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add Benchmark (pytest) benchmark result for eca7917
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Jul 30, 2024
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window.BENCHMARK_DATA = { | ||
"lastUpdate": 1722377988341, | ||
"lastUpdate": 1722382755802, | ||
"repoUrl": "https://github.com/MPACT-ORG/mpact-compiler", | ||
"entries": { | ||
"Benchmark": [ | ||
|
@@ -1282,6 +1282,114 @@ window.BENCHMARK_DATA = { | |
"extra": "mean: 46.314495388893796 msec\nrounds: 18" | ||
} | ||
] | ||
}, | ||
{ | ||
"commit": { | ||
"author": { | ||
"email": "[email protected]", | ||
"name": "Aart Bik", | ||
"username": "aartbik" | ||
}, | ||
"committer": { | ||
"email": "[email protected]", | ||
"name": "GitHub", | ||
"username": "web-flow" | ||
}, | ||
"distinct": true, | ||
"id": "eca7917e14dd523e7f048d9e53bd35a55bbf5283", | ||
"message": "[mpact][file-formats] add matrix market and extended frostt utils (#66)\n\n* [mpact][file-formats] add matrix market and extended frostt utils\r\n\r\n* add mm back\r\n\r\n* add benchmark util dep to test", | ||
"timestamp": "2024-07-30T16:32:45-07:00", | ||
"tree_id": "6a52876d649b94ee6fc8a87d896059570a3171c6", | ||
"url": "https://github.com/MPACT-ORG/mpact-compiler/commit/eca7917e14dd523e7f048d9e53bd35a55bbf5283" | ||
}, | ||
"date": 1722382755308, | ||
"tool": "pytest", | ||
"benches": [ | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", | ||
"value": 5914.089269005995, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.000006207544946596451", | ||
"extra": "mean: 169.08774191838907 usec\nrounds: 1918" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", | ||
"value": 34.47596113511465, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0003280315876168108", | ||
"extra": "mean: 29.005717812504273 msec\nrounds: 32" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", | ||
"value": 5671.871754573004, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00005037490581542402", | ||
"extra": "mean: 176.30864082808995 usec\nrounds: 2464" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", | ||
"value": 5713.32031009563, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00008044588444592375", | ||
"extra": "mean: 175.02957049913098 usec\nrounds: 2766" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", | ||
"value": 943332.1798233077, | ||
"unit": "iter/sec", | ||
"range": "stddev: 2.0259622974613897e-7", | ||
"extra": "mean: 1.0600719676363701 usec\nrounds: 146135" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", | ||
"value": 31.176506743812425, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0004640359792791233", | ||
"extra": "mean: 32.075434500001165 msec\nrounds: 32" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", | ||
"value": 12152.82118101611, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.000004941333927079426", | ||
"extra": "mean: 82.28542040609446 usec\nrounds: 2852" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", | ||
"value": 19.841889368834504, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0009961412782114644", | ||
"extra": "mean: 50.398426349997294 msec\nrounds: 20" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", | ||
"value": 208.30234475482368, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0006701236520992671", | ||
"extra": "mean: 4.800714083065275 msec\nrounds: 313" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", | ||
"value": 186.8760133680631, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00022784912720946965", | ||
"extra": "mean: 5.351141550897933 msec\nrounds: 167" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", | ||
"value": 955837.5424754084, | ||
"unit": "iter/sec", | ||
"range": "stddev: 1.7599020963282733e-7", | ||
"extra": "mean: 1.0462028907236898 usec\nrounds: 128966" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", | ||
"value": 19.17946487939754, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.003315302338347528", | ||
"extra": "mean: 52.139098055555955 msec\nrounds: 18" | ||
} | ||
] | ||
} | ||
] | ||
} | ||
|