| window.BENCHMARK_DATA = { |
| "lastUpdate": 1720646184767, |
| "repoUrl": "https://github.com/MPACT-ORG/mpact-compiler", |
| "entries": { |
| "Benchmark": [ |
| { |
| "commit": { |
| "author": { |
| "name": "MPACT-ORG", |
| "username": "MPACT-ORG" |
| }, |
| "committer": { |
| "name": "MPACT-ORG", |
| "username": "MPACT-ORG" |
| }, |
| "id": "86dff1a36cbd620f2d73af763e949ec00d777239", |
| "message": "[mpact][benchmark] add regression benchmark to gh page", |
| "timestamp": "2024-06-27T22:22:10Z", |
| "url": "https://github.com/MPACT-ORG/mpact-compiler/pull/52/commits/86dff1a36cbd620f2d73af763e949ec00d777239" |
| }, |
| "date": 1719528535086, |
| "tool": "pytest", |
| "benches": [ |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", |
| "value": 6669.427405418031, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000005968668091106435", |
| "extra": "mean: 149.93790909061124 usec\nrounds: 2057" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", |
| "value": 34.302140003556715, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0003297513733362601", |
| "extra": "mean: 29.15270009090722 msec\nrounds: 33" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", |
| "value": 5915.897194757258, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00003919411579877867", |
| "extra": "mean: 169.03606791649665 usec\nrounds: 1973" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", |
| "value": 6002.4939914783945, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000027105251732015887", |
| "extra": "mean: 166.59741790990168 usec\nrounds: 3551" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", |
| "value": 948864.5687594158, |
| "unit": "iter/sec", |
| "range": "stddev: 1.8437976818208762e-7", |
| "extra": "mean: 1.0538911799683288 usec\nrounds: 144238" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", |
| "value": 32.142115430220215, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000630869582999464", |
| "extra": "mean: 31.111829032254484 msec\nrounds: 31" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", |
| "value": 12377.336292065489, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000005090184458909657", |
| "extra": "mean: 80.79282782686057 usec\nrounds: 3299" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", |
| "value": 20.396281430385955, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0003606405379378096", |
| "extra": "mean: 49.02854490476979 msec\nrounds: 21" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", |
| "value": 210.87200147418721, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0005598196294447149", |
| "extra": "mean: 4.742213252632355 msec\nrounds: 285" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", |
| "value": 189.26652258818748, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00010650763765171751", |
| "extra": "mean: 5.2835545680512865 msec\nrounds: 169" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", |
| "value": 1093738.9035266023, |
| "unit": "iter/sec", |
| "range": "stddev: 8.140592632863567e-8", |
| "extra": "mean: 914.2949901257589 nsec\nrounds: 177589" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", |
| "value": 21.33905041385192, |
| "unit": "iter/sec", |
| "range": "stddev: 0.002583893132946909", |
| "extra": "mean: 46.862441421051486 msec\nrounds: 19" |
| } |
| ] |
| }, |
| { |
| "commit": { |
| "author": { |
| "name": "MPACT-ORG", |
| "username": "MPACT-ORG" |
| }, |
| "committer": { |
| "name": "MPACT-ORG", |
| "username": "MPACT-ORG" |
| }, |
| "id": "4b3c2668ba82622c4923d8c4c9c1baa69c7ddacf", |
| "message": "[mpact][benchmark] add regression benchmark to gh page", |
| "timestamp": "2024-06-28T20:55:50Z", |
| "url": "https://github.com/MPACT-ORG/mpact-compiler/pull/52/commits/4b3c2668ba82622c4923d8c4c9c1baa69c7ddacf" |
| }, |
| "date": 1719612311853, |
| "tool": "pytest", |
| "benches": [ |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", |
| "value": 6751.379871492692, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000009833798979422932", |
| "extra": "mean: 148.11786909257495 usec\nrounds: 1841" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", |
| "value": 33.86474121597165, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00026651849392417", |
| "extra": "mean: 29.529237906249506 msec\nrounds: 32" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", |
| "value": 5758.930502711753, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000044023333220703534", |
| "extra": "mean: 173.6433526206163 usec\nrounds: 1469" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", |
| "value": 5777.4834942014395, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000028187257621068578", |
| "extra": "mean: 173.0857389733174 usec\nrounds: 3582" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", |
| "value": 953214.3513921018, |
| "unit": "iter/sec", |
| "range": "stddev: 1.9841163030042895e-7", |
| "extra": "mean: 1.0490819809202108 usec\nrounds: 136166" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", |
| "value": 5.013373288554875, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0011602113209591113", |
| "extra": "mean: 199.46649539999726 msec\nrounds: 5" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", |
| "value": 12428.940664347065, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000004762825437678532", |
| "extra": "mean: 80.45737983676612 usec\nrounds: 3075" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", |
| "value": 21.516560306190904, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0009302272687718569", |
| "extra": "mean: 46.47583004762488 msec\nrounds: 21" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", |
| "value": 211.12310940718018, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0009240317536763013", |
| "extra": "mean: 4.73657290671748 msec\nrounds: 268" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", |
| "value": 186.48264407731656, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00008089085350596589", |
| "extra": "mean: 5.362429329269888 msec\nrounds: 164" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", |
| "value": 960641.0805626576, |
| "unit": "iter/sec", |
| "range": "stddev: 2.0111703200201038e-7", |
| "extra": "mean: 1.0409715139542954 usec\nrounds: 112020" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", |
| "value": 23.421250169062198, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0006716290731200427", |
| "extra": "mean: 42.69626910526444 msec\nrounds: 19" |
| } |
| ] |
| }, |
| { |
| "commit": { |
| "author": { |
| "name": "MPACT-ORG", |
| "username": "MPACT-ORG" |
| }, |
| "committer": { |
| "name": "MPACT-ORG", |
| "username": "MPACT-ORG" |
| }, |
| "id": "59fc5a073bebb037b2051bbe7c8a63dcf2ad82dc", |
| "message": "[mpact][benchmark] add regression benchmark to gh page", |
| "timestamp": "2024-06-28T20:55:50Z", |
| "url": "https://github.com/MPACT-ORG/mpact-compiler/pull/52/commits/59fc5a073bebb037b2051bbe7c8a63dcf2ad82dc" |
| }, |
| "date": 1719612806541, |
| "tool": "pytest", |
| "benches": [ |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", |
| "value": 5909.837407029962, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000008143470165037013", |
| "extra": "mean: 169.2093929370145 usec\nrounds: 1784" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", |
| "value": 34.25301200542631, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0003366944250263363", |
| "extra": "mean: 29.19451287500152 msec\nrounds: 32" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", |
| "value": 5882.077387952132, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00003686250866771616", |
| "extra": "mean: 170.0079638612429 usec\nrounds: 2352" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", |
| "value": 5877.128788845766, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000029053414735450336", |
| "extra": "mean: 170.1511122059985 usec\nrounds: 3654" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", |
| "value": 954326.869162598, |
| "unit": "iter/sec", |
| "range": "stddev: 2.122459628214424e-7", |
| "extra": "mean: 1.0478590012638742 usec\nrounds: 122760" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", |
| "value": 4.968651510614152, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0006829851952923742", |
| "extra": "mean: 201.26185099996974 msec\nrounds: 5" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", |
| "value": 12373.282557341212, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0000045490635324041385", |
| "extra": "mean: 80.81929717241351 usec\nrounds: 2830" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", |
| "value": 21.285310885533054, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0015616750096097195", |
| "extra": "mean: 46.980756136367646 msec\nrounds: 22" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", |
| "value": 203.30817935712298, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0007360380529483004", |
| "extra": "mean: 4.9186412625506835 msec\nrounds: 259" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", |
| "value": 188.23867331086552, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00010421745534155547", |
| "extra": "mean: 5.312404631903438 msec\nrounds: 163" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", |
| "value": 949008.389438308, |
| "unit": "iter/sec", |
| "range": "stddev: 1.9566124260579267e-7", |
| "extra": "mean: 1.053731464473009 usec\nrounds: 181786" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", |
| "value": 22.958249302316247, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0018635426701932051", |
| "extra": "mean: 43.55732821052303 msec\nrounds: 19" |
| } |
| ] |
| }, |
| { |
| "commit": { |
| "author": { |
| "name": "MPACT-ORG", |
| "username": "MPACT-ORG" |
| }, |
| "committer": { |
| "name": "MPACT-ORG", |
| "username": "MPACT-ORG" |
| }, |
| "id": "74f6291a82632c69528c283f0ad04fb7d5d65e63", |
| "message": "[mpact][benchmark] add regression benchmark to gh page", |
| "timestamp": "2024-06-28T20:55:50Z", |
| "url": "https://github.com/MPACT-ORG/mpact-compiler/pull/52/commits/74f6291a82632c69528c283f0ad04fb7d5d65e63" |
| }, |
| "date": 1719613791056, |
| "tool": "pytest", |
| "benches": [ |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", |
| "value": 5930.6630942960965, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0000069901342084117", |
| "extra": "mean: 168.61520947999307 usec\nrounds: 1962" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", |
| "value": 33.423283640985495, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00028784114480996003", |
| "extra": "mean: 29.919262593748996 msec\nrounds: 32" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", |
| "value": 5977.873004640322, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00003787120701808724", |
| "extra": "mean: 167.28358050158485 usec\nrounds: 2031" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", |
| "value": 5940.8924212876955, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00002705331572346874", |
| "extra": "mean: 168.32487934249596 usec\nrounds: 3713" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", |
| "value": 949863.7726585709, |
| "unit": "iter/sec", |
| "range": "stddev: 1.791786568219964e-7", |
| "extra": "mean: 1.0527825450181165 usec\nrounds: 148302" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", |
| "value": 5.032923153686416, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0007107591068022317", |
| "extra": "mean: 198.69168860000173 msec\nrounds: 5" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", |
| "value": 12511.47988615229, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0000035197064279101048", |
| "extra": "mean: 79.92659614205992 usec\nrounds: 3162" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", |
| "value": 21.745137400062706, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0007012204676885401", |
| "extra": "mean: 45.987292772733475 msec\nrounds: 22" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", |
| "value": 211.26695216957415, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0006688596361050787", |
| "extra": "mean: 4.733347973881625 msec\nrounds: 268" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", |
| "value": 189.42893402892727, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00008632489640648543", |
| "extra": "mean: 5.279024585796868 msec\nrounds: 169" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", |
| "value": 952227.2227348501, |
| "unit": "iter/sec", |
| "range": "stddev: 1.9643415939790627e-7", |
| "extra": "mean: 1.0501695142971692 usec\nrounds: 198060" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", |
| "value": 5.006633223271329, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0015691836550795323", |
| "extra": "mean: 199.73502259999805 msec\nrounds: 5" |
| } |
| ] |
| }, |
| { |
| "commit": { |
| "author": { |
| "email": "yinyingli@google.com", |
| "name": "Yinying Li", |
| "username": "yinying-lisa-li" |
| }, |
| "committer": { |
| "email": "noreply@github.com", |
| "name": "GitHub", |
| "username": "web-flow" |
| }, |
| "distinct": true, |
| "id": "aa8d896a77995ddf35fc50d9e06e5e121d047610", |
| "message": "[mpact][benchmark] set up regression benchmark for each commit with graphs (#58)", |
| "timestamp": "2024-07-02T15:28:13-04:00", |
| "tree_id": "c9c7befb6fd06fbbcd1209bda7e13fcb6950ed34", |
| "url": "https://github.com/MPACT-ORG/mpact-compiler/commit/aa8d896a77995ddf35fc50d9e06e5e121d047610" |
| }, |
| "date": 1719948752248, |
| "tool": "pytest", |
| "benches": [ |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", |
| "value": 5852.580803673562, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000008098366418064155", |
| "extra": "mean: 170.86479171245574 usec\nrounds: 1834" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", |
| "value": 34.43076839022791, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0006086274806415394", |
| "extra": "mean: 29.043789806440056 msec\nrounds: 31" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", |
| "value": 5802.733470460277, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00004454435239320872", |
| "extra": "mean: 172.33257482713216 usec\nrounds: 2018" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", |
| "value": 5671.297426386338, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00003430388155974315", |
| "extra": "mean: 176.32649547657113 usec\nrounds: 3316" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", |
| "value": 986318.2783261854, |
| "unit": "iter/sec", |
| "range": "stddev: 1.8088304088312995e-7", |
| "extra": "mean: 1.0138715077824907 usec\nrounds: 128140" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", |
| "value": 31.24834710280663, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00043359108481316527", |
| "extra": "mean: 32.00169265625519 msec\nrounds: 32" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", |
| "value": 12359.856575331278, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000004186711076895069", |
| "extra": "mean: 80.90708770811099 usec\nrounds: 3352" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", |
| "value": 19.952093711859064, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0009396451401827426", |
| "extra": "mean: 50.12005328571723 msec\nrounds: 21" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", |
| "value": 214.1812644000171, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0005696395902098518", |
| "extra": "mean: 4.668942462363763 msec\nrounds: 279" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", |
| "value": 187.5572338761418, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00010297395640425473", |
| "extra": "mean: 5.331705844309772 msec\nrounds: 167" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", |
| "value": 976700.9973067238, |
| "unit": "iter/sec", |
| "range": "stddev: 1.9521875916230403e-7", |
| "extra": "mean: 1.0238547956411674 usec\nrounds: 174795" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", |
| "value": 21.71808296320426, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00019562198685462142", |
| "extra": "mean: 46.04457961111229 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": "6dbb592d6176f77c3267b33c55ee6a611b191454", |
| "message": "[mpact][compiler] add training loop to models with simple test (#60)\n\n* [mpact][compiler] add training loop to models with simple test\r\n\r\nNote that although MPACT currently does not support autograd yet,\r\neventually we need to support this too. The current PR adds a very\r\nsimple training loop to the models, together with a simple neural\r\nnetwork that uses the training loop to learn classification of\r\nsimple sparse/dense tensors in a toy training set.\r\n\r\n* linter for darker (I tested with black?!)", |
| "timestamp": "2024-07-10T12:39:05-07:00", |
| "tree_id": "4d9b1fd9d8433e1ff5a993d2d4c7f06bf2a23e4a", |
| "url": "https://github.com/MPACT-ORG/mpact-compiler/commit/6dbb592d6176f77c3267b33c55ee6a611b191454" |
| }, |
| "date": 1720640716175, |
| "tool": "pytest", |
| "benches": [ |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", |
| "value": 6667.307948970807, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000009798278337849673", |
| "extra": "mean: 149.98557253596846 usec\nrounds: 1806" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", |
| "value": 32.68306054747386, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0002796872576339917", |
| "extra": "mean: 30.596889741933673 msec\nrounds: 31" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", |
| "value": 5081.997194601598, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00005778801050901175", |
| "extra": "mean: 196.77303266956937 usec\nrounds: 1255" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", |
| "value": 5379.457621769102, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00004383342499593898", |
| "extra": "mean: 185.89234646134037 usec\nrounds: 1752" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", |
| "value": 958591.6369178214, |
| "unit": "iter/sec", |
| "range": "stddev: 2.287062410905891e-7", |
| "extra": "mean: 1.0431970836041504 usec\nrounds: 108969" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", |
| "value": 31.187072550165258, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00040573286387233435", |
| "extra": "mean: 32.06456772726817 msec\nrounds: 33" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", |
| "value": 12337.291032437339, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000004143615251923706", |
| "extra": "mean: 81.05507095283635 usec\nrounds: 3002" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", |
| "value": 19.884055989984745, |
| "unit": "iter/sec", |
| "range": "stddev: 0.003329928946623915", |
| "extra": "mean: 50.2915502000036 msec\nrounds: 20" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", |
| "value": 211.98538008269045, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0005443079330878848", |
| "extra": "mean: 4.717306446368725 msec\nrounds: 289" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", |
| "value": 186.18716697079282, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00009675966320232993", |
| "extra": "mean: 5.37093944910215 msec\nrounds: 167" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", |
| "value": 975658.2476396016, |
| "unit": "iter/sec", |
| "range": "stddev: 2.2318166872127397e-7", |
| "extra": "mean: 1.0249490561057504 usec\nrounds: 104625" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", |
| "value": 20.124128779092676, |
| "unit": "iter/sec", |
| "range": "stddev: 0.001882759082698598", |
| "extra": "mean: 49.69159216665907 msec\nrounds: 18" |
| } |
| ] |
| }, |
| { |
| "commit": { |
| "author": { |
| "email": "yinyingli@google.com", |
| "name": "Yinying Li", |
| "username": "yinying-lisa-li" |
| }, |
| "committer": { |
| "email": "noreply@github.com", |
| "name": "GitHub", |
| "username": "web-flow" |
| }, |
| "distinct": true, |
| "id": "13c317b1f47932db8043fc742e4d6d785af90796", |
| "message": "[mpact][profiler] Add utils for profiling Python programs and torch ops (#59)", |
| "timestamp": "2024-07-10T16:27:00-04:00", |
| "tree_id": "f410eefea9c1a0c21f9fccca8d9f75cc3859c921", |
| "url": "https://github.com/MPACT-ORG/mpact-compiler/commit/13c317b1f47932db8043fc742e4d6d785af90796" |
| }, |
| "date": 1720643491530, |
| "tool": "pytest", |
| "benches": [ |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", |
| "value": 5882.730909058561, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000007809343889197068", |
| "extra": "mean: 169.9890774300323 usec\nrounds: 1821" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", |
| "value": 35.8260453730093, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0004098903292627477", |
| "extra": "mean: 27.912653757575548 msec\nrounds: 33" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", |
| "value": 5973.903357378356, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000029854003888673223", |
| "extra": "mean: 167.39474011826823 usec\nrounds: 2378" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", |
| "value": 5999.677258304794, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00002214935446779071", |
| "extra": "mean: 166.67563219601408 usec\nrounds: 3839" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", |
| "value": 950491.2651393854, |
| "unit": "iter/sec", |
| "range": "stddev: 2.0270360800891412e-7", |
| "extra": "mean: 1.0520875221860713 usec\nrounds: 138639" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", |
| "value": 33.43772340642905, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00032030927763137976", |
| "extra": "mean: 29.90634224241865 msec\nrounds: 33" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", |
| "value": 12432.257811160736, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000004436719488251673", |
| "extra": "mean: 80.43591238127928 usec\nrounds: 3150" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", |
| "value": 20.156932740504175, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0007951352667646836", |
| "extra": "mean: 49.610722666676295 msec\nrounds: 21" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", |
| "value": 206.25422280035204, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0005294618156626811", |
| "extra": "mean: 4.848385581748648 msec\nrounds: 263" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", |
| "value": 186.95337637042098, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00022424510447555433", |
| "extra": "mean: 5.348927200002235 msec\nrounds: 170" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", |
| "value": 960457.8018581292, |
| "unit": "iter/sec", |
| "range": "stddev: 2.443018316368644e-7", |
| "extra": "mean: 1.041170156632984 usec\nrounds: 120395" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", |
| "value": 21.143431877300007, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0023997890490843788", |
| "extra": "mean: 47.29601163156578 msec\nrounds: 19" |
| } |
| ] |
| }, |
| { |
| "commit": { |
| "author": { |
| "email": "ajcbik@google.com", |
| "name": "Aart Bik", |
| "username": "aartbik" |
| }, |
| "committer": { |
| "email": "noreply@github.com", |
| "name": "GitHub", |
| "username": "web-flow" |
| }, |
| "distinct": true, |
| "id": "466aed65269e4d9d17a4f4e5b737d3184d60e679", |
| "message": "[mpact][compiler] only import what you need in tests (#61)", |
| "timestamp": "2024-07-10T13:42:27-07:00", |
| "tree_id": "a96ce0dcd9a60503583756c90cf7c618d31f2fdf", |
| "url": "https://github.com/MPACT-ORG/mpact-compiler/commit/466aed65269e4d9d17a4f4e5b737d3184d60e679" |
| }, |
| "date": 1720644423077, |
| "tool": "pytest", |
| "benches": [ |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", |
| "value": 5892.849972887261, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000004959268193198534", |
| "extra": "mean: 169.69717617128472 usec\nrounds: 1771" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", |
| "value": 34.68541262005039, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00044738449261459793", |
| "extra": "mean: 28.830563757570403 msec\nrounds: 33" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", |
| "value": 5806.271338374302, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00004927253595346353", |
| "extra": "mean: 172.2275694197905 usec\nrounds: 2204" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", |
| "value": 5826.095561160015, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00003290846321017504", |
| "extra": "mean: 171.6415375447246 usec\nrounds: 3609" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", |
| "value": 966125.627784602, |
| "unit": "iter/sec", |
| "range": "stddev: 1.838781261933848e-7", |
| "extra": "mean: 1.035062078099589 usec\nrounds: 144447" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", |
| "value": 31.13639062389227, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00043796326986517375", |
| "extra": "mean: 32.11676048387759 msec\nrounds: 31" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", |
| "value": 12356.607210939963, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000003996111306778252", |
| "extra": "mean: 80.92836350051223 usec\nrounds: 3326" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", |
| "value": 19.92411439930273, |
| "unit": "iter/sec", |
| "range": "stddev: 0.001130257434495826", |
| "extra": "mean: 50.19043657142404 msec\nrounds: 21" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", |
| "value": 204.3538353123811, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0006957100557737918", |
| "extra": "mean: 4.893473119657243 msec\nrounds: 234" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", |
| "value": 188.81403914912207, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00020072138852544395", |
| "extra": "mean: 5.296216343373795 msec\nrounds: 166" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", |
| "value": 971022.1186577489, |
| "unit": "iter/sec", |
| "range": "stddev: 1.9237787835734797e-7", |
| "extra": "mean: 1.0298426583549996 usec\nrounds: 196890" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", |
| "value": 19.598736962010374, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0033167102573383195", |
| "extra": "mean: 51.02369616666477 msec\nrounds: 18" |
| } |
| ] |
| }, |
| { |
| "commit": { |
| "author": { |
| "email": "yinyingli@google.com", |
| "name": "Yinying Li", |
| "username": "yinying-lisa-li" |
| }, |
| "committer": { |
| "email": "noreply@github.com", |
| "name": "GitHub", |
| "username": "web-flow" |
| }, |
| "distinct": true, |
| "id": "2f8cf9d04b426ca4895c17f39dd1a1df4632af9a", |
| "message": "Update README.md (#62)\n\nadd info for pip install and performance tracking.", |
| "timestamp": "2024-07-10T17:11:30-04:00", |
| "tree_id": "673633ab88ea05b611089784a38edb7cb96d6f04", |
| "url": "https://github.com/MPACT-ORG/mpact-compiler/commit/2f8cf9d04b426ca4895c17f39dd1a1df4632af9a" |
| }, |
| "date": 1720646184454, |
| "tool": "pytest", |
| "benches": [ |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_dense", |
| "value": 5567.115407135617, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000007841718427423878", |
| "extra": "mean: 179.6262385217048 usec\nrounds: 1786" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_dense", |
| "value": 33.309292420925736, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00028773048371934126", |
| "extra": "mean: 30.021652437497437 msec\nrounds: 32" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_dense", |
| "value": 5617.572222361392, |
| "unit": "iter/sec", |
| "range": "stddev: 0.000046885770008036976", |
| "extra": "mean: 178.012842633226 usec\nrounds: 1595" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_dense", |
| "value": 5605.020685394918, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00003302817260819787", |
| "extra": "mean: 178.41147359290113 usec\nrounds: 2897" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_dense", |
| "value": 990990.6666835662, |
| "unit": "iter/sec", |
| "range": "stddev: 1.7844414339485182e-7", |
| "extra": "mean: 1.0090912393217428 usec\nrounds: 131840" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_dense", |
| "value": 31.53526822110171, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0015008376083111511", |
| "extra": "mean: 31.710527812503386 msec\nrounds: 32" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mv_sparse", |
| "value": 12410.86842730023, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0000048379110808968955", |
| "extra": "mean: 80.57453882923265 usec\nrounds: 3348" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mm_sparse", |
| "value": 20.01479085419484, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0006618735765434783", |
| "extra": "mean: 49.963050190475165 msec\nrounds: 21" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_add_sparse", |
| "value": 197.71393986199735, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0007309446772089394", |
| "extra": "mean: 5.057812315600971 msec\nrounds: 282" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_mul_sparse", |
| "value": 183.32305573422838, |
| "unit": "iter/sec", |
| "range": "stddev: 0.00023267992206215093", |
| "extra": "mean: 5.4548512514964 msec\nrounds: 167" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_nop_sparse", |
| "value": 986761.515348736, |
| "unit": "iter/sec", |
| "range": "stddev: 2.1567528682233168e-7", |
| "extra": "mean: 1.0134160933977905 usec\nrounds: 137099" |
| }, |
| { |
| "name": "benchmark/python/benchmarks/regression_benchmark.py::test_sddmm_sparse", |
| "value": 19.455210605295285, |
| "unit": "iter/sec", |
| "range": "stddev: 0.0022573877507840855", |
| "extra": "mean: 51.4001117894772 msec\nrounds: 19" |
| } |
| ] |
| } |
| ] |
| } |
| } |
