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add Benchmark (pytest) benchmark result for 664f828
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github-action-benchmark committed Aug 27, 2024
1 parent 0568d21 commit 54f5316
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110 changes: 109 additions & 1 deletion dev/bench/data.js
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window.BENCHMARK_DATA = {
"lastUpdate": 1724779552730,
"lastUpdate": 1724790820082,
"repoUrl": "https://github.com/MPACT-ORG/mpact-compiler",
"entries": {
"Benchmark": [
Expand Down Expand Up @@ -1714,6 +1714,114 @@ window.BENCHMARK_DATA = {
"extra": "mean: 44.00021333332108 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": "664f828a95fd68221dc33c459af603ba867101c6",
"message": "[mpact][test] add a count-equal idiom (for sparse consideration) (#73)\n\nThe equal operator currently does not sparsify under\r\nPyTorch, but if it were, this would be a great candidate\r\nto further optimize with doing the sum() without\r\nmaterializing the intermediate result!",
"timestamp": "2024-08-27T13:28:53-07:00",
"tree_id": "504e4d0cdab959c343e7ebff48a45f4b82a6a918",
"url": "https://github.com/MPACT-ORG/mpact-compiler/commit/664f828a95fd68221dc33c459af603ba867101c6"
},
"date": 1724790819818,
"tool": "pytest",
"benches": [
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense",
"value": 6050.659470930161,
"unit": "iter/sec",
"range": "stddev: 0.0000184565628199794",
"extra": "mean: 165.2712410613105 usec\nrounds: 1734"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense",
"value": 33.97567954004175,
"unit": "iter/sec",
"range": "stddev: 0.0005465872113972789",
"extra": "mean: 29.43281822579761 msec\nrounds: 31"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense",
"value": 5557.791770595038,
"unit": "iter/sec",
"range": "stddev: 0.00005546977041156382",
"extra": "mean: 179.92757578482224 usec\nrounds: 1148"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense",
"value": 5616.631351823394,
"unit": "iter/sec",
"range": "stddev: 0.00002913511759109151",
"extra": "mean: 178.04266247158236 usec\nrounds: 3099"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense",
"value": 961418.8730476055,
"unit": "iter/sec",
"range": "stddev: 1.8536670980732955e-7",
"extra": "mean: 1.040129363000849 usec\nrounds: 150785"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense",
"value": 31.907652554828182,
"unit": "iter/sec",
"range": "stddev: 0.0007091211167101815",
"extra": "mean: 31.340444060610867 msec\nrounds: 33"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse",
"value": 12629.889025175798,
"unit": "iter/sec",
"range": "stddev: 0.000004217342540703227",
"extra": "mean: 79.1772594364566 usec\nrounds: 2914"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse",
"value": 20.00783610902815,
"unit": "iter/sec",
"range": "stddev: 0.0009212474128569103",
"extra": "mean: 49.980417399999055 msec\nrounds: 20"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse",
"value": 199.42045652933882,
"unit": "iter/sec",
"range": "stddev: 0.0009173406373704962",
"extra": "mean: 5.014530692606652 msec\nrounds: 257"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse",
"value": 187.91837121548917,
"unit": "iter/sec",
"range": "stddev: 0.00010238249428346425",
"extra": "mean: 5.321459490798178 msec\nrounds: 163"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse",
"value": 921316.0419683264,
"unit": "iter/sec",
"range": "stddev: 3.040267017295168e-7",
"extra": "mean: 1.08540387277266 usec\nrounds: 114078"
},
{
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse",
"value": 20.662496671787927,
"unit": "iter/sec",
"range": "stddev: 0.003815844732853682",
"extra": "mean: 48.39686200000099 msec\nrounds: 18"
}
]
}
]
}
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