add Benchmark (pytest) benchmark result for f37a36c213c593245e7da4c1c1e085986e993556
diff --git a/dev/bench/data.js b/dev/bench/data.js
index 3507a13..ec62246 100644
--- a/dev/bench/data.js
+++ b/dev/bench/data.js
@@ -1,5 +1,5 @@
window.BENCHMARK_DATA = {
- "lastUpdate": 1722361982606,
+ "lastUpdate": 1722377988341,
"repoUrl": "https://github.com/MPACT-ORG/mpact-compiler",
"entries": {
"Benchmark": [
@@ -1174,6 +1174,114 @@
"extra": "mean: 49.277214444455495 msec\nrounds: 18"
}
]
+ },
+ {
+ "commit": {
+ "author": {
+ "email": "ajcbik@google.com",
+ "name": "Aart Bik",
+ "username": "aartbik"
+ },
+ "committer": {
+ "email": "noreply@github.com",
+ "name": "GitHub",
+ "username": "web-flow"
+ },
+ "distinct": true,
+ "id": "f37a36c213c593245e7da4c1c1e085986e993556",
+ "message": "[mpact][benchmark] manual sum of squares benchmark (#65)\n\n* [mpact][benchmark] manual sum of squares benchmark\r\n\r\nThis introduces a \"manual\" benchmark where we can put\r\nsome benchmarking code but without negatively adding\r\nmore load on the regular benchmark suite times.\r\n\r\n* use 4K instead of 1K\r\n\r\n* lint\r\n\r\n* undo edits",
+ "timestamp": "2024-07-30T15:14:56-07:00",
+ "tree_id": "39168f032bdcc8a083c5f708173a6e9aec58cac4",
+ "url": "https://github.com/MPACT-ORG/mpact-compiler/commit/f37a36c213c593245e7da4c1c1e085986e993556"
+ },
+ "date": 1722377987492,
+ "tool": "pytest",
+ "benches": [
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense",
+ "value": 5891.215859330467,
+ "unit": "iter/sec",
+ "range": "stddev: 0.000009263101696785644",
+ "extra": "mean: 169.74424700738254 usec\nrounds: 1838"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense",
+ "value": 34.831555094827095,
+ "unit": "iter/sec",
+ "range": "stddev: 0.000355854570805469",
+ "extra": "mean: 28.709599593746304 msec\nrounds: 32"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense",
+ "value": 5445.977050205876,
+ "unit": "iter/sec",
+ "range": "stddev: 0.00003580323977868441",
+ "extra": "mean: 183.62178003710034 usec\nrounds: 2164"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense",
+ "value": 5625.905057197205,
+ "unit": "iter/sec",
+ "range": "stddev: 0.000028657618654726816",
+ "extra": "mean: 177.74917810258864 usec\nrounds: 3425"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense",
+ "value": 979858.7836545728,
+ "unit": "iter/sec",
+ "range": "stddev: 1.9583009278486146e-7",
+ "extra": "mean: 1.020555223549976 usec\nrounds: 145943"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense",
+ "value": 31.717768854035093,
+ "unit": "iter/sec",
+ "range": "stddev: 0.00037133612139495745",
+ "extra": "mean: 31.528068843744705 msec\nrounds: 32"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse",
+ "value": 12508.816594272885,
+ "unit": "iter/sec",
+ "range": "stddev: 0.0000036073931774760563",
+ "extra": "mean: 79.94361356755734 usec\nrounds: 3302"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse",
+ "value": 20.10344503346346,
+ "unit": "iter/sec",
+ "range": "stddev: 0.0005382919104048668",
+ "extra": "mean: 49.74271814285744 msec\nrounds: 21"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse",
+ "value": 212.68125149753777,
+ "unit": "iter/sec",
+ "range": "stddev: 0.0005056017753375226",
+ "extra": "mean: 4.701871899656266 msec\nrounds: 289"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse",
+ "value": 188.3627523972336,
+ "unit": "iter/sec",
+ "range": "stddev: 0.00007639564544060225",
+ "extra": "mean: 5.308905222892074 msec\nrounds: 166"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse",
+ "value": 968079.2484267106,
+ "unit": "iter/sec",
+ "range": "stddev: 2.8657478729273046e-7",
+ "extra": "mean: 1.0329732835665737 usec\nrounds: 166639"
+ },
+ {
+ "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse",
+ "value": 21.591512367849305,
+ "unit": "iter/sec",
+ "range": "stddev: 0.0023095925263329473",
+ "extra": "mean: 46.314495388893796 msec\nrounds: 18"
+ }
+ ]
}
]
}
