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add Benchmark (pytest) benchmark result for c21ae86
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@@ -1,5 +1,5 @@ | ||
window.BENCHMARK_DATA = { | ||
"lastUpdate": 1725400020262, | ||
"lastUpdate": 1725402020391, | ||
"repoUrl": "https://github.com/MPACT-ORG/mpact-compiler", | ||
"entries": { | ||
"Benchmark": [ | ||
|
@@ -1930,6 +1930,114 @@ window.BENCHMARK_DATA = { | |
"extra": "mean: 46.63099949993921 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": "c21ae8604891021c4025ae7835b0efb6cc516016", | ||
"message": "[mpact][compiler] re-enable sparse addition tests (#75)\n\nAll PyTorch related fixes are committed to upstream\r\nPyTorch dev branch, so we can run all the tests again", | ||
"timestamp": "2024-09-03T15:16:04-07:00", | ||
"tree_id": "653f90feced3bb5de7e7922b94a49dd16765de37", | ||
"url": "https://github.com/MPACT-ORG/mpact-compiler/commit/c21ae8604891021c4025ae7835b0efb6cc516016" | ||
}, | ||
"date": 1725402020088, | ||
"tool": "pytest", | ||
"benches": [ | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", | ||
"value": 5984.604402864983, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.000004773741454056456", | ||
"extra": "mean: 167.0954223008081 usec\nrounds: 1982" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", | ||
"value": 34.89539889357125, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00046478152323981605", | ||
"extra": "mean: 28.657073187497772 msec\nrounds: 32" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", | ||
"value": 6071.164975976731, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.000037416318029174416", | ||
"extra": "mean: 164.71303348812717 usec\nrounds: 2150" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", | ||
"value": 5268.847212471226, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.000034164794229824113", | ||
"extra": "mean: 189.79483740447543 usec\nrounds: 2374" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", | ||
"value": 875386.0539375842, | ||
"unit": "iter/sec", | ||
"range": "stddev: 2.0204369637331367e-7", | ||
"extra": "mean: 1.1423531315148194 usec\nrounds: 144238" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", | ||
"value": 31.391055873061557, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00035675118926051014", | ||
"extra": "mean: 31.85620783333244 msec\nrounds: 30" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", | ||
"value": 12574.209947811167, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.000005106988003315575", | ||
"extra": "mean: 79.52785933672702 usec\nrounds: 3256" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", | ||
"value": 19.93248175538609, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.001036558393310738", | ||
"extra": "mean: 50.169367380947605 msec\nrounds: 21" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", | ||
"value": 194.33308860500836, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0008889728885080983", | ||
"extra": "mean: 5.145804078854269 msec\nrounds: 279" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", | ||
"value": 187.16066454664005, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00020579674692953573", | ||
"extra": "mean: 5.343003041917507 msec\nrounds: 167" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", | ||
"value": 871690.1013576641, | ||
"unit": "iter/sec", | ||
"range": "stddev: 2.138761759236975e-7", | ||
"extra": "mean: 1.1471966911663816 usec\nrounds: 184843" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", | ||
"value": 21.531835619041946, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.005220279923927693", | ||
"extra": "mean: 46.44285873683884 msec\nrounds: 19" | ||
} | ||
] | ||
} | ||
] | ||
} | ||
|