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Add sudo to cpufreq-set command in ci-plutus-benchmark.sh (#6733)
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@zeme-wana
zeme-wana authored Dec 5, 2024
1 parent 34e9bf2 commit e9b2583
Showing 1 changed file with 2 additions and 2 deletions.
4 changes: 2 additions & 2 deletions scripts/ci-plutus-benchmark.sh
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Expand Up @@ -70,8 +70,8 @@ if [[ -z $(which taskset) ]]; then
else
echo "[ci-plutus-benchmark]: Setting CPU $CAPABILITY_NUM frequency governor to 'userspace' and frequency to 4.21GHz"
# This makes the benchmark reliable on a single core and addresses the issue of large variance in the results.
cpufreq-set --cpu $CAPABILITY_NUM --governor userspace
cpufreq-set --cpu $CAPABILITY_NUM --related --freq 4.21GHz
sudo cpufreq-set --cpu $CAPABILITY_NUM --governor userspace
sudo cpufreq-set --cpu $CAPABILITY_NUM --related --freq 4.21GHz
TASKSET="taskset -c $CAPABILITY_NUM"
fi

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⚠️ Performance Alert ⚠️

Possible performance regression was detected for benchmark 'Plutus Benchmarks'.
Benchmark result of this commit is worse than the previous benchmark result exceeding threshold 1.05.

Benchmark suite Current: e9b2583 Previous: 34e9bf2 Ratio
validation-future-settle-early-1 234.7 μs 211.3 μs 1.11
validation-future-settle-early-2 487.5 μs 446.1 μs 1.09
validation-game-sm-success_1-3 589.9 μs 533 μs 1.11
validation-game-sm-success_1-4 237.8 μs 212.2 μs 1.12
validation-game-sm-success_2-1 364 μs 329.4 μs 1.11
validation-game-sm-success_2-2 201.9 μs 182.4 μs 1.11
validation-game-sm-success_2-3 589.5 μs 530.7 μs 1.11
validation-game-sm-success_2-4 236.5 μs 212 μs 1.12
validation-game-sm-success_2-5 590.1 μs 530.3 μs 1.11
validation-game-sm-success_2-6 235.4 μs 211.4 μs 1.11
validation-multisig-sm-1 371 μs 333.2 μs 1.11
validation-multisig-sm-2 361.5 μs 325.6 μs 1.11
validation-multisig-sm-3 369.2 μs 333.7 μs 1.11
validation-decode-auction_1-3 576.5 μs 532.3 μs 1.08
validation-decode-auction_1-4 207 μs 192.1 μs 1.08
validation-decode-auction_2-1 210.5 μs 192.2 μs 1.10
validation-decode-auction_2-2 577.1 μs 526 μs 1.10
validation-decode-auction_2-3 576.5 μs 523.7 μs 1.10
validation-decode-auction_2-4 576.6 μs 525.2 μs 1.10
validation-decode-auction_2-5 201.5 μs 191.9 μs 1.05
validation-decode-escrow-redeem_2-1 332.3 μs 310.6 μs 1.07
validation-decode-escrow-redeem_2-2 343.4 μs 310.9 μs 1.10
validation-decode-escrow-redeem_2-3 341.4 μs 310.7 μs 1.10
validation-decode-escrow-refund-1 334.2 μs 310.3 μs 1.08
validation-decode-future-increase-margin-1 252 μs 228.3 μs 1.10
validation-decode-future-increase-margin-2 343 μs 309.8 μs 1.11
validation-decode-future-increase-margin-3 343.8 μs 310.6 μs 1.11
validation-decode-future-increase-margin-4 729.6 μs 659.5 μs 1.11
validation-decode-future-increase-margin-5 729.6 μs 658.6 μs 1.11
validation-decode-future-pay-out-1 251.8 μs 227.9 μs 1.10
validation-decode-future-pay-out-2 341.1 μs 311 μs 1.10
validation-decode-future-pay-out-3 343.5 μs 310.6 μs 1.11
validation-decode-future-pay-out-4 731.4 μs 660.7 μs 1.11
validation-decode-future-settle-early-1 251.4 μs 228.7 μs 1.10
validation-decode-future-settle-early-2 344.1 μs 311.4 μs 1.11
validation-decode-game-sm-success_2-3 559.5 μs 506.4 μs 1.10
validation-decode-game-sm-success_2-4 177.7 μs 162.2 μs 1.10
validation-decode-game-sm-success_2-5 560.7 μs 507.2 μs 1.11
validation-decode-game-sm-success_2-6 177.8 μs 161.6 μs 1.10
validation-decode-multisig-sm-1 621.7 μs 562.5 μs 1.11
validation-decode-multisig-sm-2 619.5 μs 562.8 μs 1.10
validation-decode-multisig-sm-3 622.4 μs 570.6 μs 1.09
nofib-clausify/formula1 2819 μs 2651 μs 1.06
marlowe-semantics/0101020201010201010200010102000201000201010102000102010201010000 299.1 μs 274.1 μs 1.09
marlowe-semantics/0101080808040600020306010000000302050807010208060100070207080202 757.2 μs 687.3 μs 1.10
marlowe-semantics/0104010200020000040103020102020004040300030304040400010301040303 772.6 μs 706.7 μs 1.09
marlowe-semantics/04000f0b04051006000e060f09080d0b090d0104050a0b0f0506070f0a070008 746.5 μs 677.1 μs 1.10
marlowe-semantics/0543a00ba1f63076c1db6bf94c6ff13ae7d266dd7544678743890b0e8e1add63 1055 μs 963.3 μs 1.10
marlowe-semantics/0705030002040601010206030604080208020207000101060706050502040301 1039 μs 942 μs 1.10
marlowe-semantics/07070c070510030509010e050d00040907050e0a0d06030f1006030701020607 1004.9999999999999 μs 911.8 μs 1.10
marlowe-semantics/0bcfd9487614104ec48de2ea0b2c0979866a95115748c026f9ec129384c262c4 1122 μs 1020 μs 1.10
marlowe-semantics/0be82588e4e4bf2ef428d2f44b7687bbb703031d8de696d90ec789e70d6bc1d8 1335 μs 1202 μs 1.11
marlowe-semantics/0f1d0110001b121d051e15140c0c05141d151c1f1d201c040f10091b020a0e1a 471.2 μs 422.8 μs 1.11
marlowe-semantics/119fbea4164e2bf21d2b53aa6c2c4e79414fe55e4096f5ce2e804735a7fbaf91 762.8 μs 692.2 μs 1.10
marlowe-semantics/12910f24d994d451ff379b12c9d1ecdb9239c9b87e5d7bea570087ec506935d5 493.4 μs 445.5 μs 1.11
marlowe-semantics/18cefc240debc0fcab14efdd451adfd02793093efe7bc76d6322aed6ddb582ad 744.9 μs 671.9 μs 1.11
marlowe-semantics/1a2f2540121f09321216090b2b1f211e3f020c2c133a1a3c3f3c232a26153a04 307.3 μs 277.3 μs 1.11
marlowe-semantics/1a573aed5c46d637919ccb5548dfc22a55c9fc38298d567d15ee9f2eea69d89e 883.3 μs 792.9 μs 1.11
marlowe-semantics/1d56060c3b271226064c672a282663643b1b0823471c67737f0b076870331260 786.2 μs 706.5 μs 1.11
marlowe-semantics/1d6e3c137149a440f35e0efc685b16bfb8052ebcf66ec4ad77e51c11501381c7 307 μs 275 μs 1.12
marlowe-semantics/1f0f02191604101e1f201016171604060d010d1d1c150e110a110e1006160a0d 1070 μs 950.5 μs 1.13
marlowe-semantics/202d273721330b31193405101e0637202e2a0f1140211c3e3f171e26312b0220 6340 μs 5720 μs 1.11
marlowe-semantics/21953bf8798b28df60cb459db24843fb46782b19ba72dc4951941fb4c20d2263 352.5 μs 316.5 μs 1.11
marlowe-semantics/238b21364ab5bdae3ddb514d7001c8feba128b4ddcf426852b441f9a9d02c882 300.1 μs 270.3 μs 1.11
marlowe-semantics/26e24ee631a6d927ea4fb4fac530cfd82ff7636986014de2d2aaa460ddde0bc3 557.5 μs 501 μs 1.11
marlowe-semantics/2797d7ac77c1b6aff8e42cf9a47fa86b1e60f22719a996871ad412cbe4de78b5 1996 μs 1770 μs 1.13
marlowe-semantics/28fdce478e179db0e38fb5f3f4105e940ece450b9ce8a0f42a6e313b752e6f2c 960.2 μs 859 μs 1.12
marlowe-semantics/2cb21612178a2d9336b59d06cbf80488577463d209a453048a66c6eee624a695 783.5 μs 706.9 μs 1.11
marlowe-semantics/2f58c9d884813042bce9cf7c66048767dff166785e8b5183c8139db2aa7312d1 756 μs 670.8 μs 1.13
marlowe-semantics/30aa34dfbe89e0c43f569929a96c0d2b74c321d13fec0375606325eee9a34a6a 1118 μs 1010 μs 1.11
marlowe-semantics/322acde099bc34a929182d5b894214fc87ec88446e2d10625119a9d17fa3ec3d 307.2 μs 275.1 μs 1.12
marlowe-semantics/331e4a1bb30f28d7073c54f9a13c10ae19e2e396c299a0ce101ee6bf4b2020db 465.6 μs 416.9 μs 1.12
marlowe-semantics/33c3efd79d9234a78262b52bc6bbf8124cb321a467dedb278328215167eca455 617.1 μs 552 μs 1.12
marlowe-semantics/383683bfcecdab0f4df507f59631c702bd11a81ca3841f47f37633e8aacbb5de 757.2 μs 679.5 μs 1.11
marlowe-semantics/3bb75b2e53eb13f718eacd3263ab4535f9137fabffc9de499a0de7cabb335479 300.1 μs 269.1 μs 1.12
marlowe-semantics/3db496e6cd39a8b888a89d0de07dace4397878958cab3b9d9353978b08c36d8a 844.7 μs 754.8 μs 1.12
marlowe-semantics/44a9e339fa25948b48637fe7e10dcfc6d1256319a7b5ce4202cb54dfef8e37e7 299.9 μs 269.7 μs 1.11
marlowe-semantics/4c3efd13b6c69112a8a888372d56c86e60c232125976f29b1c3e21d9f537845c 1006 μs 902 μs 1.12
marlowe-semantics/4d7adf91bfc93cebe95a7e054ec17cfbb912b32bd8aecb48a228b50e02b055c8 692.5 μs 621.6 μs 1.11
marlowe-semantics/4f9e8d361b85e62db2350dd3ae77463540e7af0d28e1eb68faeecc45f4655f57 395.9 μs 355.8 μs 1.11
marlowe-semantics/52df7c8dfaa5f801cd837faa65f2fd333665fff00a555ce8c55e36ddc003007a 370.5 μs 332.8 μs 1.11
marlowe-semantics/53ed4db7ab33d6f907eec91a861d1188269be5ae1892d07ee71161bfb55a7cb7 374.8 μs 337.9 μs 1.11
marlowe-semantics/55dfe42688ad683b638df1fa7700219f00f53b335a85a2825502ab1e0687197e 299.5 μs 271.3 μs 1.10
marlowe-semantics/56333d4e413dbf1a665463bf68067f63c118f38f7539b7ba7167d577c0c8b8ce 754.2 μs 682.8 μs 1.10
marlowe-semantics/57728d8b19b0e06412786f3dfed9e1894cd0ad1d2bc2bd497ec0ecb68f989d2b 299 μs 269.2 μs 1.11
marlowe-semantics/5abae75af26f45658beccbe48f7c88e74efdfc0b8409ba1e98f95fa5b6caf999 485.4 μs 436.6 μs 1.11
marlowe-semantics/9fabc4fc3440cdb776b28c9bb1dd49c9a5b1605fe1490aa3f4f64a3fa8881b25 1060 μs 972.8 μs 1.09
marlowe-semantics/a85173a832db3ea944fafc406dfe3fa3235254897d6d1d0e21bc380147687bd5 374.7 μs 339.5 μs 1.10
marlowe-semantics/a9a853b6d083551f4ed2995551af287880ef42aee239a2d9bc5314d127cce592 526.8 μs 476.6 μs 1.11
marlowe-semantics/acb9c83c2b78dabef8674319ad69ba54912cd9997bdf2d8b2998c6bfeef3b122 643.7 μs 580.8 μs 1.11
marlowe-semantics/acce04815e8fd51be93322888250060da173eccf3df3a605bd6bc6a456cde871 290.8 μs 263.2 μs 1.10
marlowe-semantics/ad6db94ed69b7161c7604568f44358e1cc11e81fea90e41afebd669e51bb60c8 581.9 μs 524.9 μs 1.11
marlowe-semantics/b21a4df3b0266ad3481a26d3e3d848aad2fcde89510b29cccce81971e38e0835 1322 μs 1195 μs 1.11
marlowe-semantics/b50170cea48ee84b80558c02b15c6df52faf884e504d2c410ad63ba46d8ca35c 743.2 μs 671.7 μs 1.11
marlowe-semantics/bb5345bfbbc460af84e784b900ec270df1948bb1d1e29eacecd022eeb168b315 941.7 μs 854.7 μs 1.10
marlowe-semantics/c4bb185380df6e9b66fc1ee0564f09a8d1253a51a0c0c7890f2214df9ac19274 735.4 μs 662.2 μs 1.11
marlowe-semantics/c9efcb705ee057791f7c18a1de79c49f6e40ba143ce0579f1602fd780cabf153 800.4 μs 723.2 μs 1.11
marlowe-semantics/ccab11ce1a8774135d0e3c9e635631b68af9e276b5dabc66ff669d5650d0be1c 1063 μs 949.8 μs 1.12
marlowe-semantics/cdb9d5c233b288a5a9dcfbd8d5c1831a0bb46eec7a26fa31b80ae69d44805efc 855.8 μs 772.9 μs 1.11
marlowe-semantics/ced1ea04649e093a501e43f8568ac3e6b37cd3eccec8cac9c70a4857b88a5eb8 817.5 μs 738.1 μs 1.11
marlowe-semantics/cf542b7df466b228ca2197c2aaa89238a8122f3330fe5b77b3222f570395d9f5 492.8 μs 442.2 μs 1.11
marlowe-semantics/d1ab832dfab25688f8845bec9387e46ee3f00ba5822197ade7dd540489ec5e95 39090 μs 36180 μs 1.08
marlowe-semantics/d1c03759810747b7cab38c4296593b38567e11195d161b5bb0a2b58f89b2c65a 990.7 μs 904.2 μs 1.10

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CC: @IntersectMBO/plutus-core

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