Added more data points
This commit is contained in:
parent
a1af08726d
commit
8e7cfec71c
@ -117,7 +117,7 @@ elif args.matrix_type == MatrixType.SYNTHETIC:
|
||||
parameter_list = enumerate([(size, density)
|
||||
for size in args.synthetic_size
|
||||
for density in args.synthetic_density
|
||||
if size ** 2 * density <= 10000000])
|
||||
if size ** 2 * density <= 20000000])
|
||||
|
||||
#for i, matrix in enumerate(glob.glob(f'{args.matrix_dir.rstrip("/")}/*.mtx')):
|
||||
for i, parameter in parameter_list:
|
||||
@ -151,6 +151,10 @@ for i, parameter in parameter_list:
|
||||
|
||||
json_filepath = f'{args.output_dir.rstrip("/")}/{output_filename}.json'
|
||||
raw_filepath = f'{args.output_dir.rstrip("/")}/{output_filename}.output'
|
||||
if os.path.isfile(json_filepath) or os.path.isfile(raw_filepath):
|
||||
print("File " + json_filepath + " already exists! Skipping...")
|
||||
continue
|
||||
|
||||
with open(json_filepath, 'w') as json_file, open(raw_filepath, 'w') as raw_file:
|
||||
print(json_filepath)
|
||||
print(raw_filepath)
|
||||
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3268, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.551287174224854, "TIME_S_1KI": 3.2286680459684374, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 514.9588033294677, "W": 35.14704993762719, "J_1KI": 157.57613320975145, "W_1KI": 10.754911241623986, "W_D": 16.333049937627187, "J_D": 239.30451817512508, "W_D_1KI": 4.997873297927536, "J_D_1KI": 1.5293369944698703}
|
||||
@ -0,0 +1,65 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 100000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.21274733543396}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 5, 10, ..., 499991, 499996,
|
||||
500000]),
|
||||
col_indices=tensor([ 6819, 16249, 65142, ..., 35181, 90238, 95591]),
|
||||
values=tensor([0.9907, 0.7784, 0.8470, ..., 0.0401, 0.4552, 0.5172]),
|
||||
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
|
||||
tensor([0.1211, 0.3699, 0.8120, ..., 0.3387, 0.3308, 0.0427])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([100000, 100000])
|
||||
Rows: 100000
|
||||
Size: 10000000000
|
||||
NNZ: 500000
|
||||
Density: 5e-05
|
||||
Time: 3.21274733543396 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3268 -ss 100000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.551287174224854}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 6, 10, ..., 499988, 499994,
|
||||
500000]),
|
||||
col_indices=tensor([ 4698, 29712, 45324, ..., 47109, 54294, 79244]),
|
||||
values=tensor([0.0467, 0.0395, 0.2018, ..., 0.4601, 0.1623, 0.8954]),
|
||||
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
|
||||
tensor([0.1366, 0.5632, 0.5706, ..., 0.2593, 0.9938, 0.0917])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([100000, 100000])
|
||||
Rows: 100000
|
||||
Size: 10000000000
|
||||
NNZ: 500000
|
||||
Density: 5e-05
|
||||
Time: 10.551287174224854 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, 6, 10, ..., 499988, 499994,
|
||||
500000]),
|
||||
col_indices=tensor([ 4698, 29712, 45324, ..., 47109, 54294, 79244]),
|
||||
values=tensor([0.0467, 0.0395, 0.2018, ..., 0.4601, 0.1623, 0.8954]),
|
||||
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
|
||||
tensor([0.1366, 0.5632, 0.5706, ..., 0.2593, 0.9938, 0.0917])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([100000, 100000])
|
||||
Rows: 100000
|
||||
Size: 10000000000
|
||||
NNZ: 500000
|
||||
Density: 5e-05
|
||||
Time: 10.551287174224854 seconds
|
||||
|
||||
[20.52, 20.48, 20.56, 20.6, 20.6, 20.88, 21.08, 21.04, 21.04, 20.92]
|
||||
[20.68, 20.64, 20.84, 22.28, 23.8, 29.96, 35.72, 42.6, 47.2, 50.88, 50.56, 50.96, 50.68, 50.8]
|
||||
14.651551246643066
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.551287174224854, 'TIME_S_1KI': 3.2286680459684374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 514.9588033294677, 'W': 35.14704993762719}
|
||||
[20.52, 20.48, 20.56, 20.6, 20.6, 20.88, 21.08, 21.04, 21.04, 20.92, 20.72, 21.08, 21.16, 21.16, 21.24, 21.28, 21.16, 20.88, 20.68, 20.56]
|
||||
376.28
|
||||
18.814
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.551287174224854, 'TIME_S_1KI': 3.2286680459684374, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 514.9588033294677, 'W': 35.14704993762719, 'J_1KI': 157.57613320975145, 'W_1KI': 10.754911241623986, 'W_D': 16.333049937627187, 'J_D': 239.30451817512508, 'W_D_1KI': 4.997873297927536, 'J_D_1KI': 1.5293369944698703}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 424.4422960281372, "TIME_S_1KI": 424.4422960281372, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 10577.914468402869, "W": 24.381232025818772, "J_1KI": 10577.914468402869, "W_1KI": 24.381232025818772, "W_D": 5.810232025818774, "J_D": 2520.797035424717, "W_D_1KI": 5.810232025818774, "J_D_1KI": 5.810232025818774}
|
||||
@ -0,0 +1,45 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 0.2 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 424.4422960281372}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1997, 3940, ..., 19995978,
|
||||
19998013, 20000000]),
|
||||
col_indices=tensor([ 1, 6, 9, ..., 9994, 9996, 9997]),
|
||||
values=tensor([0.5415, 0.5931, 0.4382, ..., 0.2191, 0.0907, 0.5464]),
|
||||
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
|
||||
tensor([0.6696, 0.2101, 0.2330, ..., 0.2876, 0.3503, 0.9145])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 20000000
|
||||
Density: 0.2
|
||||
Time: 424.4422960281372 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, 1997, 3940, ..., 19995978,
|
||||
19998013, 20000000]),
|
||||
col_indices=tensor([ 1, 6, 9, ..., 9994, 9996, 9997]),
|
||||
values=tensor([0.5415, 0.5931, 0.4382, ..., 0.2191, 0.0907, 0.5464]),
|
||||
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
|
||||
tensor([0.6696, 0.2101, 0.2330, ..., 0.2876, 0.3503, 0.9145])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 20000000
|
||||
Density: 0.2
|
||||
Time: 424.4422960281372 seconds
|
||||
|
||||
[20.28, 20.2, 20.08, 20.08, 20.28, 20.44, 21.04, 20.84, 20.88, 20.76]
|
||||
[20.6, 20.28, 23.28, 24.56, 27.04, 28.72, 32.56, 32.56, 30.16, 30.32, 30.0, 27.84, 26.92, 26.16, 25.36, 24.2, 24.52, 24.6, 24.56, 24.56, 24.44, 24.36, 24.2, 24.16, 24.32, 24.52, 24.8, 24.88, 24.84, 24.96, 24.88, 24.88, 24.88, 24.68, 24.56, 24.52, 24.56, 24.52, 24.56, 24.6, 24.32, 24.28, 24.24, 24.36, 24.36, 24.44, 24.44, 24.36, 24.36, 24.36, 24.36, 24.4, 24.4, 24.56, 24.64, 24.68, 24.68, 24.92, 24.88, 24.92, 24.88, 24.8, 24.56, 24.56, 24.6, 24.4, 24.32, 24.44, 24.4, 24.4, 24.4, 24.52, 24.6, 24.64, 24.64, 24.36, 24.44, 24.32, 24.36, 24.32, 24.36, 24.36, 24.28, 24.36, 24.24, 24.4, 24.52, 24.72, 24.76, 24.68, 24.68, 24.24, 24.44, 24.32, 24.32, 24.48, 24.52, 24.4, 24.32, 24.24, 24.2, 24.4, 24.56, 24.64, 24.84, 24.68, 24.68, 24.68, 24.6, 24.6, 24.64, 25.0, 24.76, 24.56, 24.52, 24.4, 24.44, 24.56, 24.56, 24.72, 25.0, 24.92, 24.88, 25.04, 24.68, 24.52, 24.52, 24.32, 24.16, 24.08, 24.12, 24.12, 24.32, 24.52, 24.64, 24.8, 24.84, 24.72, 25.04, 24.92, 24.76, 24.76, 24.64, 24.64, 24.56, 24.68, 24.64, 24.4, 24.4, 24.36, 24.36, 24.28, 24.56, 24.44, 24.32, 24.32, 24.56, 24.56, 24.4, 24.28, 24.24, 24.12, 24.12, 24.28, 24.52, 24.48, 24.6, 24.64, 24.64, 24.48, 24.52, 24.4, 24.24, 24.08, 24.12, 24.2, 24.44, 24.68, 24.72, 24.96, 24.96, 24.8, 24.64, 24.56, 24.52, 24.6, 24.68, 24.68, 24.68, 24.8, 24.6, 24.52, 24.52, 24.52, 24.52, 24.32, 24.52, 24.44, 24.48, 24.52, 24.64, 24.52, 24.64, 24.8, 24.88, 24.88, 24.76, 24.6, 24.64, 24.52, 24.64, 24.52, 24.64, 24.48, 24.56, 24.44, 24.56, 24.56, 24.48, 24.52, 24.52, 24.6, 24.96, 24.92, 25.12, 25.16, 25.0, 24.92, 24.88, 24.92, 24.92, 24.96, 25.12, 25.08, 24.88, 24.56, 24.52, 24.48, 24.52, 24.68, 24.68, 24.76, 24.76, 24.68, 24.6, 24.56, 24.68, 24.6, 24.44, 24.24, 24.24, 24.36, 24.52, 24.6, 24.6, 24.64, 24.56, 24.36, 24.32, 24.44, 24.6, 24.48, 24.6, 24.56, 24.36, 24.44, 24.44, 24.44, 24.52, 24.84, 24.8, 25.12, 24.88, 24.6, 24.6, 24.64, 24.36, 24.4, 24.24, 24.24, 24.2, 24.2, 24.28, 24.52, 24.48, 24.48, 24.44, 24.4, 24.52, 24.48, 24.56, 24.56, 24.4, 24.56, 24.36, 24.56, 24.48, 24.4, 24.4, 24.36, 24.32, 24.36, 24.48, 24.52, 24.52, 24.72, 24.96, 24.88, 24.88, 24.76, 24.72, 24.64, 24.64, 24.64, 24.72, 24.56, 24.56, 24.56, 24.52, 24.4, 24.4, 24.56, 24.4, 24.64, 24.72, 24.72, 24.68, 24.72, 24.6, 24.6, 24.48, 24.68, 24.72, 24.72, 24.68, 24.68, 24.56, 24.32, 24.28, 24.44, 24.32, 24.32, 24.68, 24.68, 24.64, 24.72, 24.56, 24.52, 24.64, 24.64, 24.56, 24.6, 24.64, 24.64, 24.6, 24.28, 24.36, 24.56, 24.44, 24.76, 24.88, 24.84, 24.76, 24.72, 24.88, 24.72, 24.72, 24.64, 24.52, 24.76, 24.76, 25.0, 24.88, 24.92, 24.6, 24.44, 24.56, 24.72, 24.72, 24.84, 24.96, 25.16, 24.96, 24.96, 24.96, 24.92, 24.88, 24.92, 24.96, 25.04, 24.76, 24.76, 24.64, 24.76, 24.6, 24.68, 24.76, 24.64, 24.68, 24.52, 24.56, 24.68, 24.64, 24.64, 24.56, 24.52, 24.4, 24.28, 24.36, 24.68, 24.76, 24.76, 24.72, 24.6, 24.52, 24.52, 24.4, 24.24, 24.4, 24.44, 24.48, 24.6, 24.44, 24.4, 24.08, 24.16, 24.28, 24.28, 24.28]
|
||||
433.8547968864441
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 424.4422960281372, 'TIME_S_1KI': 424.4422960281372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 10577.914468402869, 'W': 24.381232025818772}
|
||||
[20.28, 20.2, 20.08, 20.08, 20.28, 20.44, 21.04, 20.84, 20.88, 20.76, 20.28, 20.6, 20.88, 21.24, 21.28, 21.28, 20.88, 20.36, 20.24, 20.32]
|
||||
371.41999999999996
|
||||
18.570999999999998
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 424.4422960281372, 'TIME_S_1KI': 424.4422960281372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 10577.914468402869, 'W': 24.381232025818772, 'J_1KI': 10577.914468402869, 'W_1KI': 24.381232025818772, 'W_D': 5.810232025818774, 'J_D': 2520.797035424717, 'W_D_1KI': 5.810232025818774, 'J_D_1KI': 5.810232025818774}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 52721, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.043588876724243, "TIME_S_1KI": 0.20947229522816796, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 310.41147632598876, "W": 21.8334966893401, "J_1KI": 5.8878146530981725, "W_1KI": 0.4141328254270613, "W_D": 3.2964966893401026, "J_D": 46.86699609327319, "W_D_1KI": 0.06252720337892116, "J_D_1KI": 0.0011860018470613448}
|
||||
@ -0,0 +1,81 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 10000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.24570083618164062}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1, ..., 5000, 5000, 5000]),
|
||||
col_indices=tensor([7274, 1823, 9481, ..., 3720, 7669, 6157]),
|
||||
values=tensor([0.0699, 0.4403, 0.9366, ..., 0.7220, 0.3462, 0.9666]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.3652, 0.9468, 0.8818, ..., 0.3143, 0.5478, 0.8274])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 0.24570083618164062 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 42734 -ss 10000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.510959386825562}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 4999, 4999, 5000]),
|
||||
col_indices=tensor([3889, 8009, 975, ..., 383, 3476, 3024]),
|
||||
values=tensor([0.2888, 0.9236, 0.0703, ..., 0.2234, 0.4670, 0.5913]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.8206, 0.5304, 0.1258, ..., 0.8056, 0.8493, 0.1547])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 8.510959386825562 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52721 -ss 10000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.043588876724243}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 4999, 5000, 5000]),
|
||||
col_indices=tensor([3785, 9007, 8216, ..., 3831, 7720, 5051]),
|
||||
values=tensor([0.5982, 0.6190, 0.5749, ..., 0.7670, 0.1287, 0.1052]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.6986, 0.7258, 0.0612, ..., 0.6419, 0.7078, 0.3008])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 11.043588876724243 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, ..., 4999, 5000, 5000]),
|
||||
col_indices=tensor([3785, 9007, 8216, ..., 3831, 7720, 5051]),
|
||||
values=tensor([0.5982, 0.6190, 0.5749, ..., 0.7670, 0.1287, 0.1052]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.6986, 0.7258, 0.0612, ..., 0.6419, 0.7078, 0.3008])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 11.043588876724243 seconds
|
||||
|
||||
[20.64, 20.64, 20.64, 20.64, 20.48, 20.52, 20.4, 20.52, 20.6, 20.56]
|
||||
[20.56, 20.6, 20.72, 22.44, 23.64, 25.6, 26.32, 25.84, 25.0, 23.2, 23.28, 23.24, 23.44, 23.44]
|
||||
14.217213153839111
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52721, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.043588876724243, 'TIME_S_1KI': 0.20947229522816796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.41147632598876, 'W': 21.8334966893401}
|
||||
[20.64, 20.64, 20.64, 20.64, 20.48, 20.52, 20.4, 20.52, 20.6, 20.56, 20.36, 20.48, 20.48, 20.44, 20.56, 20.68, 20.88, 20.96, 20.72, 20.64]
|
||||
370.74
|
||||
18.537
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52721, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.043588876724243, 'TIME_S_1KI': 0.20947229522816796, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.41147632598876, 'W': 21.8334966893401, 'J_1KI': 5.8878146530981725, 'W_1KI': 0.4141328254270613, 'W_D': 3.2964966893401026, 'J_D': 46.86699609327319, 'W_D_1KI': 0.06252720337892116, 'J_D_1KI': 0.0011860018470613448}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 37.50764799118042, "TIME_S_1KI": 37.50764799118042, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2045.1465811538696, "W": 43.475713756728496, "J_1KI": 2045.1465811538699, "W_1KI": 43.475713756728496, "W_D": 24.798713756728496, "J_D": 1166.5594483480452, "W_D_1KI": 24.798713756728496, "J_D_1KI": 24.798713756728496}
|
||||
@ -0,0 +1,47 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 500000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 37.50764799118042}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 37, 66, ..., 12499959,
|
||||
12499977, 12500000]),
|
||||
col_indices=tensor([ 2833, 12397, 17330, ..., 457394, 482426,
|
||||
493028]),
|
||||
values=tensor([0.7637, 0.4211, 0.1191, ..., 0.6468, 0.8330, 0.4871]),
|
||||
size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
|
||||
tensor([0.3810, 0.7581, 0.5448, ..., 0.8790, 0.4682, 0.1184])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([500000, 500000])
|
||||
Rows: 500000
|
||||
Size: 250000000000
|
||||
NNZ: 12500000
|
||||
Density: 5e-05
|
||||
Time: 37.50764799118042 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, 37, 66, ..., 12499959,
|
||||
12499977, 12500000]),
|
||||
col_indices=tensor([ 2833, 12397, 17330, ..., 457394, 482426,
|
||||
493028]),
|
||||
values=tensor([0.7637, 0.4211, 0.1191, ..., 0.6468, 0.8330, 0.4871]),
|
||||
size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
|
||||
tensor([0.3810, 0.7581, 0.5448, ..., 0.8790, 0.4682, 0.1184])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([500000, 500000])
|
||||
Rows: 500000
|
||||
Size: 250000000000
|
||||
NNZ: 12500000
|
||||
Density: 5e-05
|
||||
Time: 37.50764799118042 seconds
|
||||
|
||||
[21.08, 20.96, 20.88, 20.72, 20.6, 20.6, 20.64, 20.2, 20.28, 20.6]
|
||||
[20.72, 20.68, 21.2, 23.0, 24.88, 26.4, 29.84, 29.72, 30.96, 36.72, 36.72, 41.28, 45.64, 50.76, 52.76, 52.48, 53.28, 52.92, 52.8, 52.76, 52.48, 52.52, 52.68, 52.72, 53.4, 53.44, 53.4, 53.36, 53.12, 53.4, 53.2, 53.0, 53.0, 53.24, 53.08, 52.92, 53.0, 52.72, 52.8, 52.64, 52.8, 52.96, 52.8, 52.8, 52.68]
|
||||
47.04112720489502
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 37.50764799118042, 'TIME_S_1KI': 37.50764799118042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2045.1465811538696, 'W': 43.475713756728496}
|
||||
[21.08, 20.96, 20.88, 20.72, 20.6, 20.6, 20.64, 20.2, 20.28, 20.6, 20.92, 21.12, 21.0, 20.84, 20.92, 20.72, 20.72, 20.56, 21.0, 20.96]
|
||||
373.54
|
||||
18.677
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 37.50764799118042, 'TIME_S_1KI': 37.50764799118042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2045.1465811538696, 'W': 43.475713756728496, 'J_1KI': 2045.1465811538699, 'W_1KI': 43.475713756728496, 'W_D': 24.798713756728496, 'J_D': 1166.5594483480452, 'W_D_1KI': 24.798713756728496, 'J_D_1KI': 24.798713756728496}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 6265, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.399809837341309, "TIME_S_1KI": 1.6599856085141753, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 409.2376235961914, "W": 30.158092261325162, "J_1KI": 65.32124877832264, "W_1KI": 4.813741781536339, "W_D": 11.570092261325161, "J_D": 157.0032023506164, "W_D_1KI": 1.846782483850784, "J_D_1KI": 0.2947777308620565}
|
||||
@ -0,0 +1,65 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 50000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6757559776306152}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 4, ..., 124996, 124997,
|
||||
125000]),
|
||||
col_indices=tensor([11324, 36531, 41582, ..., 26561, 37075, 42675]),
|
||||
values=tensor([0.0907, 0.5500, 0.9495, ..., 0.7742, 0.3202, 0.5187]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.4295, 0.8994, 0.1269, ..., 0.0289, 0.7051, 0.4729])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 1.6757559776306152 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6265 -ss 50000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.399809837341309}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 6, ..., 124998, 125000,
|
||||
125000]),
|
||||
col_indices=tensor([ 5059, 34750, 35724, ..., 34703, 6591, 31118]),
|
||||
values=tensor([0.4217, 0.1867, 0.1593, ..., 0.5339, 0.2274, 0.5888]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.7798, 0.1756, 0.8709, ..., 0.5047, 0.8577, 0.3016])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 10.399809837341309 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, 6, ..., 124998, 125000,
|
||||
125000]),
|
||||
col_indices=tensor([ 5059, 34750, 35724, ..., 34703, 6591, 31118]),
|
||||
values=tensor([0.4217, 0.1867, 0.1593, ..., 0.5339, 0.2274, 0.5888]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.7798, 0.1756, 0.8709, ..., 0.5047, 0.8577, 0.3016])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 10.399809837341309 seconds
|
||||
|
||||
[20.92, 20.84, 20.96, 20.84, 20.68, 20.64, 20.84, 20.76, 20.84, 21.08]
|
||||
[21.08, 21.16, 21.84, 21.84, 23.04, 26.04, 31.36, 35.8, 40.08, 43.12, 43.68, 43.84, 43.84]
|
||||
13.569745063781738
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6265, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.399809837341309, 'TIME_S_1KI': 1.6599856085141753, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 409.2376235961914, 'W': 30.158092261325162}
|
||||
[20.92, 20.84, 20.96, 20.84, 20.68, 20.64, 20.84, 20.76, 20.84, 21.08, 20.04, 19.96, 20.16, 20.28, 20.56, 20.88, 20.76, 20.68, 20.68, 20.76]
|
||||
371.76000000000005
|
||||
18.588
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6265, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.399809837341309, 'TIME_S_1KI': 1.6599856085141753, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 409.2376235961914, 'W': 30.158092261325162, 'J_1KI': 65.32124877832264, 'W_1KI': 4.813741781536339, 'W_D': 11.570092261325161, 'J_D': 157.0032023506164, 'W_D_1KI': 1.846782483850784, 'J_D_1KI': 0.2947777308620565}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1000, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 105.13774371147156, "TIME_S_1KI": 105.13774371147156, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2659.356458015442, "W": 24.098855953533207, "J_1KI": 2659.356458015442, "W_1KI": 24.098855953533207, "W_D": 5.604855953533207, "J_D": 618.5069492516519, "W_D_1KI": 5.604855953533207, "J_D_1KI": 5.604855953533207}
|
||||
@ -0,0 +1,45 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 0.2 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 105.13774371147156}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, 1002, 2008, ..., 4998043,
|
||||
4999009, 5000000]),
|
||||
col_indices=tensor([ 2, 3, 6, ..., 4986, 4991, 4996]),
|
||||
values=tensor([0.2244, 0.9982, 0.0019, ..., 0.8643, 0.5557, 0.2909]),
|
||||
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
|
||||
tensor([0.1159, 0.5733, 0.3535, ..., 0.0131, 0.4436, 0.7693])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 5000000
|
||||
Density: 0.2
|
||||
Time: 105.13774371147156 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, 1002, 2008, ..., 4998043,
|
||||
4999009, 5000000]),
|
||||
col_indices=tensor([ 2, 3, 6, ..., 4986, 4991, 4996]),
|
||||
values=tensor([0.2244, 0.9982, 0.0019, ..., 0.8643, 0.5557, 0.2909]),
|
||||
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
|
||||
tensor([0.1159, 0.5733, 0.3535, ..., 0.0131, 0.4436, 0.7693])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 5000000
|
||||
Density: 0.2
|
||||
Time: 105.13774371147156 seconds
|
||||
|
||||
[20.56, 20.6, 20.6, 20.56, 20.72, 20.56, 20.56, 20.4, 20.28, 20.32]
|
||||
[20.52, 20.36, 21.08, 21.64, 23.52, 27.0, 27.0, 27.8, 28.08, 27.68, 25.2, 24.2, 24.68, 24.52, 24.52, 24.6, 24.52, 24.04, 24.04, 24.2, 24.04, 24.16, 24.4, 24.28, 24.28, 24.4, 24.68, 24.64, 24.72, 24.8, 24.8, 24.64, 24.52, 24.52, 24.36, 24.28, 24.2, 24.16, 24.32, 24.64, 24.76, 25.0, 25.0, 25.0, 24.96, 24.68, 24.64, 24.64, 24.6, 24.64, 24.6, 24.6, 24.52, 24.6, 24.68, 24.68, 24.68, 24.6, 24.56, 24.44, 24.36, 24.44, 24.48, 24.4, 24.64, 24.6, 24.32, 24.24, 24.24, 24.32, 24.32, 24.2, 24.24, 24.32, 24.28, 24.32, 24.44, 24.48, 24.44, 24.6, 24.6, 24.44, 24.32, 24.64, 24.52, 24.76, 24.48, 24.6, 24.44, 24.28, 24.28, 24.32, 24.32, 24.48, 24.56, 24.68, 24.72, 24.76, 24.88, 24.8, 24.72, 25.04, 25.0, 24.88, 24.72, 24.72, 24.64, 24.56, 24.48]
|
||||
110.35197949409485
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 105.13774371147156, 'TIME_S_1KI': 105.13774371147156, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2659.356458015442, 'W': 24.098855953533207}
|
||||
[20.56, 20.6, 20.6, 20.56, 20.72, 20.56, 20.56, 20.4, 20.28, 20.32, 20.32, 20.48, 20.56, 20.56, 20.76, 20.76, 20.68, 20.48, 20.52, 20.4]
|
||||
369.88
|
||||
18.494
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1000, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 105.13774371147156, 'TIME_S_1KI': 105.13774371147156, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2659.356458015442, 'W': 24.098855953533207, 'J_1KI': 2659.356458015442, 'W_1KI': 24.098855953533207, 'W_D': 5.604855953533207, 'J_D': 618.5069492516519, 'W_D_1KI': 5.604855953533207, 'J_D_1KI': 5.604855953533207}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Altra", "CORES": 16, "ITERATIONS": 151147, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.619585275650024, "TIME_S_1KI": 0.07025998051995755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.34527337074286, "W": 23.06683265939357, "J_1KI": 2.1723571977660345, "W_1KI": 0.15261191197571614, "W_D": 4.6928326593935665, "J_D": 66.80021679544454, "W_D_1KI": 0.031048136313612352, "J_D_1KI": 0.00020541682146263144}
|
||||
@ -0,0 +1,81 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1000 -ss 5000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.07715368270874023}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 1250, 1250, 1250]),
|
||||
col_indices=tensor([4186, 604, 2911, ..., 3524, 2664, 807]),
|
||||
values=tensor([0.1303, 0.5472, 0.9541, ..., 0.4453, 0.4813, 0.2933]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.2363, 0.5745, 0.8536, ..., 0.3028, 0.7626, 0.7945])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 0.07715368270874023 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 136092 -ss 5000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.454103946685791}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 1250, 1250, 1250]),
|
||||
col_indices=tensor([2642, 3295, 3317, ..., 552, 1688, 3754]),
|
||||
values=tensor([0.5853, 0.8410, 0.7758, ..., 0.7543, 0.4171, 0.3907]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.4145, 0.1634, 0.4401, ..., 0.9903, 0.7928, 0.8495])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 9.454103946685791 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 151147 -ss 5000 -sd 5e-05 -c 16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.619585275650024}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If 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, ..., 1250, 1250, 1250]),
|
||||
col_indices=tensor([2120, 4070, 3230, ..., 2901, 3405, 168]),
|
||||
values=tensor([0.1519, 0.7232, 0.6282, ..., 0.2528, 0.5199, 0.9452]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.2553, 0.4766, 0.2217, ..., 0.4056, 0.3500, 0.9553])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 10.619585275650024 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, ..., 1250, 1250, 1250]),
|
||||
col_indices=tensor([2120, 4070, 3230, ..., 2901, 3405, 168]),
|
||||
values=tensor([0.1519, 0.7232, 0.6282, ..., 0.2528, 0.5199, 0.9452]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.2553, 0.4766, 0.2217, ..., 0.4056, 0.3500, 0.9553])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 10.619585275650024 seconds
|
||||
|
||||
[20.44, 20.36, 20.28, 20.16, 20.04, 20.16, 20.28, 20.32, 20.56, 20.56]
|
||||
[20.72, 20.76, 20.8, 24.32, 26.28, 28.48, 29.36, 29.52, 26.16, 23.92, 23.8, 23.64, 23.64, 23.56]
|
||||
14.234519243240356
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 151147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.619585275650024, 'TIME_S_1KI': 0.07025998051995755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.34527337074286, 'W': 23.06683265939357}
|
||||
[20.44, 20.36, 20.28, 20.16, 20.04, 20.16, 20.28, 20.32, 20.56, 20.56, 20.24, 20.2, 20.36, 20.68, 20.96, 20.8, 20.68, 20.4, 20.44, 20.36]
|
||||
367.48
|
||||
18.374000000000002
|
||||
{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 151147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.619585275650024, 'TIME_S_1KI': 0.07025998051995755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.34527337074286, 'W': 23.06683265939357, 'J_1KI': 2.1723571977660345, 'W_1KI': 0.15261191197571614, 'W_D': 4.6928326593935665, 'J_D': 66.80021679544454, 'W_D_1KI': 0.031048136313612352, 'J_D_1KI': 0.00020541682146263144}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 85448, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.79839825630188, "TIME_S_1KI": 0.12637391461826936, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1847.2158340501787, "W": 132.18, "J_1KI": 21.618011352520583, "W_1KI": 1.5469057204381613, "W_D": 96.35400000000001, "J_D": 1346.5473935093883, "W_D_1KI": 1.127633180413819, "J_D_1KI": 0.013196718242835633}
|
||||
@ -0,0 +1,85 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.157515287399292}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 8, ..., 499988, 499996,
|
||||
500000]),
|
||||
col_indices=tensor([50162, 75153, 30191, ..., 32389, 47580, 60210]),
|
||||
values=tensor([0.9007, 0.9447, 0.0410, ..., 0.6472, 0.2952, 0.4267]),
|
||||
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
|
||||
tensor([0.3259, 0.8902, 0.7186, ..., 0.8330, 0.5312, 0.8917])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([100000, 100000])
|
||||
Rows: 100000
|
||||
Size: 10000000000
|
||||
NNZ: 500000
|
||||
Density: 5e-05
|
||||
Time: 0.157515287399292 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '66660', '-ss', '100000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.191283702850342}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 8, ..., 499990, 499997,
|
||||
500000]),
|
||||
col_indices=tensor([ 3937, 41482, 51345, ..., 57028, 62776, 96568]),
|
||||
values=tensor([0.3669, 0.7790, 0.6636, ..., 0.0088, 0.3191, 0.1015]),
|
||||
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
|
||||
tensor([0.1888, 0.6317, 0.9833, ..., 0.5078, 0.6417, 0.5906])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([100000, 100000])
|
||||
Rows: 100000
|
||||
Size: 10000000000
|
||||
NNZ: 500000
|
||||
Density: 5e-05
|
||||
Time: 8.191283702850342 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '85448', '-ss', '100000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.79839825630188}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 8, 13, ..., 499988, 499995,
|
||||
500000]),
|
||||
col_indices=tensor([ 3698, 26087, 35796, ..., 95832, 96289, 98226]),
|
||||
values=tensor([0.4478, 0.6896, 0.5878, ..., 0.3885, 0.0788, 0.0500]),
|
||||
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
|
||||
tensor([0.6913, 0.6407, 0.8664, ..., 0.8625, 0.1823, 0.9429])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([100000, 100000])
|
||||
Rows: 100000
|
||||
Size: 10000000000
|
||||
NNZ: 500000
|
||||
Density: 5e-05
|
||||
Time: 10.79839825630188 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, 8, 13, ..., 499988, 499995,
|
||||
500000]),
|
||||
col_indices=tensor([ 3698, 26087, 35796, ..., 95832, 96289, 98226]),
|
||||
values=tensor([0.4478, 0.6896, 0.5878, ..., 0.3885, 0.0788, 0.0500]),
|
||||
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
|
||||
tensor([0.6913, 0.6407, 0.8664, ..., 0.8625, 0.1823, 0.9429])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([100000, 100000])
|
||||
Rows: 100000
|
||||
Size: 10000000000
|
||||
NNZ: 500000
|
||||
Density: 5e-05
|
||||
Time: 10.79839825630188 seconds
|
||||
|
||||
[40.27, 39.58, 39.76, 40.26, 40.31, 39.92, 40.36, 39.5, 39.65, 39.54]
|
||||
[132.18]
|
||||
13.975002527236938
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 85448, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.79839825630188, 'TIME_S_1KI': 0.12637391461826936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1847.2158340501787, 'W': 132.18}
|
||||
[40.27, 39.58, 39.76, 40.26, 40.31, 39.92, 40.36, 39.5, 39.65, 39.54, 40.41, 39.45, 40.31, 39.36, 39.58, 39.39, 39.62, 39.75, 39.86, 39.5]
|
||||
716.52
|
||||
35.826
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 85448, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.79839825630188, 'TIME_S_1KI': 0.12637391461826936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1847.2158340501787, 'W': 132.18, 'J_1KI': 21.618011352520583, 'W_1KI': 1.5469057204381613, 'W_D': 96.35400000000001, 'J_D': 1346.5473935093883, 'W_D_1KI': 1.127633180413819, 'J_D_1KI': 0.013196718242835633}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2353, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 11.32719349861145, "TIME_S_1KI": 4.813936888487654, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2176.6533761024475, "W": 119.81, "J_1KI": 925.0545584795782, "W_1KI": 50.91797705057373, "W_D": 83.82675, "J_D": 1522.9261196494103, "W_D_1KI": 35.62547811304718, "J_D_1KI": 15.140449686802882}
|
||||
@ -0,0 +1,65 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.2', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 4.462156772613525}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 1973, 4017, ..., 19996005,
|
||||
19998026, 20000000]),
|
||||
col_indices=tensor([ 9, 13, 23, ..., 9991, 9994, 9996]),
|
||||
values=tensor([0.6139, 0.9857, 0.8934, ..., 0.0556, 0.4216, 0.1096]),
|
||||
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
|
||||
tensor([0.2039, 0.8822, 0.1274, ..., 0.2057, 0.5761, 0.2633])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 20000000
|
||||
Density: 0.2
|
||||
Time: 4.462156772613525 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2353', '-ss', '10000', '-sd', '0.2', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 11.32719349861145}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 2048, 4126, ..., 19995999,
|
||||
19998042, 20000000]),
|
||||
col_indices=tensor([ 2, 9, 12, ..., 9990, 9992, 9998]),
|
||||
values=tensor([0.9897, 0.5727, 0.9448, ..., 0.9783, 0.3588, 0.5443]),
|
||||
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
|
||||
tensor([0.6351, 0.0385, 0.6216, ..., 0.2960, 0.7778, 0.2124])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 20000000
|
||||
Density: 0.2
|
||||
Time: 11.32719349861145 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, 2048, 4126, ..., 19995999,
|
||||
19998042, 20000000]),
|
||||
col_indices=tensor([ 2, 9, 12, ..., 9990, 9992, 9998]),
|
||||
values=tensor([0.9897, 0.5727, 0.9448, ..., 0.9783, 0.3588, 0.5443]),
|
||||
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
|
||||
tensor([0.6351, 0.0385, 0.6216, ..., 0.2960, 0.7778, 0.2124])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 20000000
|
||||
Density: 0.2
|
||||
Time: 11.32719349861145 seconds
|
||||
|
||||
[40.52, 39.82, 39.98, 39.31, 45.78, 39.49, 39.61, 39.59, 39.44, 39.87]
|
||||
[119.81]
|
||||
18.167543411254883
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2353, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 11.32719349861145, 'TIME_S_1KI': 4.813936888487654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2176.6533761024475, 'W': 119.81}
|
||||
[40.52, 39.82, 39.98, 39.31, 45.78, 39.49, 39.61, 39.59, 39.44, 39.87, 41.36, 39.61, 39.47, 39.55, 39.57, 39.39, 39.51, 39.42, 39.39, 39.72]
|
||||
719.665
|
||||
35.98325
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2353, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 11.32719349861145, 'TIME_S_1KI': 4.813936888487654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2176.6533761024475, 'W': 119.81, 'J_1KI': 925.0545584795782, 'W_1KI': 50.91797705057373, 'W_D': 83.82675, 'J_D': 1522.9261196494103, 'W_D_1KI': 35.62547811304718, 'J_D_1KI': 15.140449686802882}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 307566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.194913387298584, "TIME_S_1KI": 0.033147075383165185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1254.457565395832, "W": 97.91, "J_1KI": 4.078661378032136, "W_1KI": 0.3183381778219959, "W_D": 62.19175, "J_D": 796.8227075141073, "W_D_1KI": 0.2022061931422849, "J_D_1KI": 0.0006574400068352317}
|
||||
@ -0,0 +1,81 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04550504684448242}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 4998, 5000, 5000]),
|
||||
col_indices=tensor([9281, 526, 5110, ..., 4172, 680, 4833]),
|
||||
values=tensor([0.9710, 0.4177, 0.1273, ..., 0.7621, 0.2431, 0.8030]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.6244, 0.3231, 0.3638, ..., 0.2586, 0.1943, 0.4038])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 0.04550504684448242 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '230743', '-ss', '10000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.877320289611816}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 4996, 4999, 5000]),
|
||||
col_indices=tensor([5149, 830, 3827, ..., 6947, 7825, 8143]),
|
||||
values=tensor([0.7974, 0.8672, 0.6352, ..., 0.0945, 0.9729, 0.8206]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.8724, 0.1762, 0.3345, ..., 0.8958, 0.7321, 0.5036])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 7.877320289611816 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '307566', '-ss', '10000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.194913387298584}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 1, ..., 5000, 5000, 5000]),
|
||||
col_indices=tensor([6013, 2562, 3841, ..., 8262, 3270, 1424]),
|
||||
values=tensor([0.2685, 0.3885, 0.6243, ..., 0.1198, 0.9466, 0.9233]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.2690, 0.9597, 0.8597, ..., 0.6996, 0.9887, 0.3737])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 10.194913387298584 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, 1, ..., 5000, 5000, 5000]),
|
||||
col_indices=tensor([6013, 2562, 3841, ..., 8262, 3270, 1424]),
|
||||
values=tensor([0.2685, 0.3885, 0.6243, ..., 0.1198, 0.9466, 0.9233]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.2690, 0.9597, 0.8597, ..., 0.6996, 0.9887, 0.3737])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 10.194913387298584 seconds
|
||||
|
||||
[40.9, 39.57, 39.65, 39.48, 39.52, 39.37, 39.42, 39.88, 40.21, 39.84]
|
||||
[97.91]
|
||||
12.81235384941101
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 307566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.194913387298584, 'TIME_S_1KI': 0.033147075383165185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1254.457565395832, 'W': 97.91}
|
||||
[40.9, 39.57, 39.65, 39.48, 39.52, 39.37, 39.42, 39.88, 40.21, 39.84, 41.61, 39.93, 39.26, 40.39, 39.17, 39.17, 39.39, 39.38, 39.35, 40.1]
|
||||
714.365
|
||||
35.71825
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 307566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.194913387298584, 'TIME_S_1KI': 0.033147075383165185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1254.457565395832, 'W': 97.91, 'J_1KI': 4.078661378032136, 'W_1KI': 0.3183381778219959, 'W_D': 62.19175, 'J_D': 796.8227075141073, 'W_D_1KI': 0.2022061931422849, 'J_D_1KI': 0.0006574400068352317}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2443, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.091211557388306, "TIME_S_1KI": 4.5399965441622205, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2093.1614422798157, "W": 125.74, "J_1KI": 856.7996079737272, "W_1KI": 51.469504707327054, "W_D": 89.3905, "J_D": 1488.0606641173363, "W_D_1KI": 36.59046254604994, "J_D_1KI": 14.977676031948402}
|
||||
@ -0,0 +1,89 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.620434761047363}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 55, ..., 12499942,
|
||||
12499972, 12500000]),
|
||||
col_indices=tensor([ 19855, 24177, 33309, ..., 430292, 468270,
|
||||
470726]),
|
||||
values=tensor([0.1735, 0.2720, 0.9086, ..., 0.2697, 0.0473, 0.0416]),
|
||||
size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
|
||||
tensor([0.2844, 0.4487, 0.9137, ..., 0.5004, 0.3000, 0.1233])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([500000, 500000])
|
||||
Rows: 500000
|
||||
Size: 250000000000
|
||||
NNZ: 12500000
|
||||
Density: 5e-05
|
||||
Time: 4.620434761047363 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2272', '-ss', '500000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.761992931365967}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 25, 50, ..., 12499939,
|
||||
12499968, 12500000]),
|
||||
col_indices=tensor([ 35309, 102593, 109410, ..., 438712, 452154,
|
||||
489935]),
|
||||
values=tensor([0.4991, 0.7582, 0.4985, ..., 0.8355, 0.6986, 0.3665]),
|
||||
size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
|
||||
tensor([0.1755, 0.5499, 0.0031, ..., 0.2944, 0.6143, 0.3232])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([500000, 500000])
|
||||
Rows: 500000
|
||||
Size: 250000000000
|
||||
NNZ: 12500000
|
||||
Density: 5e-05
|
||||
Time: 9.761992931365967 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2443', '-ss', '500000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.091211557388306}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 29, 54, ..., 12499940,
|
||||
12499966, 12500000]),
|
||||
col_indices=tensor([ 15104, 53699, 66016, ..., 451008, 478949,
|
||||
498027]),
|
||||
values=tensor([0.3834, 0.0166, 0.8269, ..., 0.7083, 0.6634, 0.5753]),
|
||||
size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
|
||||
tensor([0.3028, 0.2567, 0.4384, ..., 0.4923, 0.2329, 0.9462])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([500000, 500000])
|
||||
Rows: 500000
|
||||
Size: 250000000000
|
||||
NNZ: 12500000
|
||||
Density: 5e-05
|
||||
Time: 11.091211557388306 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, 29, 54, ..., 12499940,
|
||||
12499966, 12500000]),
|
||||
col_indices=tensor([ 15104, 53699, 66016, ..., 451008, 478949,
|
||||
498027]),
|
||||
values=tensor([0.3834, 0.0166, 0.8269, ..., 0.7083, 0.6634, 0.5753]),
|
||||
size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
|
||||
tensor([0.3028, 0.2567, 0.4384, ..., 0.4923, 0.2329, 0.9462])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([500000, 500000])
|
||||
Rows: 500000
|
||||
Size: 250000000000
|
||||
NNZ: 12500000
|
||||
Density: 5e-05
|
||||
Time: 11.091211557388306 seconds
|
||||
|
||||
[41.47, 41.03, 39.87, 40.83, 39.9, 40.09, 40.23, 40.2, 39.74, 39.65]
|
||||
[125.74]
|
||||
16.646742820739746
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.091211557388306, 'TIME_S_1KI': 4.5399965441622205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2093.1614422798157, 'W': 125.74}
|
||||
[41.47, 41.03, 39.87, 40.83, 39.9, 40.09, 40.23, 40.2, 39.74, 39.65, 40.36, 39.88, 39.75, 39.86, 39.72, 40.13, 40.1, 39.56, 45.29, 40.14]
|
||||
726.99
|
||||
36.3495
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.091211557388306, 'TIME_S_1KI': 4.5399965441622205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2093.1614422798157, 'W': 125.74, 'J_1KI': 856.7996079737272, 'W_1KI': 51.469504707327054, 'W_D': 89.3905, 'J_D': 1488.0606641173363, 'W_D_1KI': 36.59046254604994, 'J_D_1KI': 14.977676031948402}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 110048, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.860910654067993, "TIME_S_1KI": 0.09869248558872486, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1513.3099088716508, "W": 110.42, "J_1KI": 13.751362213503661, "W_1KI": 1.0033803431230008, "W_D": 74.49324999999999, "J_D": 1020.9325608499645, "W_D_1KI": 0.6769159821168944, "J_D_1KI": 0.006151097540317811}
|
||||
@ -0,0 +1,85 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.1521902084350586}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 4, ..., 124998, 124999,
|
||||
125000]),
|
||||
col_indices=tensor([28568, 23377, 33207, ..., 35070, 20237, 35086]),
|
||||
values=tensor([0.0970, 0.0746, 0.1789, ..., 0.4665, 0.3762, 0.5874]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.5202, 0.5834, 0.5039, ..., 0.2581, 0.4110, 0.2043])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 0.1521902084350586 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '68992', '-ss', '50000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.582725286483765}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 4, ..., 124994, 124996,
|
||||
125000]),
|
||||
col_indices=tensor([13407, 30849, 37582, ..., 4235, 7510, 16049]),
|
||||
values=tensor([0.4132, 0.1824, 0.9780, ..., 0.4864, 0.4697, 0.1823]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.9509, 0.2372, 0.8108, ..., 0.6237, 0.0261, 0.7128])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 6.582725286483765 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '110048', '-ss', '50000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.860910654067993}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 6, ..., 124995, 124996,
|
||||
125000]),
|
||||
col_indices=tensor([ 8770, 24657, 47529, ..., 37234, 42798, 47480]),
|
||||
values=tensor([0.5915, 0.6574, 0.6205, ..., 0.3170, 0.3438, 0.3659]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.7258, 0.6385, 0.7203, ..., 0.8359, 0.3176, 0.3735])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 10.860910654067993 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, 6, ..., 124995, 124996,
|
||||
125000]),
|
||||
col_indices=tensor([ 8770, 24657, 47529, ..., 37234, 42798, 47480]),
|
||||
values=tensor([0.5915, 0.6574, 0.6205, ..., 0.3170, 0.3438, 0.3659]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.7258, 0.6385, 0.7203, ..., 0.8359, 0.3176, 0.3735])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 10.860910654067993 seconds
|
||||
|
||||
[40.58, 39.41, 39.64, 39.39, 39.67, 39.83, 39.65, 39.43, 39.45, 39.8]
|
||||
[110.42]
|
||||
13.705034494400024
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110048, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.860910654067993, 'TIME_S_1KI': 0.09869248558872486, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1513.3099088716508, 'W': 110.42}
|
||||
[40.58, 39.41, 39.64, 39.39, 39.67, 39.83, 39.65, 39.43, 39.45, 39.8, 40.29, 39.44, 40.08, 39.34, 39.59, 39.49, 39.42, 44.77, 39.96, 39.28]
|
||||
718.5350000000001
|
||||
35.926750000000006
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110048, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.860910654067993, 'TIME_S_1KI': 0.09869248558872486, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1513.3099088716508, 'W': 110.42, 'J_1KI': 13.751362213503661, 'W_1KI': 1.0033803431230008, 'W_D': 74.49324999999999, 'J_D': 1020.9325608499645, 'W_D_1KI': 0.6769159821168944, 'J_D_1KI': 0.006151097540317811}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28937, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 14.827638626098633, "TIME_S_1KI": 0.5124110524967561, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3609.586873168945, "W": 118.08, "J_1KI": 124.73949867536183, "W_1KI": 4.080588865466358, "W_D": 72.93325, "J_D": 2229.496119728565, "W_D_1KI": 2.5204150395687184, "J_D_1KI": 0.08710008085042398}
|
||||
@ -0,0 +1,105 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.2', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.43933725357055664}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 1026, 2006, ..., 4997956,
|
||||
4998952, 5000000]),
|
||||
col_indices=tensor([ 2, 17, 24, ..., 4985, 4989, 4995]),
|
||||
values=tensor([0.8274, 0.9158, 0.7152, ..., 0.4764, 0.4337, 0.1760]),
|
||||
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
|
||||
tensor([0.9697, 0.2900, 0.2967, ..., 0.8048, 0.6311, 0.8937])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 5000000
|
||||
Density: 0.2
|
||||
Time: 0.43933725357055664 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '23899', '-ss', '5000', '-sd', '0.2', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.217516660690308}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 1007, 2063, ..., 4997980,
|
||||
4998987, 5000000]),
|
||||
col_indices=tensor([ 1, 11, 20, ..., 4979, 4988, 4999]),
|
||||
values=tensor([0.6304, 0.9983, 0.6257, ..., 0.2269, 0.0229, 0.1968]),
|
||||
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
|
||||
tensor([0.1233, 0.6806, 0.4968, ..., 0.5662, 0.5626, 0.1071])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 5000000
|
||||
Density: 0.2
|
||||
Time: 9.217516660690308 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27224', '-ss', '5000', '-sd', '0.2', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.878192901611328}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 990, 1993, ..., 4997978,
|
||||
4998966, 5000000]),
|
||||
col_indices=tensor([ 3, 4, 10, ..., 4987, 4994, 4995]),
|
||||
values=tensor([0.4002, 0.6649, 0.4467, ..., 0.0548, 0.1613, 0.2598]),
|
||||
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
|
||||
tensor([0.8115, 0.3462, 0.9342, ..., 0.0919, 0.3442, 0.4570])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 5000000
|
||||
Density: 0.2
|
||||
Time: 9.878192901611328 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28937', '-ss', '5000', '-sd', '0.2', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 14.827638626098633}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 969, 2001, ..., 4997956,
|
||||
4998975, 5000000]),
|
||||
col_indices=tensor([ 6, 11, 14, ..., 4991, 4992, 4999]),
|
||||
values=tensor([0.1794, 0.8821, 0.0709, ..., 0.4822, 0.4100, 0.3846]),
|
||||
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
|
||||
tensor([0.9695, 0.1954, 0.0199, ..., 0.8094, 0.5782, 0.4777])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 5000000
|
||||
Density: 0.2
|
||||
Time: 14.827638626098633 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, 969, 2001, ..., 4997956,
|
||||
4998975, 5000000]),
|
||||
col_indices=tensor([ 6, 11, 14, ..., 4991, 4992, 4999]),
|
||||
values=tensor([0.1794, 0.8821, 0.0709, ..., 0.4822, 0.4100, 0.3846]),
|
||||
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
|
||||
tensor([0.9695, 0.1954, 0.0199, ..., 0.8094, 0.5782, 0.4777])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 5000000
|
||||
Density: 0.2
|
||||
Time: 14.827638626098633 seconds
|
||||
|
||||
[40.27, 40.67, 45.31, 61.44, 66.02, 68.38, 70.5, 73.12, 64.38, 67.33]
|
||||
[118.08]
|
||||
30.568994522094727
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28937, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 14.827638626098633, 'TIME_S_1KI': 0.5124110524967561, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3609.586873168945, 'W': 118.08}
|
||||
[40.27, 40.67, 45.31, 61.44, 66.02, 68.38, 70.5, 73.12, 64.38, 67.33, 41.67, 40.18, 39.68, 39.83, 39.75, 39.7, 39.65, 40.28, 39.58, 39.66]
|
||||
902.935
|
||||
45.14675
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28937, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 14.827638626098633, 'TIME_S_1KI': 0.5124110524967561, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3609.586873168945, 'W': 118.08, 'J_1KI': 124.73949867536183, 'W_1KI': 4.080588865466358, 'W_D': 72.93325, 'J_D': 2229.496119728565, 'W_D_1KI': 2.5204150395687184, 'J_D_1KI': 0.08710008085042398}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 471062, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.751366138458252, "TIME_S_1KI": 0.022823675309106343, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1247.6856990122794, "W": 95.07, "J_1KI": 2.6486655663421788, "W_1KI": 0.20182056714402774, "W_D": 59.887249999999995, "J_D": 785.9520919130443, "W_D_1KI": 0.12713241569050357, "J_D_1KI": 0.0002698846769438069}
|
||||
@ -0,0 +1,81 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.06770849227905273}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 1249, 1249, 1250]),
|
||||
col_indices=tensor([ 631, 2604, 2210, ..., 3865, 405, 4638]),
|
||||
values=tensor([0.5383, 0.4516, 0.3480, ..., 0.7821, 0.4439, 0.3322]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.7026, 0.4971, 0.6548, ..., 0.0098, 0.8140, 0.3691])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 0.06770849227905273 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '155076', '-ss', '5000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.4566495418548584}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 1249, 1249, 1250]),
|
||||
col_indices=tensor([2690, 1150, 3956, ..., 712, 3582, 778]),
|
||||
values=tensor([0.8228, 0.9297, 0.1263, ..., 0.8170, 0.9056, 0.4551]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.8564, 0.8640, 0.2790, ..., 0.6413, 0.1958, 0.4583])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 3.4566495418548584 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '471062', '-ss', '5000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.751366138458252}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 1, ..., 1249, 1249, 1250]),
|
||||
col_indices=tensor([1001, 2591, 823, ..., 591, 3447, 2958]),
|
||||
values=tensor([0.9383, 0.1843, 0.8614, ..., 0.3843, 0.5733, 0.2218]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.8666, 0.6997, 0.8565, ..., 0.1580, 0.8946, 0.5984])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 10.751366138458252 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, 1, ..., 1249, 1249, 1250]),
|
||||
col_indices=tensor([1001, 2591, 823, ..., 591, 3447, 2958]),
|
||||
values=tensor([0.9383, 0.1843, 0.8614, ..., 0.3843, 0.5733, 0.2218]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.8666, 0.6997, 0.8565, ..., 0.1580, 0.8946, 0.5984])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 10.751366138458252 seconds
|
||||
|
||||
[39.99, 39.18, 38.82, 38.83, 39.11, 38.71, 39.17, 38.68, 38.75, 38.85]
|
||||
[95.07]
|
||||
13.123863458633423
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 471062, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.751366138458252, 'TIME_S_1KI': 0.022823675309106343, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1247.6856990122794, 'W': 95.07}
|
||||
[39.99, 39.18, 38.82, 38.83, 39.11, 38.71, 39.17, 38.68, 38.75, 38.85, 39.95, 39.47, 39.27, 39.32, 39.37, 38.76, 39.37, 39.0, 39.09, 38.72]
|
||||
703.655
|
||||
35.18275
|
||||
{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 471062, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.751366138458252, 'TIME_S_1KI': 0.022823675309106343, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1247.6856990122794, 'W': 95.07, 'J_1KI': 2.6486655663421788, 'W_1KI': 0.20182056714402774, 'W_D': 59.887249999999995, 'J_D': 785.9520919130443, 'W_D_1KI': 0.12713241569050357, 'J_D_1KI': 0.0002698846769438069}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 43635, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.050816535949707, "TIME_S_1KI": 0.23033841035750444, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1224.4814233660697, "W": 89.55, "J_1KI": 28.06190955347931, "W_1KI": 2.052251632863527, "W_D": 73.275, "J_D": 1001.9416671931745, "W_D_1KI": 1.679271227225851, "J_D_1KI": 0.03848450159793402}
|
||||
@ -0,0 +1,65 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '100000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.24062752723693848}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 8, ..., 499990, 499993,
|
||||
500000]),
|
||||
col_indices=tensor([25845, 82264, 90566, ..., 92820, 97145, 99590]),
|
||||
values=tensor([0.6382, 0.4794, 0.9065, ..., 0.0565, 0.2096, 0.7456]),
|
||||
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
|
||||
tensor([0.3202, 0.7109, 0.9868, ..., 0.4243, 0.8639, 0.9226])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([100000, 100000])
|
||||
Rows: 100000
|
||||
Size: 10000000000
|
||||
NNZ: 500000
|
||||
Density: 5e-05
|
||||
Time: 0.24062752723693848 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '43635', '-ss', '100000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.050816535949707}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 6, 11, ..., 499985, 499991,
|
||||
500000]),
|
||||
col_indices=tensor([ 2943, 35530, 40183, ..., 77324, 82017, 92181]),
|
||||
values=tensor([0.7176, 0.7408, 0.8709, ..., 0.2236, 0.1509, 0.6788]),
|
||||
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
|
||||
tensor([0.0661, 0.4747, 0.8915, ..., 0.6458, 0.7762, 0.4694])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([100000, 100000])
|
||||
Rows: 100000
|
||||
Size: 10000000000
|
||||
NNZ: 500000
|
||||
Density: 5e-05
|
||||
Time: 10.050816535949707 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, 6, 11, ..., 499985, 499991,
|
||||
500000]),
|
||||
col_indices=tensor([ 2943, 35530, 40183, ..., 77324, 82017, 92181]),
|
||||
values=tensor([0.7176, 0.7408, 0.8709, ..., 0.2236, 0.1509, 0.6788]),
|
||||
size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
|
||||
tensor([0.0661, 0.4747, 0.8915, ..., 0.6458, 0.7762, 0.4694])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([100000, 100000])
|
||||
Rows: 100000
|
||||
Size: 10000000000
|
||||
NNZ: 500000
|
||||
Density: 5e-05
|
||||
Time: 10.050816535949707 seconds
|
||||
|
||||
[18.28, 18.77, 17.89, 17.54, 18.06, 17.6, 17.83, 17.74, 18.0, 17.55]
|
||||
[89.55]
|
||||
13.673717737197876
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43635, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.050816535949707, 'TIME_S_1KI': 0.23033841035750444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.4814233660697, 'W': 89.55}
|
||||
[18.28, 18.77, 17.89, 17.54, 18.06, 17.6, 17.83, 17.74, 18.0, 17.55, 18.31, 17.61, 17.7, 17.7, 17.86, 17.61, 17.69, 21.85, 18.07, 17.82]
|
||||
325.5
|
||||
16.275
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43635, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.050816535949707, 'TIME_S_1KI': 0.23033841035750444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1224.4814233660697, 'W': 89.55, 'J_1KI': 28.06190955347931, 'W_1KI': 2.052251632863527, 'W_D': 73.275, 'J_D': 1001.9416671931745, 'W_D_1KI': 1.679271227225851, 'J_D_1KI': 0.03848450159793402}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1414, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.28559684753418, "TIME_S_1KI": 7.27411375356024, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2220.7405538129806, "W": 61.419999999999995, "J_1KI": 1570.5378739837204, "W_1KI": 43.437057991513434, "W_D": 45.185249999999996, "J_D": 1633.7466152585148, "W_D_1KI": 31.955622347949074, "J_D_1KI": 22.599450033910237}
|
||||
@ -0,0 +1,65 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '0.2', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 7.425642490386963}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 2015, 4097, ..., 19995972,
|
||||
19998068, 20000000]),
|
||||
col_indices=tensor([ 2, 3, 5, ..., 9990, 9995, 9998]),
|
||||
values=tensor([0.5695, 0.2550, 0.6356, ..., 0.8876, 0.2921, 0.6555]),
|
||||
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
|
||||
tensor([0.8689, 0.2102, 0.7438, ..., 0.4178, 0.9795, 0.1690])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 20000000
|
||||
Density: 0.2
|
||||
Time: 7.425642490386963 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1414', '-ss', '10000', '-sd', '0.2', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.28559684753418}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 1956, 4016, ..., 19996011,
|
||||
19998026, 20000000]),
|
||||
col_indices=tensor([ 0, 3, 4, ..., 9992, 9993, 9997]),
|
||||
values=tensor([0.4860, 0.9038, 0.0641, ..., 0.5614, 0.3748, 0.7950]),
|
||||
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
|
||||
tensor([0.5984, 0.1665, 0.8658, ..., 0.8665, 0.0874, 0.1467])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 20000000
|
||||
Density: 0.2
|
||||
Time: 10.28559684753418 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, 1956, 4016, ..., 19996011,
|
||||
19998026, 20000000]),
|
||||
col_indices=tensor([ 0, 3, 4, ..., 9992, 9993, 9997]),
|
||||
values=tensor([0.4860, 0.9038, 0.0641, ..., 0.5614, 0.3748, 0.7950]),
|
||||
size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
|
||||
tensor([0.5984, 0.1665, 0.8658, ..., 0.8665, 0.0874, 0.1467])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 20000000
|
||||
Density: 0.2
|
||||
Time: 10.28559684753418 seconds
|
||||
|
||||
[18.83, 17.69, 17.79, 17.74, 18.11, 18.01, 17.84, 17.99, 20.88, 19.1]
|
||||
[61.42]
|
||||
36.15663552284241
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1414, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.28559684753418, 'TIME_S_1KI': 7.27411375356024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2220.7405538129806, 'W': 61.419999999999995}
|
||||
[18.83, 17.69, 17.79, 17.74, 18.11, 18.01, 17.84, 17.99, 20.88, 19.1, 17.89, 17.55, 18.69, 17.55, 17.64, 17.53, 17.47, 17.68, 17.69, 17.87]
|
||||
324.69500000000005
|
||||
16.234750000000002
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1414, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.28559684753418, 'TIME_S_1KI': 7.27411375356024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2220.7405538129806, 'W': 61.419999999999995, 'J_1KI': 1570.5378739837204, 'W_1KI': 43.437057991513434, 'W_D': 45.185249999999996, 'J_D': 1633.7466152585148, 'W_D_1KI': 31.955622347949074, 'J_D_1KI': 22.599450033910237}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 269593, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.567216634750366, "TIME_S_1KI": 0.03919692512324269, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1116.8250253772735, "W": 79.97999999999999, "J_1KI": 4.142633619482974, "W_1KI": 0.29666942390937445, "W_D": 63.59324999999999, "J_D": 888.003663979411, "W_D_1KI": 0.23588613205832493, "J_D_1KI": 0.0008749712791442097}
|
||||
@ -0,0 +1,81 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '10000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.056284427642822266}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 5000, 5000, 5000]),
|
||||
col_indices=tensor([8618, 2157, 180, ..., 6301, 3776, 9276]),
|
||||
values=tensor([0.6370, 0.4514, 0.1911, ..., 0.9917, 0.3039, 0.3760]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.3110, 0.1501, 0.3783, ..., 0.1317, 0.1435, 0.1761])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 0.056284427642822266 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '186552', '-ss', '10000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.265737533569336}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 4998, 4999, 5000]),
|
||||
col_indices=tensor([3104, 929, 8564, ..., 6717, 8359, 928]),
|
||||
values=tensor([0.4370, 0.1053, 0.0742, ..., 0.7004, 0.4944, 0.4492]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.3493, 0.4826, 0.4715, ..., 0.3349, 0.2581, 0.7669])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 7.265737533569336 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '269593', '-ss', '10000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.567216634750366}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 4999, 5000, 5000]),
|
||||
col_indices=tensor([2457, 434, 2201, ..., 5356, 91, 9583]),
|
||||
values=tensor([0.6200, 0.7893, 0.7607, ..., 0.6419, 0.6044, 0.8766]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.5913, 0.4546, 0.3506, ..., 0.4687, 0.7353, 0.8006])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 10.567216634750366 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, ..., 4999, 5000, 5000]),
|
||||
col_indices=tensor([2457, 434, 2201, ..., 5356, 91, 9583]),
|
||||
values=tensor([0.6200, 0.7893, 0.7607, ..., 0.6419, 0.6044, 0.8766]),
|
||||
size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
|
||||
tensor([0.5913, 0.4546, 0.3506, ..., 0.4687, 0.7353, 0.8006])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([10000, 10000])
|
||||
Rows: 10000
|
||||
Size: 100000000
|
||||
NNZ: 5000
|
||||
Density: 5e-05
|
||||
Time: 10.567216634750366 seconds
|
||||
|
||||
[17.89, 17.35, 17.61, 17.72, 17.83, 17.6, 17.61, 17.87, 22.45, 17.59]
|
||||
[79.98]
|
||||
13.963803768157959
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 269593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.567216634750366, 'TIME_S_1KI': 0.03919692512324269, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1116.8250253772735, 'W': 79.97999999999999}
|
||||
[17.89, 17.35, 17.61, 17.72, 17.83, 17.6, 17.61, 17.87, 22.45, 17.59, 18.03, 17.7, 17.74, 17.72, 21.48, 17.84, 17.81, 17.57, 18.13, 17.9]
|
||||
327.735
|
||||
16.38675
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 269593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.567216634750366, 'TIME_S_1KI': 0.03919692512324269, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1116.8250253772735, 'W': 79.97999999999999, 'J_1KI': 4.142633619482974, 'W_1KI': 0.29666942390937445, 'W_D': 63.59324999999999, 'J_D': 888.003663979411, 'W_D_1KI': 0.23588613205832493, 'J_D_1KI': 0.0008749712791442097}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1325, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.266479253768921, "TIME_S_1KI": 7.748286229259563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1511.6237173461914, "W": 78.72, "J_1KI": 1140.8480885631634, "W_1KI": 59.41132075471698, "W_D": 51.030249999999995, "J_D": 979.9102667950391, "W_D_1KI": 38.5133962264151, "J_D_1KI": 29.06671413314347}
|
||||
@ -0,0 +1,68 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '500000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.920783042907715}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 27, 58, ..., 12499955,
|
||||
12499976, 12500000]),
|
||||
col_indices=tensor([ 1130, 30184, 52843, ..., 432238, 460389,
|
||||
464098]),
|
||||
values=tensor([0.9711, 0.9391, 0.1931, ..., 0.2077, 0.5139, 0.7168]),
|
||||
size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
|
||||
tensor([0.7084, 0.4119, 0.9069, ..., 0.7058, 0.3504, 0.1364])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([500000, 500000])
|
||||
Rows: 500000
|
||||
Size: 250000000000
|
||||
NNZ: 12500000
|
||||
Density: 5e-05
|
||||
Time: 7.920783042907715 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1325', '-ss', '500000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.266479253768921}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 47, ..., 12499960,
|
||||
12499984, 12500000]),
|
||||
col_indices=tensor([105223, 111339, 112839, ..., 478264, 484121,
|
||||
494514]),
|
||||
values=tensor([0.5455, 0.8774, 0.0623, ..., 0.6447, 0.0740, 0.9564]),
|
||||
size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
|
||||
tensor([0.8058, 0.5348, 0.2222, ..., 0.5938, 0.1996, 0.3404])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([500000, 500000])
|
||||
Rows: 500000
|
||||
Size: 250000000000
|
||||
NNZ: 12500000
|
||||
Density: 5e-05
|
||||
Time: 10.266479253768921 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, 47, ..., 12499960,
|
||||
12499984, 12500000]),
|
||||
col_indices=tensor([105223, 111339, 112839, ..., 478264, 484121,
|
||||
494514]),
|
||||
values=tensor([0.5455, 0.8774, 0.0623, ..., 0.6447, 0.0740, 0.9564]),
|
||||
size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
|
||||
tensor([0.8058, 0.5348, 0.2222, ..., 0.5938, 0.1996, 0.3404])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([500000, 500000])
|
||||
Rows: 500000
|
||||
Size: 250000000000
|
||||
NNZ: 12500000
|
||||
Density: 5e-05
|
||||
Time: 10.266479253768921 seconds
|
||||
|
||||
[18.04, 18.12, 17.82, 17.66, 17.96, 17.84, 17.85, 17.56, 17.9, 17.9]
|
||||
[78.72]
|
||||
19.202537059783936
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.266479253768921, 'TIME_S_1KI': 7.748286229259563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1511.6237173461914, 'W': 78.72}
|
||||
[18.04, 18.12, 17.82, 17.66, 17.96, 17.84, 17.85, 17.56, 17.9, 17.9, 42.26, 43.36, 45.35, 45.57, 48.14, 42.56, 42.0, 41.9, 42.22, 41.77]
|
||||
553.7950000000001
|
||||
27.689750000000004
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.266479253768921, 'TIME_S_1KI': 7.748286229259563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1511.6237173461914, 'W': 78.72, 'J_1KI': 1140.8480885631634, 'W_1KI': 59.41132075471698, 'W_D': 51.030249999999995, 'J_D': 979.9102667950391, 'W_D_1KI': 38.5133962264151, 'J_D_1KI': 29.06671413314347}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 87647, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.566825151443481, "TIME_S_1KI": 0.12056117324544459, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1221.4293744707109, "W": 86.61000000000001, "J_1KI": 13.935780739451559, "W_1KI": 0.9881684484352005, "W_D": 70.62725000000002, "J_D": 996.0304559298756, "W_D_1KI": 0.8058148025602704, "J_D_1KI": 0.009193866333819417}
|
||||
@ -0,0 +1,85 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '50000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.14960765838623047}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 124995, 124998,
|
||||
125000]),
|
||||
col_indices=tensor([ 5328, 5758, 21245, ..., 40217, 2052, 16010]),
|
||||
values=tensor([0.8699, 0.1385, 0.5605, ..., 0.3914, 0.4912, 0.7839]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.1699, 0.5973, 0.2081, ..., 0.0281, 0.7559, 0.8178])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 0.14960765838623047 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '70183', '-ss', '50000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.40775990486145}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 8, ..., 124997, 124999,
|
||||
125000]),
|
||||
col_indices=tensor([ 5449, 14906, 26173, ..., 30325, 48181, 9186]),
|
||||
values=tensor([0.5897, 0.0944, 0.5204, ..., 0.1014, 0.9614, 0.5057]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.5323, 0.2435, 0.2265, ..., 0.3258, 0.3469, 0.7874])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 8.40775990486145 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '87647', '-ss', '50000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.566825151443481}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 5, ..., 124995, 124996,
|
||||
125000]),
|
||||
col_indices=tensor([14301, 36836, 2496, ..., 26599, 44389, 45216]),
|
||||
values=tensor([0.0827, 0.6231, 0.3315, ..., 0.4386, 0.5843, 0.2734]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.3713, 0.0426, 0.5176, ..., 0.5970, 0.6758, 0.3745])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 10.566825151443481 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, 5, ..., 124995, 124996,
|
||||
125000]),
|
||||
col_indices=tensor([14301, 36836, 2496, ..., 26599, 44389, 45216]),
|
||||
values=tensor([0.0827, 0.6231, 0.3315, ..., 0.4386, 0.5843, 0.2734]),
|
||||
size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
|
||||
tensor([0.3713, 0.0426, 0.5176, ..., 0.5970, 0.6758, 0.3745])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([50000, 50000])
|
||||
Rows: 50000
|
||||
Size: 2500000000
|
||||
NNZ: 125000
|
||||
Density: 5e-05
|
||||
Time: 10.566825151443481 seconds
|
||||
|
||||
[18.15, 17.58, 18.01, 17.67, 17.78, 17.59, 17.99, 17.46, 17.88, 17.7]
|
||||
[86.61]
|
||||
14.102636814117432
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.566825151443481, 'TIME_S_1KI': 0.12056117324544459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4293744707109, 'W': 86.61000000000001}
|
||||
[18.15, 17.58, 18.01, 17.67, 17.78, 17.59, 17.99, 17.46, 17.88, 17.7, 18.22, 17.6, 17.52, 17.68, 18.1, 17.84, 17.59, 17.63, 17.89, 17.62]
|
||||
319.655
|
||||
15.98275
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.566825151443481, 'TIME_S_1KI': 0.12056117324544459, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4293744707109, 'W': 86.61000000000001, 'J_1KI': 13.935780739451559, 'W_1KI': 0.9881684484352005, 'W_D': 70.62725000000002, 'J_D': 996.0304559298756, 'W_D_1KI': 0.8058148025602704, 'J_D_1KI': 0.009193866333819417}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8907, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.383960962295532, "TIME_S_1KI": 1.1658202494998913, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1297.8042968559266, "W": 87.12, "J_1KI": 145.7061072028659, "W_1KI": 9.781071067699562, "W_D": 70.95125, "J_D": 1056.9425748082995, "W_D_1KI": 7.965785337375098, "J_D_1KI": 0.8943286558184684}
|
||||
@ -0,0 +1,65 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '0.2', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.1788151264190674}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 1020, 2047, ..., 4997993,
|
||||
4999046, 5000000]),
|
||||
col_indices=tensor([ 0, 5, 11, ..., 4968, 4973, 4981]),
|
||||
values=tensor([0.5537, 0.3394, 0.2735, ..., 0.5110, 0.5845, 0.1364]),
|
||||
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
|
||||
tensor([0.2008, 0.9176, 0.6130, ..., 0.2299, 0.3337, 0.1107])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 5000000
|
||||
Density: 0.2
|
||||
Time: 1.1788151264190674 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8907', '-ss', '5000', '-sd', '0.2', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.383960962295532}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
|
||||
matrix = matrix.to_sparse_csr().type(torch.float32)
|
||||
tensor(crow_indices=tensor([ 0, 1043, 2083, ..., 4998055,
|
||||
4999016, 5000000]),
|
||||
col_indices=tensor([ 11, 19, 21, ..., 4992, 4995, 4997]),
|
||||
values=tensor([0.8807, 0.7903, 0.9771, ..., 0.4816, 0.8305, 0.1823]),
|
||||
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
|
||||
tensor([0.8731, 0.0343, 0.1598, ..., 0.1067, 0.9050, 0.6111])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 5000000
|
||||
Density: 0.2
|
||||
Time: 10.383960962295532 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, 1043, 2083, ..., 4998055,
|
||||
4999016, 5000000]),
|
||||
col_indices=tensor([ 11, 19, 21, ..., 4992, 4995, 4997]),
|
||||
values=tensor([0.8807, 0.7903, 0.9771, ..., 0.4816, 0.8305, 0.1823]),
|
||||
size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
|
||||
tensor([0.8731, 0.0343, 0.1598, ..., 0.1067, 0.9050, 0.6111])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 5000000
|
||||
Density: 0.2
|
||||
Time: 10.383960962295532 seconds
|
||||
|
||||
[17.98, 21.91, 18.08, 17.67, 17.68, 17.88, 18.1, 17.78, 17.61, 17.52]
|
||||
[87.12]
|
||||
14.896743535995483
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8907, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.383960962295532, 'TIME_S_1KI': 1.1658202494998913, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.8042968559266, 'W': 87.12}
|
||||
[17.98, 21.91, 18.08, 17.67, 17.68, 17.88, 18.1, 17.78, 17.61, 17.52, 18.28, 17.97, 17.57, 17.52, 17.69, 17.59, 17.67, 17.39, 17.74, 17.27]
|
||||
323.375
|
||||
16.16875
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8907, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.383960962295532, 'TIME_S_1KI': 1.1658202494998913, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.8042968559266, 'W': 87.12, 'J_1KI': 145.7061072028659, 'W_1KI': 9.781071067699562, 'W_D': 70.95125, 'J_D': 1056.9425748082995, 'W_D_1KI': 7.965785337375098, 'J_D_1KI': 0.8943286558184684}
|
||||
@ -0,0 +1 @@
|
||||
{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 355197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.57451844215393, "TIME_S_1KI": 0.029770855165313703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1141.788979816437, "W": 79.9, "J_1KI": 3.214523151424243, "W_1KI": 0.2249455935720178, "W_D": 63.777750000000005, "J_D": 911.3983993427754, "W_D_1KI": 0.17955599287156143, "J_D_1KI": 0.000505511006206588}
|
||||
@ -0,0 +1,81 @@
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1000', '-ss', '5000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04642629623413086}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 1, ..., 1249, 1249, 1250]),
|
||||
col_indices=tensor([1959, 1786, 16, ..., 4660, 524, 2490]),
|
||||
values=tensor([0.5665, 0.4844, 0.2984, ..., 0.5218, 0.3017, 0.5058]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.6773, 0.1820, 0.1692, ..., 0.1637, 0.2279, 0.2140])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 0.04642629623413086 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '226164', '-ss', '5000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.685633659362793}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, 1, ..., 1250, 1250, 1250]),
|
||||
col_indices=tensor([4154, 3836, 1179, ..., 2527, 1259, 153]),
|
||||
values=tensor([0.7262, 0.5127, 0.2351, ..., 0.4025, 0.3877, 0.1384]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.5042, 0.5821, 0.3979, ..., 0.3479, 0.7780, 0.3728])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 6.685633659362793 seconds
|
||||
|
||||
['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '355197', '-ss', '5000', '-sd', '5e-05', '-c', '16']
|
||||
{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.57451844215393}
|
||||
|
||||
/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/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, ..., 1250, 1250, 1250]),
|
||||
col_indices=tensor([2741, 2960, 2565, ..., 976, 304, 1232]),
|
||||
values=tensor([0.5180, 0.1270, 0.6648, ..., 0.3095, 0.3853, 0.9447]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.1571, 0.5465, 0.3582, ..., 0.1118, 0.4116, 0.5757])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 10.57451844215393 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, ..., 1250, 1250, 1250]),
|
||||
col_indices=tensor([2741, 2960, 2565, ..., 976, 304, 1232]),
|
||||
values=tensor([0.5180, 0.1270, 0.6648, ..., 0.3095, 0.3853, 0.9447]),
|
||||
size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
|
||||
tensor([0.1571, 0.5465, 0.3582, ..., 0.1118, 0.4116, 0.5757])
|
||||
Matrix Type: synthetic
|
||||
Matrix Format: csr
|
||||
Shape: torch.Size([5000, 5000])
|
||||
Rows: 5000
|
||||
Size: 25000000
|
||||
NNZ: 1250
|
||||
Density: 5e-05
|
||||
Time: 10.57451844215393 seconds
|
||||
|
||||
[17.86, 17.95, 18.35, 17.84, 17.76, 17.65, 18.09, 17.61, 17.89, 17.57]
|
||||
[79.9]
|
||||
14.2902250289917
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 355197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.57451844215393, 'TIME_S_1KI': 0.029770855165313703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1141.788979816437, 'W': 79.9}
|
||||
[17.86, 17.95, 18.35, 17.84, 17.76, 17.65, 18.09, 17.61, 17.89, 17.57, 18.28, 17.96, 17.74, 18.66, 18.09, 17.53, 17.73, 17.6, 18.05, 18.18]
|
||||
322.44500000000005
|
||||
16.12225
|
||||
{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 355197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.57451844215393, 'TIME_S_1KI': 0.029770855165313703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1141.788979816437, 'W': 79.9, 'J_1KI': 3.214523151424243, 'W_1KI': 0.2249455935720178, 'W_D': 63.777750000000005, 'J_D': 911.3983993427754, 'W_D_1KI': 0.17955599287156143, 'J_D_1KI': 0.000505511006206588}
|
||||
@ -1,6 +1,8 @@
|
||||
0.00001
|
||||
0.00005
|
||||
0.0001
|
||||
0.001
|
||||
0.01
|
||||
0.05
|
||||
0.1
|
||||
0.2
|
||||
|
||||
Loading…
Reference in New Issue
Block a user