, 0.8081, ..., 0.5780, 0.5988, 0.5226]) +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: 18.018179655075073 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '206065', '-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": 20.589470148086548} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9602, 0.1628, 0.2657, ..., 0.1443, 0.4555, 0.7312]) +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: 20.589470148086548 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([0.9602, 0.1628, 0.2657, ..., 0.1443, 0.4555, 0.7312]) +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: 20.589470148086548 seconds + +[18.14, 17.99, 17.95, 17.91, 17.97, 17.78, 18.04, 17.92, 18.0, 17.9] +[82.1] +24.212164878845215 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 206065, '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': 20.589470148086548, 'TIME_S_1KI': 0.09991735689266275, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1987.818736553192, 'W': 82.1} +[18.14, 17.99, 17.95, 17.91, 17.97, 17.78, 18.04, 17.92, 18.0, 17.9, 18.43, 18.31, 18.24, 18.02, 18.05, 18.2, 17.98, 18.0, 17.88, 17.72] +324.335 +16.216749999999998 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 206065, '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': 20.589470148086548, 'TIME_S_1KI': 0.09991735689266275, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1987.818736553192, 'W': 82.1, 'J_1KI': 9.64656169923661, 'W_1KI': 0.3984179749108291, 'W_D': 65.88325, 'J_D': 1595.1761117541791, 'W_D_1KI': 0.3197207191905467, 'J_D_1KI': 0.0015515527585497134} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_050.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_050.json new file mode 100644 index 0000000..58bea04 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_050.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 202468, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 20.665090799331665, "TIME_S_1KI": 0.10206596004964569, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1958.7941827774048, "W": 82.0, "J_1KI": 9.674586516276175, "W_1KI": 0.40500227196396466, "W_D": 65.61525, "J_D": 1567.3996341644527, "W_D_1KI": 0.32407713811565286, "J_D_1KI": 0.001600633868639256} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_050.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_050.output new file mode 100644 index 0000000..bcd4683 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_050.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 0.1376354694366455} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.1109, 0.9986, 0.1088, ..., 0.8500, 0.3501, 0.7135]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 0.1376354694366455 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '152576', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 15.825188398361206} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.4280, 0.3440, 0.9593, ..., 0.9124, 0.8562, 0.2892]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 15.825188398361206 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '202468', '-m', 'matrices/as-caida_pruned/as-caida_G_050.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_050", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 90392, "MATRIX_DENSITY": 9.180192695100532e-05, "TIME_S": 20.665090799331665} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.4774, 0.3036, 0.0435, ..., 0.2639, 0.1396, 0.7889]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 20.665090799331665 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, ..., 90390, 90390, 90392]), + col_indices=tensor([ 5326, 106, 329, ..., 882, 2232, 16085]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=90392, layout=torch.sparse_csr) +tensor([0.4774, 0.3036, 0.0435, ..., 0.2639, 0.1396, 0.7889]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_050 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 90392 +Density: 9.180192695100532e-05 +Time: 20.665090799331665 seconds + +[18.39, 18.78, 18.0, 17.95, 18.04, 17.88, 18.09, 17.97, 18.22, 17.87] +[82.0] +23.887733936309814 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 202468, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 20.665090799331665, 'TIME_S_1KI': 0.10206596004964569, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1958.7941827774048, 'W': 82.0} +[18.39, 18.78, 18.0, 17.95, 18.04, 17.88, 18.09, 17.97, 18.22, 17.87, 18.46, 17.98, 18.08, 17.81, 20.28, 17.87, 18.39, 17.99, 18.03, 17.95] +327.69500000000005 +16.384750000000004 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 202468, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_050', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 90392, 'MATRIX_DENSITY': 9.180192695100532e-05, 'TIME_S': 20.665090799331665, 'TIME_S_1KI': 0.10206596004964569, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1958.7941827774048, 'W': 82.0, 'J_1KI': 9.674586516276175, 'W_1KI': 0.40500227196396466, 'W_D': 65.61525, 'J_D': 1567.3996341644527, 'W_D_1KI': 0.32407713811565286, 'J_D_1KI': 0.001600633868639256} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_055.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_055.json new file mode 100644 index 0000000..482996d --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_055.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 204802, "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": 20.279945373535156, "TIME_S_1KI": 0.09902220375550608, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1938.3870105481146, "W": 79.13, "J_1KI": 9.46468789634923, "W_1KI": 0.38637317994941456, "W_D": 62.906749999999995, "J_D": 1540.9784794110656, "W_D_1KI": 0.30715886563607775, "J_D_1KI": 0.0014997845022806308} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_055.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_055.output new file mode 100644 index 0000000..79dd92e --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_055.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.11484789848327637} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4070, 0.3856, 0.1102, ..., 0.7469, 0.7423, 0.4097]) +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: 0.11484789848327637 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '182850', '-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": 18.749075651168823} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8198, 0.5578, 0.2848, ..., 0.8271, 0.3889, 0.8491]) +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: 18.749075651168823 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '204802', '-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": 20.279945373535156} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.1394, 0.2388, 0.0790, ..., 0.4447, 0.8026, 0.9077]) +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: 20.279945373535156 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([0.1394, 0.2388, 0.0790, ..., 0.4447, 0.8026, 0.9077]) +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: 20.279945373535156 seconds + +[18.53, 17.81, 18.14, 17.86, 18.13, 17.99, 18.26, 17.9, 17.98, 18.0] +[79.13] +24.49623417854309 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 204802, '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': 20.279945373535156, 'TIME_S_1KI': 0.09902220375550608, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1938.3870105481146, 'W': 79.13} +[18.53, 17.81, 18.14, 17.86, 18.13, 17.99, 18.26, 17.9, 17.98, 18.0, 18.45, 18.02, 18.0, 18.08, 18.16, 18.02, 17.95, 17.74, 17.87, 18.13] +324.46500000000003 +16.22325 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 204802, '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': 20.279945373535156, 'TIME_S_1KI': 0.09902220375550608, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1938.3870105481146, 'W': 79.13, 'J_1KI': 9.46468789634923, 'W_1KI': 0.38637317994941456, 'W_D': 62.906749999999995, 'J_D': 1540.9784794110656, 'W_D_1KI': 0.30715886563607775, 'J_D_1KI': 0.0014997845022806308} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_060.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_060.json new file mode 100644 index 0000000..ce31612 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_060.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 217957, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 20.861847639083862, "TIME_S_1KI": 0.09571542845186831, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2121.2108467197418, "W": 82.47, "J_1KI": 9.732244647888077, "W_1KI": 0.37837738636520046, "W_D": 66.20474999999999, "J_D": 1702.85235606122, "W_D_1KI": 0.30375142803397, "J_D_1KI": 0.001393630064801635} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_060.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_060.output new file mode 100644 index 0000000..c1a6beb --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_060.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 0.11899280548095703} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.5857, 0.2929, 0.7315, ..., 0.4573, 0.4901, 0.1505]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 0.11899280548095703 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '176481', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 17.003808975219727} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.1442, 0.0365, 0.9251, ..., 0.4607, 0.0840, 0.2633]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 17.003808975219727 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '217957', '-m', 'matrices/as-caida_pruned/as-caida_G_060.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_060", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 94180, "MATRIX_DENSITY": 9.564901186217454e-05, "TIME_S": 20.861847639083862} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.6782, 0.8328, 0.6880, ..., 0.3480, 0.8726, 0.9179]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 20.861847639083862 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, ..., 94180, 94180, 94180]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 882]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=94180, layout=torch.sparse_csr) +tensor([0.6782, 0.8328, 0.6880, ..., 0.3480, 0.8726, 0.9179]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_060 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 94180 +Density: 9.564901186217454e-05 +Time: 20.861847639083862 seconds + +[18.48, 17.9, 18.3, 17.77, 18.29, 18.47, 18.15, 17.69, 17.98, 17.82] +[82.47] +25.720999717712402 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 217957, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 20.861847639083862, 'TIME_S_1KI': 0.09571542845186831, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2121.2108467197418, 'W': 82.47} +[18.48, 17.9, 18.3, 17.77, 18.29, 18.47, 18.15, 17.69, 17.98, 17.82, 18.58, 18.07, 17.93, 17.98, 18.31, 17.83, 17.96, 17.98, 18.27, 17.97] +325.30500000000006 +16.26525 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 217957, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_060', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 94180, 'MATRIX_DENSITY': 9.564901186217454e-05, 'TIME_S': 20.861847639083862, 'TIME_S_1KI': 0.09571542845186831, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2121.2108467197418, 'W': 82.47, 'J_1KI': 9.732244647888077, 'W_1KI': 0.37837738636520046, 'W_D': 66.20474999999999, 'J_D': 1702.85235606122, 'W_D_1KI': 0.30375142803397, 'J_D_1KI': 0.001393630064801635} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_065.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_065.json new file mode 100644 index 0000000..fa1fce8 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_065.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 208471, "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": 20.47442650794983, "TIME_S_1KI": 0.09821234851825832, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1952.2020723819733, "W": 81.88, "J_1KI": 9.364381963831772, "W_1KI": 0.39276446124400993, "W_D": 65.526, "J_D": 1562.2861870408058, "W_D_1KI": 0.3143170992608084, "J_D_1KI": 0.0015077257712622304} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_065.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_065.output new file mode 100644 index 0000000..aa488b7 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_065.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.1133120059967041} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.6312, 0.1724, 0.3505, ..., 0.1354, 0.5230, 0.1190]) +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: 0.1133120059967041 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '185328', '-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": 18.66867995262146} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.9996, 0.8294, 0.7872, ..., 0.5950, 0.0585, 0.0072]) +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: 18.66867995262146 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '208471', '-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": 20.47442650794983} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.5419, 0.5261, 0.0501, ..., 0.7930, 0.8181, 0.7038]) +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: 20.47442650794983 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([0.5419, 0.5261, 0.0501, ..., 0.7930, 0.8181, 0.7038]) +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: 20.47442650794983 seconds + +[18.38, 17.97, 18.04, 18.02, 17.92, 19.23, 17.93, 18.87, 18.15, 17.91] +[81.88] +23.842233419418335 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 208471, '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': 20.47442650794983, 'TIME_S_1KI': 0.09821234851825832, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1952.2020723819733, 'W': 81.88} +[18.38, 17.97, 18.04, 18.02, 17.92, 19.23, 17.93, 18.87, 18.15, 17.91, 18.17, 17.9, 18.08, 18.1, 18.16, 18.48, 18.07, 18.05, 17.96, 17.84] +327.08 +16.354 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 208471, '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': 20.47442650794983, 'TIME_S_1KI': 0.09821234851825832, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1952.2020723819733, 'W': 81.88, 'J_1KI': 9.364381963831772, 'W_1KI': 0.39276446124400993, 'W_D': 65.526, 'J_D': 1562.2861870408058, 'W_D_1KI': 0.3143170992608084, 'J_D_1KI': 0.0015077257712622304} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_070.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_070.json new file mode 100644 index 0000000..70131f6 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_070.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 222464, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 21.104984283447266, "TIME_S_1KI": 0.09486921157332091, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2079.4145326662065, "W": 81.91000000000001, "J_1KI": 9.347195648132761, "W_1KI": 0.3681944044879172, "W_D": 63.60175000000001, "J_D": 1614.6307319375278, "W_D_1KI": 0.2858968192606445, "J_D_1KI": 0.001285137457119554} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_070.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_070.output new file mode 100644 index 0000000..271fe63 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_070.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 0.10785865783691406} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.3281, 0.3327, 0.1628, ..., 0.2001, 0.4781, 0.4372]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 0.10785865783691406 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '194699', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 18.378977298736572} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.8721, 0.3904, 0.6224, ..., 0.1233, 0.7073, 0.5729]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 18.378977298736572 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '222464', '-m', 'matrices/as-caida_pruned/as-caida_G_070.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_070", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 78684, "MATRIX_DENSITY": 7.991130653390679e-05, "TIME_S": 21.104984283447266} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.2737, 0.9652, 0.3124, ..., 0.6712, 0.6161, 0.6195]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 21.104984283447266 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, ..., 78684, 78684, 78684]), + col_indices=tensor([ 106, 329, 1040, ..., 16263, 2242, 2242]), + values=tensor([1., 1., 1., ..., 3., 1., 1.]), size=(31379, 31379), + nnz=78684, layout=torch.sparse_csr) +tensor([0.2737, 0.9652, 0.3124, ..., 0.6712, 0.6161, 0.6195]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_070 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 78684 +Density: 7.991130653390679e-05 +Time: 21.104984283447266 seconds + +[18.24, 18.03, 17.75, 17.93, 17.95, 18.09, 18.08, 17.83, 18.05, 18.14] +[81.91] +25.386577129364014 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 222464, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 21.104984283447266, 'TIME_S_1KI': 0.09486921157332091, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2079.4145326662065, 'W': 81.91000000000001} +[18.24, 18.03, 17.75, 17.93, 17.95, 18.09, 18.08, 17.83, 18.05, 18.14, 18.62, 18.47, 17.99, 18.15, 18.29, 19.92, 18.0, 22.58, 41.78, 39.55] +366.16499999999996 +18.308249999999997 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 222464, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_070', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 78684, 'MATRIX_DENSITY': 7.991130653390679e-05, 'TIME_S': 21.104984283447266, 'TIME_S_1KI': 0.09486921157332091, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2079.4145326662065, 'W': 81.91000000000001, 'J_1KI': 9.347195648132761, 'W_1KI': 0.3681944044879172, 'W_D': 63.60175000000001, 'J_D': 1614.6307319375278, 'W_D_1KI': 0.2858968192606445, 'J_D_1KI': 0.001285137457119554} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_075.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_075.json new file mode 100644 index 0000000..a61f720 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_075.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 212896, "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": 21.713075876235962, "TIME_S_1KI": 0.10198912086763473, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2054.0308270454407, "W": 82.35, "J_1KI": 9.648048000175864, "W_1KI": 0.38680858259431833, "W_D": 65.97574999999999, "J_D": 1645.6129245591162, "W_D_1KI": 0.30989661618818576, "J_D_1KI": 0.0014556244184399227} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_075.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_075.output new file mode 100644 index 0000000..4f1f751 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_075.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.11899256706237793} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2831, 0.8825, 0.6935, ..., 0.0450, 0.7057, 0.7008]) +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: 0.11899256706237793 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '176481', '-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": 17.40796184539795} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.5092, 0.1620, 0.8266, ..., 0.5276, 0.2147, 0.7524]) +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: 17.40796184539795 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '212896', '-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": 21.713075876235962} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0454, 0.0771, 0.2860, ..., 0.4067, 0.4639, 0.0640]) +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: 21.713075876235962 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([0.0454, 0.0771, 0.2860, ..., 0.4067, 0.4639, 0.0640]) +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: 21.713075876235962 seconds + +[18.19, 17.99, 18.3, 17.93, 18.15, 17.76, 20.19, 17.72, 18.43, 17.99] +[82.35] +24.94269371032715 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 212896, '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': 21.713075876235962, 'TIME_S_1KI': 0.10198912086763473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2054.0308270454407, 'W': 82.35} +[18.19, 17.99, 18.3, 17.93, 18.15, 17.76, 20.19, 17.72, 18.43, 17.99, 18.35, 18.12, 18.02, 17.97, 18.28, 18.05, 18.06, 18.15, 18.04, 18.12] +327.485 +16.37425 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 212896, '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': 21.713075876235962, 'TIME_S_1KI': 0.10198912086763473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2054.0308270454407, 'W': 82.35, 'J_1KI': 9.648048000175864, 'W_1KI': 0.38680858259431833, 'W_D': 65.97574999999999, 'J_D': 1645.6129245591162, 'W_D_1KI': 0.30989661618818576, 'J_D_1KI': 0.0014556244184399227} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_080.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_080.json new file mode 100644 index 0000000..acf1dbd --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_080.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 203300, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 20.364664554595947, "TIME_S_1KI": 0.10017050936840112, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2026.8266043567658, "W": 82.34, "J_1KI": 9.969634059797176, "W_1KI": 0.4050172159370389, "W_D": 65.99275, "J_D": 1624.433585070014, "W_D_1KI": 0.3246077225774717, "J_D_1KI": 0.0015966931754917448} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_080.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_080.output new file mode 100644 index 0000000..417075b --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_080.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 0.11817717552185059} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.7594, 0.3925, 0.7127, ..., 0.8236, 0.2734, 0.4681]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 0.11817717552185059 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '177699', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 18.35543918609619} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.3060, 0.3630, 0.5975, ..., 0.7405, 0.5921, 0.7410]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 18.35543918609619 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '203300', '-m', 'matrices/as-caida_pruned/as-caida_G_080.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_080", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 98112, "MATRIX_DENSITY": 9.964234287345156e-05, "TIME_S": 20.364664554595947} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.9122, 0.9488, 0.7356, ..., 0.5872, 0.9648, 0.9938]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 20.364664554595947 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, ..., 98111, 98111, 98112]), + col_indices=tensor([22754, 22754, 106, ..., 4133, 31329, 12170]), + values=tensor([1., 1., 1., ..., 3., 3., 1.]), size=(31379, 31379), + nnz=98112, layout=torch.sparse_csr) +tensor([0.9122, 0.9488, 0.7356, ..., 0.5872, 0.9648, 0.9938]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_080 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 98112 +Density: 9.964234287345156e-05 +Time: 20.364664554595947 seconds + +[18.11, 17.85, 18.16, 18.86, 18.09, 17.97, 18.29, 17.86, 18.03, 17.83] +[82.34] +24.615334033966064 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 203300, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 20.364664554595947, 'TIME_S_1KI': 0.10017050936840112, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2026.8266043567658, 'W': 82.34} +[18.11, 17.85, 18.16, 18.86, 18.09, 17.97, 18.29, 17.86, 18.03, 17.83, 18.39, 18.08, 17.9, 17.79, 17.96, 19.63, 17.94, 18.13, 18.15, 18.18] +326.945 +16.34725 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 203300, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_080', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 98112, 'MATRIX_DENSITY': 9.964234287345156e-05, 'TIME_S': 20.364664554595947, 'TIME_S_1KI': 0.10017050936840112, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2026.8266043567658, 'W': 82.34, 'J_1KI': 9.969634059797176, 'W_1KI': 0.4050172159370389, 'W_D': 65.99275, 'J_D': 1624.433585070014, 'W_D_1KI': 0.3246077225774717, 'J_D_1KI': 0.0015966931754917448} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_085.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_085.json new file mode 100644 index 0000000..c69e4b4 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_085.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 220027, "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": 21.298619270324707, "TIME_S_1KI": 0.09680002577104041, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2064.001230354309, "W": 82.17, "J_1KI": 9.38067250998427, "W_1KI": 0.37345416698859685, "W_D": 65.743, "J_D": 1651.3768149833677, "W_D_1KI": 0.29879514786821615, "J_D_1KI": 0.0013579931002477702} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_085.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_085.output new file mode 100644 index 0000000..6efad18 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_085.output @@ -0,0 +1,85 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.11109638214111328} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0464, 0.3765, 0.8126, ..., 0.4782, 0.8440, 0.9012]) +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: 0.11109638214111328 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '189025', '-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": 18.04103922843933} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.2631, 0.8684, 0.6795, ..., 0.8756, 0.6868, 0.3104]) +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: 18.04103922843933 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '220027', '-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": 21.298619270324707} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.5380, 0.5707, 0.0168, ..., 0.9158, 0.5081, 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: 21.298619270324707 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([0.5380, 0.5707, 0.0168, ..., 0.9158, 0.5081, 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: 21.298619270324707 seconds + +[18.75, 18.24, 18.78, 18.27, 19.12, 18.2, 18.44, 18.13, 18.33, 18.18] +[82.17] +25.118671417236328 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 220027, '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': 21.298619270324707, 'TIME_S_1KI': 0.09680002577104041, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2064.001230354309, 'W': 82.17} +[18.75, 18.24, 18.78, 18.27, 19.12, 18.2, 18.44, 18.13, 18.33, 18.18, 18.6, 17.97, 18.17, 18.0, 18.2, 18.14, 17.9, 17.95, 17.93, 18.01] +328.53999999999996 +16.427 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 220027, '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': 21.298619270324707, 'TIME_S_1KI': 0.09680002577104041, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2064.001230354309, 'W': 82.17, 'J_1KI': 9.38067250998427, 'W_1KI': 0.37345416698859685, 'W_D': 65.743, 'J_D': 1651.3768149833677, 'W_D_1KI': 0.29879514786821615, 'J_D_1KI': 0.0013579931002477702} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_090.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_090.json new file mode 100644 index 0000000..5bef5a0 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_090.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 216121, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 21.48875379562378, "TIME_S_1KI": 0.09942927247062423, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2125.561991868019, "W": 78.31, "J_1KI": 9.835055324878281, "W_1KI": 0.36234331693819666, "W_D": 61.91275, "J_D": 1680.492762252927, "W_D_1KI": 0.2864726241318521, "J_D_1KI": 0.0013255196123090865} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_090.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_090.output new file mode 100644 index 0000000..d535a99 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_090.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 0.11545324325561523} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.0933, 0.5542, 0.0876, ..., 0.1058, 0.2255, 0.9559]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 0.11545324325561523 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '181891', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 17.673869609832764} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.2893, 0.4058, 0.4231, ..., 0.7246, 0.8798, 0.1051]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 17.673869609832764 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '216121', '-m', 'matrices/as-caida_pruned/as-caida_G_090.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_090", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 100924, "MATRIX_DENSITY": 0.00010249820421722343, "TIME_S": 21.48875379562378} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.7750, 0.2865, 0.6853, ..., 0.9402, 0.8394, 0.1691]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 21.48875379562378 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, ..., 100923, 100923, + 100924]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 31211, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=100924, layout=torch.sparse_csr) +tensor([0.7750, 0.2865, 0.6853, ..., 0.9402, 0.8394, 0.1691]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_090 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 100924 +Density: 0.00010249820421722343 +Time: 21.48875379562378 seconds + +[18.56, 18.09, 18.16, 17.99, 17.94, 17.97, 18.06, 18.5, 17.97, 17.96] +[78.31] +27.142919063568115 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 216121, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 21.48875379562378, 'TIME_S_1KI': 0.09942927247062423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2125.561991868019, 'W': 78.31} +[18.56, 18.09, 18.16, 17.99, 17.94, 17.97, 18.06, 18.5, 17.97, 17.96, 18.37, 17.98, 18.22, 17.84, 18.08, 19.76, 18.43, 17.88, 18.61, 18.04] +327.94500000000005 +16.397250000000003 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 216121, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_090', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 100924, 'MATRIX_DENSITY': 0.00010249820421722343, 'TIME_S': 21.48875379562378, 'TIME_S_1KI': 0.09942927247062423, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2125.561991868019, 'W': 78.31, 'J_1KI': 9.835055324878281, 'W_1KI': 0.36234331693819666, 'W_D': 61.91275, 'J_D': 1680.492762252927, 'W_D_1KI': 0.2864726241318521, 'J_D_1KI': 0.0013255196123090865} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_095.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_095.json new file mode 100644 index 0000000..d031744 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_095.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 211727, "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": 20.957764387130737, "TIME_S_1KI": 0.09898484551866667, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2090.9206402587893, "W": 82.56, "J_1KI": 9.875550308929846, "W_1KI": 0.38993609695504117, "W_D": 66.18125, "J_D": 1676.1112115204335, "W_D_1KI": 0.3125782257340821, "J_D_1KI": 0.0014763267119171485} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_095.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_095.output new file mode 100644 index 0000000..7afd330 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_095.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.1134791374206543} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.7733, 0.4912, 0.8606, ..., 0.2884, 0.0436, 0.6421]) +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: 0.1134791374206543 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '185056', '-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": 18.354573488235474} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3335, 0.6556, 0.1290, ..., 0.6703, 0.6803, 0.0499]) +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: 18.354573488235474 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '211727', '-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": 20.957764387130737} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0803, 0.7105, 0.5871, ..., 0.3244, 0.3191, 0.2965]) +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: 20.957764387130737 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([0.0803, 0.7105, 0.5871, ..., 0.3244, 0.3191, 0.2965]) +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: 20.957764387130737 seconds + +[18.28, 18.29, 18.2, 18.08, 18.1, 17.86, 17.99, 17.94, 17.9, 20.1] +[82.56] +25.32607364654541 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 211727, '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': 20.957764387130737, 'TIME_S_1KI': 0.09898484551866667, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2090.9206402587893, 'W': 82.56} +[18.28, 18.29, 18.2, 18.08, 18.1, 17.86, 17.99, 17.94, 17.9, 20.1, 18.63, 18.07, 18.32, 17.89, 18.02, 18.01, 18.56, 18.72, 18.07, 18.1] +327.57500000000005 +16.378750000000004 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 211727, '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': 20.957764387130737, 'TIME_S_1KI': 0.09898484551866667, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2090.9206402587893, 'W': 82.56, 'J_1KI': 9.875550308929846, 'W_1KI': 0.38993609695504117, 'W_D': 66.18125, 'J_D': 1676.1112115204335, 'W_D_1KI': 0.3125782257340821, 'J_D_1KI': 0.0014763267119171485} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_100.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_100.json new file mode 100644 index 0000000..164390e --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_100.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 213619, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 20.809861183166504, "TIME_S_1KI": 0.09741577848022182, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2062.2136768746373, "W": 82.57, "J_1KI": 9.653699703091192, "W_1KI": 0.38652928812511994, "W_D": 66.27175, "J_D": 1655.1593707208037, "W_D_1KI": 0.31023340620450424, "J_D_1KI": 0.0014522744053876492} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_100.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_100.output new file mode 100644 index 0000000..58a8083 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_100.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 0.11469888687133789} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.7504, 0.8370, 0.5428, ..., 0.9204, 0.3297, 0.6496]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 0.11469888687133789 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '183088', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 17.998604774475098} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.2964, 0.9717, 0.1718, ..., 0.1228, 0.8968, 0.8841]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 17.998604774475098 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '213619', '-m', 'matrices/as-caida_pruned/as-caida_G_100.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_100", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 102888, "MATRIX_DENSITY": 0.00010449283852702711, "TIME_S": 20.809861183166504} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.9750, 0.7284, 0.4235, ..., 0.1875, 0.6181, 0.5107]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 20.809861183166504 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, ..., 102886, 102887, + 102888]), + col_indices=tensor([ 106, 329, 1040, ..., 25970, 5128, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=102888, layout=torch.sparse_csr) +tensor([0.9750, 0.7284, 0.4235, ..., 0.1875, 0.6181, 0.5107]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_100 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 102888 +Density: 0.00010449283852702711 +Time: 20.809861183166504 seconds + +[18.32, 18.08, 17.92, 17.81, 18.01, 17.72, 17.98, 17.71, 18.21, 17.82] +[82.57] +24.975338220596313 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 213619, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 20.809861183166504, 'TIME_S_1KI': 0.09741577848022182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2062.2136768746373, 'W': 82.57} +[18.32, 18.08, 17.92, 17.81, 18.01, 17.72, 17.98, 17.71, 18.21, 17.82, 19.32, 18.61, 17.87, 18.45, 17.93, 17.79, 18.24, 18.3, 17.94, 19.33] +325.965 +16.29825 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 213619, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_100', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 102888, 'MATRIX_DENSITY': 0.00010449283852702711, 'TIME_S': 20.809861183166504, 'TIME_S_1KI': 0.09741577848022182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2062.2136768746373, 'W': 82.57, 'J_1KI': 9.653699703091192, 'W_1KI': 0.38652928812511994, 'W_D': 66.27175, 'J_D': 1655.1593707208037, 'W_D_1KI': 0.31023340620450424, 'J_D_1KI': 0.0014522744053876492} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_105.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_105.json new file mode 100644 index 0000000..48edf22 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_105.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 202484, "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": 20.52411675453186, "TIME_S_1KI": 0.10136167180879409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1967.2744417190552, "W": 82.17, "J_1KI": 9.715703175159792, "W_1KI": 0.4058098417652753, "W_D": 65.84175, "J_D": 1576.3513687849045, "W_D_1KI": 0.32517013689970564, "J_D_1KI": 0.0016059053401735724} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_105.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_105.output new file mode 100644 index 0000000..409cb73 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_105.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.11512088775634766} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.8098, 0.5667, 0.0739, ..., 0.0975, 0.6256, 0.2299]) +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: 0.11512088775634766 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '182416', '-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": 18.918691635131836} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3207, 0.7168, 0.3935, ..., 0.8006, 0.9040, 0.3242]) +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: 18.918691635131836 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '202484', '-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": 20.52411675453186} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0689, 0.9268, 0.7116, ..., 0.8609, 0.6797, 0.2492]) +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: 20.52411675453186 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([0.0689, 0.9268, 0.7116, ..., 0.8609, 0.6797, 0.2492]) +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: 20.52411675453186 seconds + +[18.32, 18.6, 18.14, 18.11, 18.05, 17.8, 18.18, 17.85, 17.96, 17.82] +[82.17] +23.941516876220703 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 202484, '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': 20.52411675453186, 'TIME_S_1KI': 0.10136167180879409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1967.2744417190552, 'W': 82.17} +[18.32, 18.6, 18.14, 18.11, 18.05, 17.8, 18.18, 17.85, 17.96, 17.82, 18.46, 18.13, 18.34, 18.69, 18.07, 18.03, 18.08, 17.93, 18.29, 18.03] +326.56499999999994 +16.328249999999997 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 202484, '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': 20.52411675453186, 'TIME_S_1KI': 0.10136167180879409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1967.2744417190552, 'W': 82.17, 'J_1KI': 9.715703175159792, 'W_1KI': 0.4058098417652753, 'W_D': 65.84175, 'J_D': 1576.3513687849045, 'W_D_1KI': 0.32517013689970564, 'J_D_1KI': 0.0016059053401735724} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_110.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_110.json new file mode 100644 index 0000000..e8d199c --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_110.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 207629, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 20.774281978607178, "TIME_S_1KI": 0.1000548188288109, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1987.6400883245467, "W": 82.46, "J_1KI": 9.573036947269152, "W_1KI": 0.3971506870427541, "W_D": 66.1225, "J_D": 1593.836184092164, "W_D_1KI": 0.3184646653405835, "J_D_1KI": 0.0015338159184920387} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_110.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_110.output new file mode 100644 index 0000000..32cf278 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_110.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 0.11849784851074219} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.9275, 0.6962, 0.0856, ..., 0.5775, 0.8533, 0.9888]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 0.11849784851074219 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '177218', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 17.924124717712402} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.9347, 0.7121, 0.0216, ..., 0.4738, 0.0498, 0.0135]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 17.924124717712402 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '207629', '-m', 'matrices/as-caida_pruned/as-caida_G_110.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_110", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 104846, "MATRIX_DENSITY": 0.0001064813792493263, "TIME_S": 20.774281978607178} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.7195, 0.4914, 0.0827, ..., 0.5005, 0.9828, 0.7271]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 20.774281978607178 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, ..., 104844, 104844, + 104846]), + col_indices=tensor([ 106, 329, 1040, ..., 882, 2616, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=104846, layout=torch.sparse_csr) +tensor([0.7195, 0.4914, 0.0827, ..., 0.5005, 0.9828, 0.7271]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_110 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 104846 +Density: 0.0001064813792493263 +Time: 20.774281978607178 seconds + +[18.32, 17.89, 19.83, 17.99, 18.2, 18.04, 18.03, 18.24, 17.9, 18.11] +[82.46] +24.104294061660767 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 207629, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 20.774281978607178, 'TIME_S_1KI': 0.1000548188288109, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1987.6400883245467, 'W': 82.46} +[18.32, 17.89, 19.83, 17.99, 18.2, 18.04, 18.03, 18.24, 17.9, 18.11, 18.37, 18.09, 18.23, 18.05, 17.99, 17.89, 18.09, 17.89, 18.07, 17.86] +326.75 +16.3375 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 207629, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_110', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 104846, 'MATRIX_DENSITY': 0.0001064813792493263, 'TIME_S': 20.774281978607178, 'TIME_S_1KI': 0.1000548188288109, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1987.6400883245467, 'W': 82.46, 'J_1KI': 9.573036947269152, 'W_1KI': 0.3971506870427541, 'W_D': 66.1225, 'J_D': 1593.836184092164, 'W_D_1KI': 0.3184646653405835, 'J_D_1KI': 0.0015338159184920387} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_115.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_115.json new file mode 100644 index 0000000..0436844 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_115.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 199258, "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": 20.110419034957886, "TIME_S_1KI": 0.1009265326107754, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1964.6470641946792, "W": 82.41, "J_1KI": 9.85981523549709, "W_1KI": 0.41358439811701414, "W_D": 66.136, "J_D": 1576.6763528404235, "W_D_1KI": 0.3319113912615804, "J_D_1KI": 0.0016657368399842437} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_115.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_115.output new file mode 100644 index 0000000..7e5b560 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_115.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-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": 0.12461400032043457} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.0740, 0.3053, 0.4171, ..., 0.4152, 0.3455, 0.5968]) +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: 0.12461400032043457 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '168520', '-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": 17.76044273376465} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.4974, 0.2069, 0.6967, ..., 0.9608, 0.8042, 0.6406]) +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: 17.76044273376465 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '199258', '-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": 20.110419034957886} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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([0.3838, 0.3555, 0.1228, ..., 0.9017, 0.4931, 0.1986]) +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: 20.110419034957886 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([0.3838, 0.3555, 0.1228, ..., 0.9017, 0.4931, 0.1986]) +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: 20.110419034957886 seconds + +[18.58, 17.98, 18.34, 18.14, 17.9, 17.86, 17.88, 17.97, 18.6, 18.68] +[82.41] +23.839910984039307 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 199258, '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': 20.110419034957886, 'TIME_S_1KI': 0.1009265326107754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1964.6470641946792, 'W': 82.41} +[18.58, 17.98, 18.34, 18.14, 17.9, 17.86, 17.88, 17.97, 18.6, 18.68, 18.48, 18.35, 17.98, 17.97, 18.04, 17.81, 18.08, 17.8, 18.0, 17.82] +325.48 +16.274 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 199258, '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': 20.110419034957886, 'TIME_S_1KI': 0.1009265326107754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1964.6470641946792, 'W': 82.41, 'J_1KI': 9.85981523549709, 'W_1KI': 0.41358439811701414, 'W_D': 66.136, 'J_D': 1576.6763528404235, 'W_D_1KI': 0.3319113912615804, 'J_D_1KI': 0.0016657368399842437} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_120.json b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_120.json new file mode 100644 index 0000000..2bdce81 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_120.json @@ -0,0 +1 @@ +{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 208039, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 21.32126784324646, "TIME_S_1KI": 0.10248687911039016, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2066.483355734348, "W": 82.91, "J_1KI": 9.933153667025644, "W_1KI": 0.39853104465989553, "W_D": 66.4915, "J_D": 1657.2618266531229, "W_D_1KI": 0.31961074606203643, "J_D_1KI": 0.0015363020686603784} diff --git a/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_120.output b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_120.output new file mode 100644 index 0000000..2201fa7 --- /dev/null +++ b/pytorch/output_as-caida_maxcore/xeon_4216_max_csr_20_10_10_as-caida_G_120.output @@ -0,0 +1,89 @@ +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '1000', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 0.11581158638000488} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.6495, 0.2558, 0.6599, ..., 0.9891, 0.9990, 0.0108]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 0.11581158638000488 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '181329', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 18.303788900375366} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.7161, 0.0320, 0.4861, ..., 0.7523, 0.2687, 0.5034]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 18.303788900375366 seconds + +['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'suitesparse', 'csr', '208039', '-m', 'matrices/as-caida_pruned/as-caida_G_120.mtx'] +{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_120", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 106510, "MATRIX_DENSITY": 0.0001081713341839054, "TIME_S": 21.32126784324646} + +/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.0932, 0.8762, 0.0737, ..., 0.3695, 0.8725, 0.6975]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 21.32126784324646 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, ..., 106509, 106509, + 106510]), + col_indices=tensor([ 106, 329, 1040, ..., 155, 160, 12170]), + values=tensor([1., 1., 1., ..., 1., 1., 1.]), size=(31379, 31379), + nnz=106510, layout=torch.sparse_csr) +tensor([0.0932, 0.8762, 0.0737, ..., 0.3695, 0.8725, 0.6975]) +Matrix Type: SuiteSparse +Matrix: as-caida_G_120 +Matrix Format: csr +Shape: torch.Size([31379, 31379]) +Rows: 31379 +Size: 984641641 +NNZ: 106510 +Density: 0.0001081713341839054 +Time: 21.32126784324646 seconds + +[18.62, 18.13, 18.26, 17.97, 18.22, 18.17, 18.16, 18.05, 20.15, 17.97] +[82.91] +24.924416303634644 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 208039, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 21.32126784324646, 'TIME_S_1KI': 0.10248687911039016, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2066.483355734348, 'W': 82.91} +[18.62, 18.13, 18.26, 17.97, 18.22, 18.17, 18.16, 18.05, 20.15, 17.97, 18.52, 18.64, 18.06, 18.07, 17.97, 17.99, 17.9, 18.05, 18.08, 17.89] +328.37 +16.4185 +{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 208039, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_120', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 106510, 'MATRIX_DENSITY': 0.0001081713341839054, 'TIME_S': 21.32126784324646, 'TIME_S_1KI': 0.10248687911039016, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2066.483355734348, 'W': 82.91, 'J_1KI': 9.933153667025644, 'W_1KI': 0.39853104465989553, 'W_D': 66.4915, 'J_D': 1657.2618266531229, 'W_D_1KI': 0.31961074606203643, 'J_D_1KI': 0.0015363020686603784}