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Update plutus-tx-template.yml (#6489)
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zliu41 authored Sep 17, 2024
1 parent 979c895 commit e2016d5
Showing 1 changed file with 14 additions and 14 deletions.
28 changes: 14 additions & 14 deletions .github/workflows/plutus-tx-template.yml
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
# This workflows ensures that the plutus-tx-template repository stays working
# even if there are changes in plutus. It checks out both the current commit of
# plutus and the master branch of plutus-tx-template. Then, it creates a
# cabal.project.local for plutus-tx-template that adjusts the plutus version.
# This workflows ensures that the plutus-tx-template repository stays working
# even if there are changes in plutus. It checks out both the current commit of
# plutus and the master branch of plutus-tx-template. Then, it creates a
# cabal.project.local for plutus-tx-template that adjusts the plutus version.
# Finally, it double-checks that everything still builds correctly using cabal
# inside the devx shell.

Expand All @@ -17,40 +17,40 @@ jobs:
steps:
- name: Checkout plutus-tx-template Repo
uses: actions/checkout@main
with:
with:
repository: IntersectMBO/plutus-tx-template
path: plutus-tx-template
path: plutus-tx-template

- name: Checkout plutus Repo
uses: actions/checkout@main
with:
with:
path: plutus-tx-template/plutus

- name: Overwrite cabal.project.local
uses: DamianReeves/write-file-action@master
with:
path: plutus-tx-template/cabal.project.local
write-mode: overwrite
contents: |
packages:
packages:
plutus/plutus-tx
plutus/plutus-tx-plugin
plutus/plutus-core
plutus/plutus-ledger-api
plutus/prettyprinter-configurable
allow-newer:
plutus-tx
, plutus-tx-plugin
, plutus-core
, plutus-ledger-api
, plutus-ledger-api
, prettyprinter-configurable
- name: Build Project With Docker
run: |
cd plutus-tx-template
cd plutus-tx-template
docker run \
-v ./.:/workspaces/plutus-tx-template \
-w /workspaces/plutus-tx-template \
-i ghcr.io/input-output-hk/devx-devcontainer:x86_64-linux.ghc96-iog \
bash -ic "cabal update && cabal run plutus-tx-template && test -e validator.uplc"
bash -ic "cabal update && cabal build all"

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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: e2016d5 Previous: 979c895 Ratio
validation-auction_2-3 1165 μs 825.1 μs 1.41
validation-auction_2-4 884.1 μs 654.7 μs 1.35
validation-escrow-refund-1 216.7 μs 185.8 μs 1.17
validation-future-increase-margin-2 747.9 μs 597 μs 1.25
validation-future-increase-margin-3 751.1 μs 640.8 μs 1.17
validation-uniswap-4 462.1 μs 327.5 μs 1.41
validation-uniswap-5 1238 μs 1138 μs 1.09
validation-uniswap-6 444.9 μs 367.5 μs 1.21
validation-vesting-1 485.5 μs 454.6 μs 1.07
validation-decode-future-pay-out-1 249.8 μs 233.5 μs 1.07
validation-decode-future-settle-early-1 330.7 μs 234.8 μs 1.41
validation-decode-future-settle-early-2 454.2 μs 320.6 μs 1.42
validation-decode-future-settle-early-3 454.3 μs 320.6 μs 1.42
validation-decode-future-settle-early-4 966 μs 681.2 μs 1.42
validation-decode-game-sm-success_1-1 738.7 μs 522 μs 1.42
nofib-knights/8x8 115000 μs 80890 μs 1.42
nofib-primetest/05digits 14620 μs 13520 μs 1.08
nofib-queens4x4/bjbt2 8615 μs 8186.999999999999 μs 1.05
nofib-queens4x4/fc 19350 μs 15670 μs 1.23
nofib-queens5x5/bt 102600 μs 72060 μs 1.42
nofib-queens5x5/bm 106400 μs 76230 μs 1.40
nofib-queens5x5/bjbt1 117800 μs 97240 μs 1.21
nofib-queens5x5/bjbt2 116400 μs 90020 μs 1.29
marlowe-semantics/0000020002010200020101020201000100010001020101020201010000020102 469.2 μs 321.1 μs 1.46
marlowe-semantics/0001000101000000010101000001000001010101010100000001000001010000 642.8 μs 439.3 μs 1.46
marlowe-semantics/0003040402030103010203030303000200000104030002040304020400000102 1492 μs 1031 μs 1.45
marlowe-semantics/004025fd712d6c325ffa12c16d157064192992faf62e0b991d7310a2f91666b8 1186 μs 809.1 μs 1.47
marlowe-semantics/0101010001010101010101000100010100000001010000010001000001000101 1310 μs 906.6 μs 1.44
marlowe-semantics/0101020201010201010200010102000201000201010102000102010201010000 442.1 μs 302.8 μs 1.46
marlowe-semantics/0101080808040600020306010000000302050807010208060100070207080202 1117 μs 767.7 μs 1.45
marlowe-semantics/0104010200020000040103020102020004040300030304040400010301040303 1137 μs 774.8 μs 1.47
marlowe-semantics/04000f0b04051006000e060f09080d0b090d0104050a0b0f0506070f0a070008 1084 μs 743.1 μs 1.46
marlowe-semantics/0543a00ba1f63076c1db6bf94c6ff13ae7d266dd7544678743890b0e8e1add63 1540 μs 1042 μs 1.48
marlowe-semantics/0705030002040601010206030604080208020207000101060706050502040301 1494 μs 1008 μs 1.48
marlowe-semantics/07070c070510030509010e050d00040907050e0a0d06030f1006030701020607 1492 μs 1024 μs 1.46
marlowe-semantics/0bcfd9487614104ec48de2ea0b2c0979866a95115748c026f9ec129384c262c4 1653 μs 1128 μs 1.47
marlowe-semantics/0be82588e4e4bf2ef428d2f44b7687bbb703031d8de696d90ec789e70d6bc1d8 1989 μs 1363 μs 1.46
marlowe-semantics/0f1d0110001b121d051e15140c0c05141d151c1f1d201c040f10091b020a0e1a 686.9 μs 469.8 μs 1.46
marlowe-semantics/119fbea4164e2bf21d2b53aa6c2c4e79414fe55e4096f5ce2e804735a7fbaf91 1113 μs 857.1 μs 1.30
marlowe-semantics/12910f24d994d451ff379b12c9d1ecdb9239c9b87e5d7bea570087ec506935d5 730.7 μs 497.8 μs 1.47
marlowe-semantics/18cefc240debc0fcab14efdd451adfd02793093efe7bc76d6322aed6ddb582ad 1097 μs 750.5 μs 1.46
marlowe-semantics/1a2f2540121f09321216090b2b1f211e3f020c2c133a1a3c3f3c232a26153a04 447.8 μs 307 μs 1.46
marlowe-semantics/1a573aed5c46d637919ccb5548dfc22a55c9fc38298d567d15ee9f2eea69d89e 1305 μs 896.4 μs 1.46
marlowe-semantics/1d56060c3b271226064c672a282663643b1b0823471c67737f0b076870331260 1134 μs 776.7 μs 1.46
marlowe-semantics/1d6e3c137149a440f35e0efc685b16bfb8052ebcf66ec4ad77e51c11501381c7 448.7 μs 305 μs 1.47
marlowe-semantics/1f0f02191604101e1f201016171604060d010d1d1c150e110a110e1006160a0d 1384 μs 954.2 μs 1.45
marlowe-semantics/202d273721330b31193405101e0637202e2a0f1140211c3e3f171e26312b0220 7973 μs 5742 μs 1.39
marlowe-semantics/21953bf8798b28df60cb459db24843fb46782b19ba72dc4951941fb4c20d2263 520.7 μs 356.4 μs 1.46
marlowe-semantics/2cb21612178a2d9336b59d06cbf80488577463d209a453048a66c6eee624a695 836.3 μs 787.1 μs 1.06
marlowe-semantics/2f58c9d884813042bce9cf7c66048767dff166785e8b5183c8139db2aa7312d1 1006.9999999999999 μs 759.2 μs 1.33
marlowe-semantics/322acde099bc34a929182d5b894214fc87ec88446e2d10625119a9d17fa3ec3d 446.6 μs 305.1 μs 1.46
marlowe-semantics/331e4a1bb30f28d7073c54f9a13c10ae19e2e396c299a0ce101ee6bf4b2020db 680.2 μs 465.5 μs 1.46
marlowe-semantics/33c3efd79d9234a78262b52bc6bbf8124cb321a467dedb278328215167eca455 911.6 μs 624.7 μs 1.46
marlowe-semantics/383683bfcecdab0f4df507f59631c702bd11a81ca3841f47f37633e8aacbb5de 1108 μs 760.9 μs 1.46
marlowe-semantics/3bb75b2e53eb13f718eacd3263ab4535f9137fabffc9de499a0de7cabb335479 440.8 μs 300.1 μs 1.47
marlowe-semantics/3db496e6cd39a8b888a89d0de07dace4397878958cab3b9d9353978b08c36d8a 1226 μs 832.2 μs 1.47
marlowe-semantics/44a9e339fa25948b48637fe7e10dcfc6d1256319a7b5ce4202cb54dfef8e37e7 440.4 μs 300.3 μs 1.47
marlowe-semantics/4c3efd13b6c69112a8a888372d56c86e60c232125976f29b1c3e21d9f537845c 1505 μs 1027 μs 1.47
marlowe-semantics/4d7adf91bfc93cebe95a7e054ec17cfbb912b32bd8aecb48a228b50e02b055c8 1020.9999999999999 μs 697.4 μs 1.46
marlowe-semantics/4f9e8d361b85e62db2350dd3ae77463540e7af0d28e1eb68faeecc45f4655f57 581.3 μs 400.2 μs 1.45
marlowe-semantics/52df7c8dfaa5f801cd837faa65f2fd333665fff00a555ce8c55e36ddc003007a 534.3 μs 363.6 μs 1.47
marlowe-semantics/53ed4db7ab33d6f907eec91a861d1188269be5ae1892d07ee71161bfb55a7cb7 549.4 μs 373 μs 1.47
marlowe-semantics/55dfe42688ad683b638df1fa7700219f00f53b335a85a2825502ab1e0687197e 444.2 μs 301.3 μs 1.47
marlowe-semantics/56333d4e413dbf1a665463bf68067f63c118f38f7539b7ba7167d577c0c8b8ce 1120 μs 771 μs 1.45
marlowe-semantics/57728d8b19b0e06412786f3dfed9e1894cd0ad1d2bc2bd497ec0ecb68f989d2b 442.6 μs 301.8 μs 1.47
marlowe-semantics/5abae75af26f45658beccbe48f7c88e74efdfc0b8409ba1e98f95fa5b6caf999 721 μs 493.2 μs 1.46
marlowe-semantics/5d0a88250f13c49c20e146819357a808911c878a0e0a7d6f7fe1d4a619e06112 1536 μs 1045 μs 1.47
marlowe-semantics/5e274e0f593511543d41570a4b03646c1d7539062b5728182e073e5760561a66 1478 μs 1016 μs 1.45
marlowe-semantics/5e2c68ac9f62580d626636679679b97109109df7ac1a8ce86d3e43dfb5e4f6bc 757.4 μs 518.2 μs 1.46
marlowe-semantics/5f130d19918807b60eab4c03119d67878fb6c6712c28c54f5a25792049294acc 449 μs 306.7 μs 1.46
marlowe-semantics/5f306b4b24ff2b39dab6cdc9ac6ca9bb442c1dc6f4e7e412eeb5a3ced42fb642 1106 μs 757.4 μs 1.46
marlowe-semantics/5f3d46c57a56cef6764f96c9de9677ac6e494dd7a4e368d1c8dd9c1f7a4309a5 717.8 μs 491.4 μs 1.46
marlowe-semantics/64c3d5b43f005855ffc4d0950a02fd159aa1575294ea39061b81a194ebb9eaae 973.2 μs 664.7 μs 1.46
marlowe-semantics/65bc4b69b46d18fdff0fadbf00dd5ec2b3e03805fac9d5fb4ff2d3066e53fc7e 3277 μs 2294 μs 1.43
marlowe-semantics/66af9e473d75e3f464971f6879cc0f2ef84bafcb38fbfa1dbc31ac2053628a38 1830 μs 1236 μs 1.48
marlowe-semantics/675d63836cad11b547d1b4cddd498f04c919d4342612accf40913f9ae9419fac 1521 μs 1036 μs 1.47
marlowe-semantics/67ba5a9a0245ee3aff4f34852b9889b8c810fccd3dce2a23910bddd35c503b71 7870 μs 5691 μs 1.38
marlowe-semantics/6d88f7294dd2b5ce02c3dc609bc7715bd508009738401d264bf9b3eb7c6f49c1 718.6 μs 490.4 μs 1.47
marlowe-semantics/70f65b21b77ddb451f3df9d9fb403ced3d10e1e953867cc4900cc25e5b9dec47 1155 μs 783.8 μs 1.47
marlowe-semantics/71965c9ccae31f1ffc1d85aa20a356d4ed97a420954018d8301ec4f9783be0d7 696.8 μs 472.9 μs 1.47
marlowe-semantics/74c67f2f182b9a0a66c62b95d6fac5ace3f7e71ea3abfc52ffbe3ecb93436ea2 1164 μs 793.1 μs 1.47
marlowe-semantics/7529b206a78becb793da74b78c04d9d33a2540a1abd79718e681228f4057403a 1169 μs 798.5 μs 1.46
marlowe-semantics/75a8bb183688bce447e00f435a144c835435e40a5defc6f3b9be68b70b4a3db6 1020 μs 692.2 μs 1.47
marlowe-semantics/7a758e17486d1a30462c32a5d5309bd1e98322a9dcbe277c143ed3aede9d265f 755.9 μs 516 μs 1.46
marlowe-semantics/7cbc5644b745f4ea635aca42cce5e4a4b9d2e61afdb3ac18128e1688c07071ba 690.3 μs 471.4 μs 1.46
marlowe-semantics/82213dfdb6a812b40446438767c61a388d2c0cfd0cbf7fd4a372b0dc59fa17e1 1886 μs 1277 μs 1.48
marlowe-semantics/8c7fdc3da6822b5112074380003524f50fb3a1ce6db4e501df1086773c6c0201 1683 μs 1163 μs 1.45
marlowe-semantics/8d9ae67656a2911ab15a8e5301c960c69aa2517055197aff6b60a87ff718d66c 521.1 μs 354.6 μs 1.47
marlowe-semantics/96e1a2fa3ceb9a402f2a5841a0b645f87b4e8e75beb636692478ec39f74ee221 447.9 μs 307 μs 1.46
marlowe-semantics/9fabc4fc3440cdb776b28c9bb1dd49c9a5b1605fe1490aa3f4f64a3fa8881b25 1545 μs 1051 μs 1.47
marlowe-semantics/a85173a832db3ea944fafc406dfe3fa3235254897d6d1d0e21bc380147687bd5 547 μs 372.8 μs 1.47
marlowe-semantics/a9a853b6d083551f4ed2995551af287880ef42aee239a2d9bc5314d127cce592 756 μs 513.2 μs 1.47
marlowe-semantics/acb9c83c2b78dabef8674319ad69ba54912cd9997bdf2d8b2998c6bfeef3b122 951.6 μs 654.2 μs 1.45
marlowe-semantics/acce04815e8fd51be93322888250060da173eccf3df3a605bd6bc6a456cde871 426.7 μs 285.2 μs 1.50
marlowe-semantics/ad6db94ed69b7161c7604568f44358e1cc11e81fea90e41afebd669e51bb60c8 856.2 μs 585.7 μs 1.46
marlowe-semantics/b21a4df3b0266ad3481a26d3e3d848aad2fcde89510b29cccce81971e38e0835 1969 μs 1364 μs 1.44
marlowe-semantics/b50170cea48ee84b80558c02b15c6df52faf884e504d2c410ad63ba46d8ca35c 1096 μs 752.8 μs 1.46
marlowe-semantics/bb5345bfbbc460af84e784b900ec270df1948bb1d1e29eacecd022eeb168b315 1390 μs 953.5 μs 1.46
marlowe-semantics/c4bb185380df6e9b66fc1ee0564f09a8d1253a51a0c0c7890f2214df9ac19274 1085 μs 738.7 μs 1.47
marlowe-semantics/c9efcb705ee057791f7c18a1de79c49f6e40ba143ce0579f1602fd780cabf153 1198 μs 816.6 μs 1.47
marlowe-semantics/ccab11ce1a8774135d0e3c9e635631b68af9e276b5dabc66ff669d5650d0be1c 1393 μs 983.2 μs 1.42
marlowe-semantics/cdb9d5c233b288a5a9dcfbd8d5c1831a0bb46eec7a26fa31b80ae69d44805efc 1283 μs 878.7 μs 1.46
marlowe-semantics/ced1ea04649e093a501e43f8568ac3e6b37cd3eccec8cac9c70a4857b88a5eb8 1228 μs 844.4 μs 1.45
marlowe-semantics/cf542b7df466b228ca2197c2aaa89238a8122f3330fe5b77b3222f570395d9f5 720.2 μs 490.4 μs 1.47
marlowe-semantics/d1ab832dfab25688f8845bec9387e46ee3f00ba5822197ade7dd540489ec5e95 47650 μs 35790 μs 1.33
marlowe-semantics/d1c03759810747b7cab38c4296593b38567e11195d161b5bb0a2b58f89b2c65a 1499 μs 1025 μs 1.46
marlowe-semantics/d64607eb8a1448595081547ea8780886fcbd9e06036460eea3705c88ea867e33 444 μs 300 μs 1.48
marlowe-semantics/dc241ac6ad1e04fb056d555d6a4f2d08a45d054c6f7f34355fcfeefebef479f3 686.5 μs 467.9 μs 1.47
marlowe-semantics/dd11ae574eaeab0e9925319768989313a93913fdc347c704ddaa27042757d990 1104 μs 756.2 μs 1.46
marlowe-semantics/e9234d2671760874f3f660aae5d3416d18ce6dfd7af4231bdd41b9ec268bc7e1 1360 μs 932.1 μs 1.46
marlowe-semantics/eb4a605ed3a64961e9e66ad9631c2813dadf7131740212762ae4483ec749fe1d 442.5 μs 300.9 μs 1.47
marlowe-semantics/ecb5e8308b57724e0f8533921693f111eba942123cf8660aac2b5bac21ec28f0 966.4 μs 661.8 μs 1.46
marlowe-semantics/f2a8fd2014922f0d8e01541205d47e9bb2d4e54333bdd408cbe7c47c55e73ae4 1098 μs 741.1 μs 1.48
marlowe-semantics/f339f59bdf92495ed2b14e2e4d3705972b4dda59aa929cffe0f1ff5355db8d79 6293 μs 4411 μs 1.43
marlowe-semantics/ffdd68a33afd86f8844c9f5e45b2bda5b035aa02274161b23d57709c0f8b8de6 1368 μs 943.4 μs 1.45
marlowe-role-payout/0004000402010401030101030100040000010104020201030001000204020401 274.5 μs 184.3 μs 1.49
marlowe-role-payout/0100000100010000000001000100010101000101000001000000010000010000 382.5 μs 259.2 μs 1.48
marlowe-role-payout/0101000100000101010000010101000100010101000001000001000000010101 296.4 μs 199.1 μs 1.49
marlowe-role-payout/01dcc372ea619cb9f23c45b17b9a0a8a16b7ca0e04093ef8ecce291667a99a4c 243.8 μs 164.4 μs 1.48
marlowe-role-payout/0201020201020000020000010201020001020200000002010200000101010100 273.5 μs 183.8 μs 1.49
marlowe-role-payout/0202010002010100020102020102020001010101020102010001010101000100 229.8 μs 171.3 μs 1.34
marlowe-role-payout/03d730a62332c51c7b70c16c64da72dd1c3ea36c26b41cd1a1e00d39fda3d6cc 206.3 μs 194.9 μs 1.06
marlowe-role-payout/0403020000030204010000030001000202010101000304030001040404030100 269.6 μs 180.7 μs 1.49
marlowe-role-payout/0405010105020401010304080005050800040301010800080207080704020206 293.1 μs 196.8 μs 1.49
marlowe-role-payout/041a2c3b111139201a3a2c173c392b170e16370d300f2d28342d0f2f0e182e01 295.3 μs 199.1 μs 1.48
marlowe-role-payout/04f592afc6e57c633b9c55246e7c82e87258f04e2fb910c37d8e2417e9db46e5 346.3 μs 234.2 μs 1.48
marlowe-role-payout/057ebc80922f16a5f4bf13e985bf586b8cff37a2f6fe0f3ce842178c16981027 248.5 μs 167 μs 1.49
marlowe-role-payout/06317060a8e488b1219c9dae427f9ce27918a9e09ee8ac424afa33ca923f7954 266.1 μs 196 μs 1.36
marlowe-role-payout/07658a6c898ad6d624c37df1e49e909c2e9349ba7f4c0a6be5f166fe239bfcae 241.8 μs 229.3 μs 1.05
marlowe-role-payout/0bdca1cb8fa7e38e09062557b82490714052e84e2054e913092cd84ac071b961 294.1 μs 279.7 μs 1.05
marlowe-role-payout/0c9d3634aeae7038f839a1262d1a8bc724dc77af9426459417a56ec73240f0e0 262.7 μs 249.6 μs 1.05
marlowe-role-payout/0e72f62b0f922e31a2340baccc768104025400cf7fdd7dae62fbba5fc770936d 282.8 μs 269.3 μs 1.05
marlowe-role-payout/0e97c9d9417354d9460f2eb35018d3904b7b035af16ab299258adab93be0911a 274.2 μs 260.6 μs 1.05
marlowe-role-payout/0f010d040810040b10020e040f0e030b0a0d100f0c080c0c05000d04100c100f 289.6 μs 274 μs 1.06
marlowe-role-payout/1138a04a83edc0579053f9ffa9394b41df38230121fbecebee8c039776a88c0c 254.8 μs 241.5 μs 1.06
marlowe-role-payout/121a0a1b12030616111f02121a0e070716090a0e031c071419121f141409031d 246.4 μs 233.4 μs 1.06
marlowe-role-payout/159e5a1bf16fe984b5569be7011b61b5e98f5d2839ca7e1b34c7f2afc7ffb58e 252.6 μs 239.4 μs 1.06
marlowe-role-payout/195f522b596360690d04586a2563470f2214163435331a6622311f7323433f1c 246.5 μs 233.4 μs 1.06
marlowe-role-payout/1a20b465d48a585ffd622bd8dc26a498a3c12f930ab4feab3a5064cfb3bc536a 278.6 μs 264.5 μs 1.05
marlowe-role-payout/211e1b6c10260c4620074d2e372c260d38643a3d605f63772524034f0a4a7632 265.1 μs 251.5 μs 1.05
marlowe-role-payout/21a1426fb3fb3019d5dc93f210152e90b0a6e740ef509b1cdd423395f010e0ca 278.7 μs 265.1 μs 1.05
marlowe-role-payout/224ce46046fab9a17be4197622825f45cc0c59a6bd1604405148e43768c487ef 254.7 μs 242.1 μs 1.05
marlowe-role-payout/332c2b1c11383d1b373e1315201f1128010e0e1518332f273f141b23243f2a07 243 μs 231.3 μs 1.05
marlowe-role-payout/3565ee025317e065e8555eef288080276716366769aad89e03389f5ec4ce26d7 269.1 μs 256.1 μs 1.05
marlowe-role-payout/3569299fc986f5354d02e627a9eaa48ab46d5af52722307a0af72bae87e256dc 249.5 μs 237 μs 1.05
marlowe-role-payout/36866914aa07cf62ef36cf2cd64c7f240e3371e27bb9fff5464301678e809c40 249.8 μs 237 μs 1.05
marlowe-role-payout/3897ef714bba3e6821495b706c75f8d64264c3fdaa58a3826c808b5a768c303d 258.3 μs 245.7 μs 1.05
marlowe-role-payout/4299c7fcf093a5dbfe114c188e32ca199b571a7c25cb7f766bf49f12dab308be 274.4 μs 260.9 μs 1.05
marlowe-role-payout/46f8d00030436e4da490a86b331fa6c3251425fb8c19556080e124d75bad7bd6 250.8 μs 238.3 μs 1.05
marlowe-role-payout/47364cfaf2c00f7d633283dce6cf84e4fd4e8228c0a0aa50e7c55f35c3ecaa1c 251.2 μs 238.4 μs 1.05
marlowe-role-payout/49b8275d0cb817be40865694ab05e3cfe5fc35fb43b78e7de68c1f3519b536bd 259.1 μs 246.1 μs 1.05
marlowe-role-payout/4fbcfdb577a56b842d6f6938187a783f71d9da7519353e3da3ef0c564e1eb344 310.5 μs 295.5 μs 1.05
marlowe-role-payout/5a0725d49c733130eda8bc6ed5234f7f6ff8c9dd2d201e8806125e5fbcc081f9 264.4 μs 251.6 μs 1.05
marlowe-role-payout/5a2aae344e569a2c644dd9fa8c7b1f129850937eb562b7748c275f9e40bed596 251.1 μs 238.6 μs 1.05
marlowe-role-payout/5efe992e306e31cc857c64a62436ad2f9325acc5b4a74a8cebccdfd853ce63d2 257.9 μs 244.9 μs 1.05
marlowe-role-payout/6621a69217f09d91f42876a9c0cecf79de0e29bdd5b16c82c6c52cf959092ec4 281.9 μs 268.3 μs 1.05
marlowe-role-payout/674b0577409957172ad85223c765d17e94c27714276c49c38dfae0a47a561a1e 246.9 μs 233.8 μs 1.06
marlowe-role-payout/6b7bc2b9002a71b33cfd535d43f26334a283d0b9ad189b7cd74baac232c3b9fc 242.6 μs 229.3 μs 1.06
marlowe-role-payout/6c364699767a84059ffd99cf718562a8c09d96e343f23dc481e8ffda13af424f 251.6 μs 238 μs 1.06
marlowe-role-payout/6d66bddb4269bdf77392d3894da5341cf019d39787522af4f83f01285991e93c 252.4 μs 239 μs 1.06
marlowe-role-payout/73f044f34a30f26639c58bafe952047f74c7bf1eafebab5aadf5b73cfb9024ed 251.9 μs 239.1 μs 1.05
marlowe-role-payout/7b1dd76edc27f00eb382bf996378155baf74d6a7c6f3d5ec837c39d29784aade 252.3 μs 239.4 μs 1.05
marlowe-role-payout/996804e90f2c75fe68886fc8511304b8ab9b36785f8858f5cb098e91c159dde9 261 μs 248.5 μs 1.05
marlowe-role-payout/a004a989c005d59043f996500e110fa756ad1b85800b889d5815a0106388e1d7 267.9 μs 254.9 μs 1.05
marlowe-role-payout/a0fba5740174b5cd24036c8b008cb1efde73f1edae097b9325c6117a0ff40d3b 279.1 μs 265.8 μs 1.05
marlowe-role-payout/a1b25347409c3993feca1a60b6fcaf93d1d4bbaae19ab06fdf50cedc26cee68d 241.2 μs 229.5 μs 1.05
marlowe-role-payout/a27524cfad019df45e4e8316f927346d4cc39da6bdd294fb2c33c3f58e6a8994 250.7 μs 238.4 μs 1.05
marlowe-role-payout/a6664a2d2a82f370a34a36a45234f6b33120a39372331678a3b3690312560ce9 302.3 μs 287.9 μs 1.05
marlowe-role-payout/a6f064b83b31032ea7f25921364727224707268e472a569f584cc6b1d8c017e8 251.5 μs 238.8 μs 1.05
marlowe-role-payout/a7cb09f417c3f089619fe25b7624392026382b458486129efcff18f8912bf302 251.4 μs 238.5 μs 1.05
marlowe-role-payout/a92b4072cb8601fa697e1150c08463b14ffced54eb963df08d322216e27373cb 252.9 μs 239.1 μs 1.06
marlowe-role-payout/cb2ab8e22d1f64e8d204dece092e90e9bf1fa8b2a6e9cba5012dbe4978065832 254.6 μs 242.3 μs 1.05
marlowe-role-payout/eabeeae18131af89fa57936c0e9eb8d2c7adba534f7e1a517d75410028fa0d6c 251.9 μs 239.2 μs 1.05
marlowe-role-payout/ec4712ee820eb959a43ebedfab6735f2325fa52994747526ffd2a4f4f84dd58e 285.2 μs 271.3 μs 1.05
marlowe-role-payout/f1a1e6a487f91feca5606f72bbb1e948c71abf043c6a0ea83bfea9ec6a0f08d8 250.9 μs 238.3 μs 1.05
marlowe-role-payout/f2932e4ca4bbb94b0a9ffbe95fcb7bd5639d9751d75d56d5e14efa5bbed981df 249.3 μs 237.2 μs 1.05
marlowe-role-payout/f53e8cafe26647ccce51e4c31db13608aea1f39034c0f52dee2e5634ef66e747 275.4 μs 261.7 μs 1.05
marlowe-role-payout/f7275afb60e33a550df13a132102e7e925dd28965a4efbe510a89b077ff9417f 251.8 μs 239.5 μs 1.05

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