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add Benchmark (pytest) benchmark result for eca7917
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github-action-benchmark committed Jul 30, 2024
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110 changes: 109 additions & 1 deletion dev/bench/data.js
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window.BENCHMARK_DATA = {
"lastUpdate": 1722377988341,
"lastUpdate": 1722382755802,
"repoUrl": "https://github.com/MPACT-ORG/mpact-compiler",
"entries": {
"Benchmark": [
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"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"
}
]
}
]
}
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