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add Benchmark (pytest) benchmark result for 556009c
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Sep 12, 2024
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window.BENCHMARK_DATA = { | ||
"lastUpdate": 1726130265672, | ||
"lastUpdate": 1726171671142, | ||
"repoUrl": "https://github.com/MPACT-ORG/mpact-compiler", | ||
"entries": { | ||
"Benchmark": [ | ||
|
@@ -2254,6 +2254,114 @@ window.BENCHMARK_DATA = { | |
"extra": "mean: 45.56706211112922 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": "556009cda5b9d1befb943cb439d5aab5aaa28a7b", | ||
"message": "[mpact][compiler] add stable hlo pipeline (#78)\n\nadds a lowering to stable hlo method in addition\r\nto lowering to linalg; note that this can be used\r\nas an alternative path into the mpact pipeline", | ||
"timestamp": "2024-09-12T13:03:26-07:00", | ||
"tree_id": "c132dfa864fb1eb3817199a6a466dbc3d04707bf", | ||
"url": "https://github.com/MPACT-ORG/mpact-compiler/commit/556009cda5b9d1befb943cb439d5aab5aaa28a7b" | ||
}, | ||
"date": 1726171670822, | ||
"tool": "pytest", | ||
"benches": [ | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", | ||
"value": 6646.579164247324, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.000006182923038083194", | ||
"extra": "mean: 150.45333475889512 usec\nrounds: 1870" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", | ||
"value": 33.44298782328942, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0005074393597061762", | ||
"extra": "mean: 29.90163454545196 msec\nrounds: 33" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", | ||
"value": 4970.319979551754, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0000808525090647376", | ||
"extra": "mean: 201.19429012901992 usec\nrounds: 1003" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", | ||
"value": 5558.053722146078, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.000034640726148464404", | ||
"extra": "mean: 179.91909578266535 usec\nrounds: 2798" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", | ||
"value": 829999.6467168161, | ||
"unit": "iter/sec", | ||
"range": "stddev: 2.3605975930713207e-7", | ||
"extra": "mean: 1.2048197899308088 usec\nrounds: 136352" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", | ||
"value": 31.475469546581827, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0005501207767812787", | ||
"extra": "mean: 31.770773062497426 msec\nrounds: 32" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", | ||
"value": 12326.731020357509, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.000008093830561403162", | ||
"extra": "mean: 81.12450887007327 usec\nrounds: 3044" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", | ||
"value": 19.894403028680408, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0011684904009305079", | ||
"extra": "mean: 50.26539366666936 msec\nrounds: 21" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", | ||
"value": 202.74269097694668, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0007228784376801891", | ||
"extra": "mean: 4.932360299556778 msec\nrounds: 227" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", | ||
"value": 185.55104593619325, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.00007714804693757229", | ||
"extra": "mean: 5.389352536141871 msec\nrounds: 166" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", | ||
"value": 820980.7220351726, | ||
"unit": "iter/sec", | ||
"range": "stddev: 2.5644396420797874e-7", | ||
"extra": "mean: 1.2180553978430175 usec\nrounds: 95148" | ||
}, | ||
{ | ||
"name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", | ||
"value": 20.01659668739807, | ||
"unit": "iter/sec", | ||
"range": "stddev: 0.0013845172737135196", | ||
"extra": "mean: 49.95854268421035 msec\nrounds: 19" | ||
} | ||
] | ||
} | ||
] | ||
} | ||
|