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add Benchmark (pytest) benchmark result for a5c7bfa
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Aug 27, 2024
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@@ -1,5 +1,5 @@ | ||
window.BENCHMARK_DATA = { | ||
"lastUpdate": 1724190187063, | ||
"lastUpdate": 1724779552730, | ||
"repoUrl": "https://github.com/MPACT-ORG/mpact-compiler", | ||
"entries": { | ||
"Benchmark": [ | ||
|
@@ -1606,6 +1606,114 @@ window.BENCHMARK_DATA = { | |
"extra": "mean: 48.2182052104403 msec\nrounds: 19" | ||
} | ||
] | ||
}, | ||
{ | ||
"commit": { | ||
"author": { | ||
"email": "[email protected]", | ||
"name": "Aart Bik", | ||
"username": "aartbik" | ||
}, | ||
"committer": { | ||
"email": "[email protected]", | ||
"name": "GitHub", | ||
"username": "web-flow" | ||
}, | ||
"distinct": true, | ||
"id": "a5c7bfa6fe566c2065604f8892561ca16a4d43e3", | ||
"message": "[mpact] bump torch-mlir to @b92e61832f85f35ec (#72)", | ||
"timestamp": "2024-08-27T10:13:07-07:00", | ||
"tree_id": "223b8ae0767a6a90babb3d7f83f42379d8715d92", | ||
"url": "https://github.com/MPACT-ORG/mpact-compiler/commit/a5c7bfa6fe566c2065604f8892561ca16a4d43e3" | ||
}, | ||
"date": 1724779551878, | ||
"tool": "pytest", | ||
"benches": [ | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", | ||
"value": 6020.962344059295, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0000059883528790397716", | ||
"extra": "mean: 166.0864066002124 usec\nrounds: 1697" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", | ||
"value": 36.10155973561661, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0005150833480676415", | ||
"extra": "mean: 27.69963423528853 msec\nrounds: 34" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", | ||
"value": 5940.974203423838, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00003835876698407216", | ||
"extra": "mean: 168.32256221945735 usec\nrounds: 2668" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", | ||
"value": 5891.786731452907, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00004941253923273595", | ||
"extra": "mean: 169.7278000002219 usec\nrounds: 2400" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", | ||
"value": 987933.4297848109, | ||
"unit": "iter/sec", | ||
"range": "stddev: 1.8022626662534966e-7", | ||
"extra": "mean: 1.0122139507090244 usec\nrounds: 147646" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", | ||
"value": 32.812913107117254, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0004499927021638927", | ||
"extra": "mean: 30.475806787879982 msec\nrounds: 33" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", | ||
"value": 12354.524580494592, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0000059562901432246445", | ||
"extra": "mean: 80.94200577971304 usec\nrounds: 3114" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", | ||
"value": 19.295362255585047, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0008144339001935827", | ||
"extra": "mean: 51.825925149995555 msec\nrounds: 20" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", | ||
"value": 199.2530159731606, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0005389328754834914", | ||
"extra": "mean: 5.0187446102933775 msec\nrounds: 272" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", | ||
"value": 190.21873588712774, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00010598713423324759", | ||
"extra": "mean: 5.257105696430353 msec\nrounds: 168" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", | ||
"value": 1129071.6661179548, | ||
"unit": "iter/sec", | ||
"range": "stddev: 8.14334988631514e-8", | ||
"extra": "mean: 885.6833715775238 nsec\nrounds: 176026" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", | ||
"value": 22.72716253497586, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.001393856871036311", | ||
"extra": "mean: 44.00021333332108 msec\nrounds: 18" | ||
} | ||
] | ||
} | ||
] | ||
} | ||
|