tasty-bench: Featherlight benchmark framework

[ benchmarking, development, library, mit, performance, testing ] [ Propose Tags ]

Featherlight framework (only one file!) for performance measurement with API mimicking criterion and gauge, featuring built-in comparison against previous runs and between benchmarks. Our benchmarks are just regular tasty tests.

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Versions [RSS] [faq] 0.1, 0.2, 0.2.1, 0.2.2, 0.2.3, 0.2.4, 0.2.5, 0.3, 0.3.1 (info)
Change log changelog.md
Dependencies base (>=4.3 && <5), containers (>=0.4), deepseq (>=1.1), tagged (>=0.2), tasty (>=0.11.3) [details]
License MIT
Copyright 2021 Andrew Lelechenko
Author Andrew Lelechenko
Maintainer Andrew Lelechenko <andrew.lelechenko@gmail.com>
Category Development, Performance, Testing, Benchmarking
Home page https://github.com/Bodigrim/tasty-bench
Bug tracker https://github.com/Bodigrim/tasty-bench/issues
Source repo head: git clone https://github.com/Bodigrim/tasty-bench
Uploaded by Bodigrim at 2021-10-31T00:45:56Z
Distributions LTSHaskell:0.2.5, NixOS:0.2.5, Stackage:0.3.1
Downloads 2548 total (168 in the last 30 days)
Rating 2.0 (votes: 1) [estimated by Bayesian average]
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Status Hackage Matrix CI
Docs available [build log]
Last success reported on 2021-10-31 [all 1 reports]


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Manual Flags


When disabled, reduces API to functions independent of tasty: combinators to construct Benchmarkable and measureCpuTime.


Emit ongoing diagnostic information for benchmarks.

Automatic Flags

Use -f <flag> to enable a flag, or -f -<flag> to disable that flag. More info


Maintainer's Corner

For package maintainers and hackage trustees


Readme for tasty-bench-0.3.1

[back to package description]

tasty-bench Hackage Stackage LTS Stackage Nightly

Featherlight benchmark framework (only one file!) for performance measurement with API mimicking criterion and gauge. A prominent feature is built-in comparison against previous runs and between benchmarks.

How lightweight is it?

There is only one source file Test.Tasty.Bench and no non-boot dependencies except tasty. So if you already depend on tasty for a test suite, there is nothing else to install.

Compare this to criterion (10+ modules, 50+ dependencies) and gauge (40+ modules, depends on basement and vector). A build on a clean machine is up to 16x faster than criterion and up to 4x faster than gauge. A build without dependencies is up to 6x faster than criterion and up to 8x faster than gauge.

How is it possible?

Our benchmarks are literally regular tasty tests, so we can leverage all existing machinery for command-line options, resource management, structuring, listing and filtering benchmarks, running and reporting results. It also means that tasty-bench can be used in conjunction with other tasty ingredients.

Unlike criterion and gauge we use a very simple statistical model described below. This is arguably a questionable choice, but it works pretty well in practice. A rare developer is sufficiently well-versed in probability theory to make sense and use of all numbers generated by criterion.

How to switch?

Cabal mixins allow to taste tasty-bench instead of criterion or gauge without changing a single line of code:

cabal-version: 2.0

benchmark foo
    tasty-bench (Test.Tasty.Bench as Criterion, Test.Tasty.Bench as Criterion.Main, Test.Tasty.Bench as Gauge, Test.Tasty.Bench as Gauge.Main)

This works vice versa as well: if you use tasty-bench, but at some point need a more comprehensive statistical analysis, it is easy to switch temporarily back to criterion.

How to write a benchmark?

Benchmarks are declared in a separate section of cabal file:

cabal-version:   2.0
name:            bench-fibo
version:         0.0
build-type:      Simple
synopsis:        Example of a benchmark

benchmark bench-fibo
  main-is:       BenchFibo.hs
  type:          exitcode-stdio-1.0
  build-depends: base, tasty-bench
  if impl(ghc >= 8.10)
    ghc-options:  "-with-rtsopts=-A32m --nonmoving-gc"
    ghc-options:  "-with-rtsopts=-A32m"

And here is BenchFibo.hs:

import Test.Tasty.Bench

fibo :: Int -> Integer
fibo n = if n < 2 then toInteger n else fibo (n - 1) + fibo (n - 2)

main :: IO ()
main = defaultMain
  [ bgroup "fibonacci numbers"
    [ bench "fifth"     $ nf fibo  5
    , bench "tenth"     $ nf fibo 10
    , bench "twentieth" $ nf fibo 20

Since tasty-bench provides an API compatible with criterion, one can refer to its documentation for more examples.

How to read results?

Running the example above (cabal bench or stack bench) results in the following output:

  fibonacci numbers
    fifth:     OK (2.13s)
       63 ns ± 3.4 ns
    tenth:     OK (1.71s)
      809 ns ±  73 ns
    twentieth: OK (3.39s)
      104 μs ± 4.9 μs

All 3 tests passed (7.25s)

The output says that, for instance, the first benchmark was repeatedly executed for 2.13 seconds (wall time), its mean CPU time was 63 nanoseconds and, assuming ideal precision of a system clock, execution time does not often diverge from the mean further than ±3.4 nanoseconds (double standard deviation, which for normal distributions corresponds to 95% probability). Take standard deviation numbers with a grain of salt; there are lies, damned lies, and statistics.

Note that this data is not directly comparable with criterion output:

benchmarking fibonacci numbers/fifth
time                 62.78 ns   (61.99 ns .. 63.41 ns)
                     0.999 R²   (0.999 R² .. 1.000 R²)
mean                 62.39 ns   (61.93 ns .. 62.94 ns)
std dev              1.753 ns   (1.427 ns .. 2.258 ns)

One might interpret the second line as saying that 95% of measurements fell into 61.99–63.41 ns interval, but this is wrong. It states that the OLS regression (which is not exactly the mean) of wall execution time is most probably somewhere between 61.99 ns and 63.41 ns, but does not say a thing about individual measurements. To understand how far away a typical measurement deviates you need to add/subtract double standard deviation yourself (which gives 62.78 ns ± 3.506 ns, similar to tasty-bench above).

To add to the confusion, gauge in --small mode outputs not the second line of criterion report as one might expect, but a mean value from the penultimate line and a standard deviation:

fibonacci numbers/fifth                  mean 62.39 ns  ( +- 1.753 ns  )

The interval ±1.753 ns answers for 68% of samples only, double it to estimate the behavior in 95% of cases.

Wall-clock time vs. CPU time

What time are we talking about? Both criterion and gauge by default report wall-clock time, which is affected by any other application which runs concurrently. While ideally benchmarks are executed on a dedicated server without any other load, but — let's face the truth — most of developers run benchmarks on a laptop with a hundred other services and a window manager, and watch videos while waiting for benchmarks to finish. That's the cause of a notorious "variance introduced by outliers: 88% (severely inflated)" warning.

To alleviate this issue tasty-bench measures CPU time by getCPUTime instead of wall-clock time. It does not provide a perfect isolation from other processes (e. g., if CPU cache is spoiled by others, populating data back from RAM is your burden), but is a bit more stable.

Caveat: this means that for multithreaded algorithms tasty-bench reports total elapsed CPU time across all cores, while criterion and gauge print maximum of core's wall-clock time.

Statistical model

Here is a procedure used by tasty-bench to measure execution time:

  1. Set n ← 1.
  2. Measure execution time tₙ of n iterations and execution time t₂ₙ of 2n iterations.
  3. Find t which minimizes deviation of (nt, 2nt) from (tₙ, t₂ₙ), namely t ← (tₙ + 2t₂ₙ) / 5n.
  4. If deviation is small enough (see --stdev below) or time is running out soon (see --timeout below), return t as a mean execution time.
  5. Otherwise set n2n and jump back to Step 2.

This is roughly similar to the linear regression approach which criterion takes, but we fit only two last points. This allows us to simplify away all heavy-weight statistical analysis. More importantly, earlier measurements, which are presumably shorter and noisier, do not affect overall result. This is in contrast to criterion, which fits all measurements and is biased to use more data points corresponding to shorter runs (it employs n1.05n progression).

An alert reader could object that we measure standard deviation for samples with n and 2n iterations, but report it scaled to a single iteration. Strictly speaking, this is justified only if we assume that deviating factors are either roughly periodic (e. g., coarseness of a system clock, garbage collection) or are likely to affect several successive iterations in the same way (e. g., slow down by another concurrent process).

Obligatory disclaimer: statistics is a tricky matter, there is no one-size-fits-all approach. In the absence of a good theory simplistic approaches are as (un)sound as obscure ones. Those who seek statistical soundness should rather collect raw data and process it themselves using a proper statistical toolbox. Data reported by tasty-bench is only of indicative and comparative significance.

Memory usage

Configuring RTS to collect GC statistics (e. g., via cabal bench --benchmark-options '+RTS -T' or stack bench --ba '+RTS -T') enables tasty-bench to estimate and report memory usage:

  fibonacci numbers
    fifth:     OK (2.13s)
       63 ns ± 3.4 ns, 223 B  allocated,   0 B  copied, 2.0 MB peak memory
    tenth:     OK (1.71s)
      809 ns ±  73 ns, 2.3 KB allocated,   0 B  copied, 4.0 MB peak memory
    twentieth: OK (3.39s)
      104 μs ± 4.9 μs, 277 KB allocated,  59 B  copied, 5.0 MB peak memory

All 3 tests passed (7.25s)

This data is reported as per RTSStats fields: allocated_bytes, copied_bytes and max_mem_in_use_bytes.

Combining tests and benchmarks

When optimizing an existing function, it is important to check that its observable behavior remains unchanged. One can rebuild both tests and benchmarks after each change, but it would be more convenient to run sanity checks within benchmark itself. Since our benchmarks are compatible with tasty tests, we can easily do so.

Imagine you come up with a faster function myFibo to generate Fibonacci numbers:

import Test.Tasty.Bench
import Test.Tasty.QuickCheck -- from tasty-quickcheck package

fibo :: Int -> Integer
fibo n = if n < 2 then toInteger n else fibo (n - 1) + fibo (n - 2)

myFibo :: Int -> Integer
myFibo n = if n < 3 then toInteger n else myFibo (n - 1) + myFibo (n - 2)

main :: IO ()
main = Test.Tasty.Bench.defaultMain -- not Test.Tasty.defaultMain
  [ bench "fibo   20" $ nf fibo   20
  , bench "myFibo 20" $ nf myFibo 20
  , testProperty "myFibo = fibo" $ \n -> fibo n === myFibo n

This outputs:

  fibo   20:     OK (3.02s)
    104 μs ± 4.9 μs
  myFibo 20:     OK (1.99s)
     71 μs ± 5.3 μs
  myFibo = fibo: FAIL
    *** Failed! Falsified (after 5 tests and 1 shrink):
    1 /= 2
    Use --quickcheck-replay=927711 to reproduce.

1 out of 3 tests failed (5.03s)

We see that myFibo is indeed significantly faster than fibo, but unfortunately does not do the same thing. One should probably look for another way to speed up generation of Fibonacci numbers.


  • If benchmarks take too long, set --timeout to limit execution time of individual benchmarks, and tasty-bench will do its best to fit into a given time frame. Without --timeout we rerun benchmarks until achieving a target precision set by --stdev, which in a noisy environment of a modern laptop with GUI may take a lot of time.

    While criterion runs each benchmark at least for 5 seconds, tasty-bench is happy to conclude earlier, if it does not compromise the quality of results. In our experiments tasty-bench suites tend to finish earlier, even if some individual benchmarks take longer than with criterion.

    A common source of noisiness is garbage collection. Setting a larger allocation area (nursery) is often a good idea, either via cabal bench --benchmark-options '+RTS -A32m' or stack bench --ba '+RTS -A32m'. Alternatively bake it into cabal file as ghc-options: "-with-rtsopts=-A32m".

    For GHC ≥ 8.10 consider switching benchmarks to a non-moving garbage collector, because it decreases GC pauses and corresponding noise: +RTS --nonmoving-gc.

  • If benchmark results look malformed like below, make sure that you are invoking Test.Tasty.Bench.defaultMain and not Test.Tasty.defaultMain (the difference is consoleBenchReporter vs. consoleTestReporter):

      fibo 20:       OK (1.46s)
        Response {respEstimate = Estimate {estMean = Measurement {measTime = 87496728, measAllocs = 0, measCopied = 0}, estStdev = 694487}, respIfSlower = FailIfSlower Infinity, respIfFaster = FailIfFaster Infinity}
  • If benchmarks fail with an error message

    Unhandled resource. Probably a bug in the runner you're using.


    Unexpected state of the resource (NotCreated) in getResource. Report as a tasty bug.

    this is likely caused by env or envWithCleanup affecting benchmarks structure. You can use env to read test data from IO, but not to read benchmark names or affect their hierarchy in other way. This is a fundamental restriction of tasty to list and filter benchmarks without launching missiles.

  • If benchmarks fail with Test dependencies form a loop, this is likely because of bcompare, which compares a benchmark with itself. Locating a benchmark in a global environment may be tricky, please refer to tasty documentation for details.

Isolating interfering benchmarks

One difficulty of benchmarking in Haskell is that it is hard to isolate benchmarks so that they do not interfere. Changing the order of benchmarks or skipping some of them has an effect on heap's layout and thus affects garbage collection. This issue is well attested in both criterion and gauge.

Usually (but not always) skipping some benchmarks speeds up remaining ones. That's because once a benchmark allocated heap which for some reason was not promptly released afterwards (e. g., it forced a top-level thunk in an underlying library), all further benchmarks are slowed down by garbage collector processing this additional amount of live data over and over again.

There are several mitigation strategies. First of all, giving garbage collector more breathing space by +RTS -A32m (or more) is often good enough.

Further, avoid using top-level bindings to store large test data. Once such thunks are forced, they remain allocated forever, which affects detrimentally subsequent unrelated benchmarks. Treat them as external data, supplied via env: instead of

largeData :: String
largeData = replicate 1000000 'a'

main :: IO ()
main = defaultMain
  [ bench "large" $ nf length largeData, ... ]


import Control.DeepSeq (force)
import Control.Exception (evaluate)

main :: IO ()
main = defaultMain
  [ env (evaluate (force (replicate 1000000 'a'))) $ \largeData ->
    bench "large" $ nf length largeData, ... ]

Finally, as an ultimate measure to reduce interference between benchmarks, one can run each of them in a separate process. We do not quite recommend this approach, but if you are desperate, here is how.

Assuming that a benchmark is declared in cabal file as benchmark my-bench component, let's first find its executable:

cabal build --enable-benchmarks my-bench
MYBENCH=$(cabal list-bin my-bench) # available since cabal-3.4

Now list all benchmark names (hopefully, they do not contain newlines), escape quotes and slashes, and run each of them separately:

$MYBENCH -l | sed -e 's/[\"]/\\\\\\&/g' | while read -r name; do $MYBENCH -p '$0 == "'"$name"'"'; done

Comparison against baseline

One can compare benchmark results against an earlier baseline in an automatic way. To use this feature, first run tasty-bench with --csv FILE key to dump results to FILE in CSV format (it could be a good idea to set smaller --stdev, if possible):

Name,Mean (ps),2*Stdev (ps)
All.fibonacci numbers.fifth,48453,4060
All.fibonacci numbers.tenth,637152,46744
All.fibonacci numbers.twentieth,81369531,3342646

Now modify implementation and rerun benchmarks with --baseline FILE key. This produces a report as follows:

  fibonacci numbers
    fifth:     OK (0.44s)
       53 ns ± 2.7 ns,  8% slower than baseline
    tenth:     OK (0.33s)
      641 ns ±  59 ns
    twentieth: OK (0.36s)
       77 μs ± 6.4 μs,  5% faster than baseline

All 3 tests passed (1.50s)

You can also fail benchmarks, which deviate too far from baseline, using --fail-if-slower and --fail-if-faster options. For example, setting both of them to 6 will fail the first benchmark above (because it is more than 6% slower), but the last one still succeeds (even while it is measurably faster than baseline, deviation is less than 6%). Consider also using --hide-successes to show only problematic benchmarks, or even tasty-rerun package to focus on rerunning failing items only.

If you wish to compare two CSV reports non-interactively, here is a handy awk incantation:

awk 'BEGIN{FS=",";OFS=",";print "Name,Old,New,Ratio"}FNR==1{next}FNR==NR{a[$1]=$2;next}{print $1,a[$1],$2,$2/a[$1];gs+=log($2/a[$1]);gc++}END{print "Geometric mean,,",exp(gs/gc)}' old.csv new.csv

Note that columns in CSV report are different from what criterion or gauge would produce. If names do not contain commas, missing columns can be faked this way:

cat tasty-bench.csv \
| awk 'BEGIN {FS=",";OFS=","}; {print $1,$2/1e12,$2/1e12,$2/1e12,$3/2e12,$3/2e12,$3/2e12}' \
| sed '1s/.*/Name,Mean,MeanLB,MeanUB,Stddev,StddevLB,StddevUB/'

To fake gauge in --csvraw mode use

cat tasty-bench.csv \
| awk 'BEGIN {FS=",";OFS=","}; {print $1,1,$2/1e12,0,$2/1e12,$2/1e12,0,$6+0,0,0,0,0,$4+0,0,$5+0,0,0,0,0}' \
| sed '1s/.*/name,iters,time,cycles,cpuTime,utime,stime,maxrss,minflt,majflt,nvcsw,nivcsw,allocated,numGcs,bytesCopied,mutatorWallSeconds,mutatorCpuSeconds,gcWallSeconds,gcCpuSeconds/'

Please refer to gawk manual, if you wish to process names with commas or quotes.

Comparison between benchmarks

You can also compare benchmarks to each other without reaching to external tools, all in the comfort of your terminal.

import Test.Tasty.Bench

fibo :: Int -> Integer
fibo n = if n < 2 then toInteger n else fibo (n - 1) + fibo (n - 2)

main :: IO ()
main = defaultMain
  [ bgroup "fibonacci numbers"
    [ bcompare "tenth"  $ bench "fifth"     $ nf fibo  5
    ,                     bench "tenth"     $ nf fibo 10
    , bcompare "tenth"  $ bench "twentieth" $ nf fibo 20

This produces a report, comparing mean times of fifth and twentieth to tenth:

  fibonacci numbers
    fifth:     OK (16.56s)
      121 ns ± 2.6 ns, 0.08x
    tenth:     OK (6.84s)
      1.6 μs ±  31 ns
    twentieth: OK (6.96s)
      203 μs ± 4.1 μs, 128.36x

Locating a baseline benchmark in larger suites could get tricky;

bcompare "$NF == \"tenth\" && $(NF-1) == \"fibonacci numbers\""

is a more robust choice of an awk pattern here.

One can leverage comparisons between benchmarks to implement portable performance tests, expressing properties like "this algorithm must be at least twice faster than that one" or "this operation should not be more than thrice slower than that". This can be achieved with bcompareWithin, which takes an acceptable interval of performance as an argument.

Plotting results

Users can dump results into CSV with --csv FILE and plot them using gnuplot or other software. But for convenience there is also a built-in quick-and-dirty SVG plotting feature, which can be invoked by passing --svg FILE.

Build flags

Build flags are a brittle subject and users do not normally need to touch them.

  • If you find yourself in an environment, where tasty is not available and you have access to boot packages only, you can still use tasty-bench! Just copy Test/Tasty/Bench.hs to your project (imagine it like a header-only C library). It will provide you with functions to build Benchmarkable and run them manually via measureCpuTime. This mode of operation can be also configured by disabling Cabal flag tasty.

  • If results are amiss or oscillate wildly and adjusting --timeout and --stdev does not help, you may be interested to investigate individual timings of successive runs by enabling Cabal flag debug. This will pipe raw data into stderr.

Command-line options

Use --help to list command-line options.

  • -p, --pattern

    This is a standard tasty option, which allows filtering benchmarks by a pattern or awk expression. Please refer to tasty documentation for details.

  • -t, --timeout

    This is a standard tasty option, setting timeout for individual benchmarks in seconds. Use it when benchmarks tend to take too long: tasty-bench will make an effort to report results (even if of subpar quality) before timeout. Setting timeout too tight (insufficient for at least three iterations) will result in a benchmark failure. One can adjust it locally for a group of benchmarks, e. g., localOption (mkTimeout 100000000) for 100 seconds.

  • --stdev

    Target relative standard deviation of measurements in percents (5% by default). Large values correspond to fast and loose benchmarks, and small ones to long and precise. It can also be adjusted locally for a group of benchmarks, e. g., localOption (RelStDev 0.02). If benchmarking takes far too long, consider setting --timeout, which will interrupt benchmarks, potentially before reaching the target deviation.

  • --csv

    File to write results in CSV format.

  • --baseline

    File to read baseline results in CSV format (as produced by --csv).

  • --fail-if-slower, --fail-if-faster

    Upper bounds of acceptable slow down / speed up in percents. If a benchmark is unacceptably slower / faster than baseline (see --baseline), it will be reported as failed. Can be used in conjunction with a standard tasty option --hide-successes to show only problematic benchmarks. Both options can be adjusted locally for a group of benchmarks, e. g., localOption (FailIfSlower 0.10).

  • --svg

    File to plot results in SVG format.

  • +RTS -T

    Estimate and report memory usage.

Custom command-line options

As usual with tasty, it is easy to extend benchmarks with custom command-line options. Here is an example:

import Data.Proxy
import Test.Tasty.Bench
import Test.Tasty.Ingredients.Basic
import Test.Tasty.Options
import Test.Tasty.Runners

newtype RandomSeed = RandomSeed Int

instance IsOption RandomSeed where
  defaultValue = RandomSeed 42
  parseValue = fmap RandomSeed . safeRead
  optionName = pure "seed"
  optionHelp = pure "Random seed used in benchmarks"

main :: IO ()
main = do
  let customOpts  = [Option (Proxy :: Proxy RandomSeed)]
      ingredients = includingOptions customOpts : benchIngredients
  opts <- parseOptions ingredients benchmarks
  let RandomSeed seed = lookupOption opts
  defaultMainWithIngredients ingredients benchmarks

benchmarks :: Benchmark
benchmarks = bgroup "All" []