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chore: update the performance tests to use @chainsafe/benchmark #7373
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c9c194f
Update the benchmark package and remove mocha deps
nazarhussain d72390d
Update the config
nazarhussain 63dbc7f
Migrate the perf tests to use the new package
nazarhussain 4a38fc3
Remove unused benchmark case
nazarhussain aa299da
Remove unnecessary assertion
nazarhussain 393af12
Update the benchmark loop
nazarhussain 36aa4da
Cleanup the map benchmark test
nazarhussain 7763826
Cleanup the map benchmark test
nazarhussain bcdad5f
Update the state benchmark
nazarhussain a24ee29
Update the state calculation
nazarhussain 3ca2f10
Revert changes to the benchmark
nazarhussain a4446b9
Disable benchmark with an issue reference
nazarhussain 8affe84
Fix lint
nazarhussain 8a0e51d
Trigger GC
nazarhussain 627c7a5
Revert "Trigger GC"
nazarhussain d75b90f
Update the convergence factor
nazarhussain 2ed9a83
Update the benchmark util
nazarhussain 58f75d4
Update the convergance factor
nazarhussain d32d4af
Update the package version
nazarhussain f5c69cc
Update the bench options
nazarhussain 178a36a
Revert the benchfiles change
nazarhussain 6e7cf5a
Update package version
nazarhussain 17eb18b
Fix benchmark test
nazarhussain 60fe2df
Fix lint error
nazarhussain 2213730
Update benchmark rule
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Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,10 +1,11 @@ | ||
# Mocha opts | ||
extension: ["ts"] | ||
colors: true | ||
node-option: | ||
- "loader=ts-node/register" | ||
|
||
# benchmark opts | ||
threshold: 3 | ||
maxMs: 60_000 | ||
maxMs: 60000 | ||
minRuns: 10 | ||
# Default is set to 0.005, which is too low considering the benchmark setup we have | ||
# Changing it to 0.05 which is 5/100, so 5% difference of moving average among run times | ||
convergeFactor: 0.075 # 7.5 / 100 | ||
triggerGC: false | ||
sort: true | ||
convergence: linear | ||
averageCalculation: clean-outliers |
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how did you come up with this number? we should probably test if this is still low enough to catch actual regressions
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I run through some runs of few files and saw the outliers. Then approximates the outlier and came up with reasonable percentage to filter. And it's just an approximation, if the outliers skewed further the benchmark can fail.