git clone ''

(ql:quickload :hugoduncan.criterium)


Criterium measures the computation time of an expression. It is designed to address some of the pitfalls of benchmarking, and benchmarking on the JVM in particular.

This includes:



Add the following to your :dependencies:

[criterium "0.4.5"]




The top level interface is in criterium.core.

(use 'criterium.core)

Use bench to run a benchmark in a simple manner.

(bench (Thread/sleep 1000))
                   Execution time mean : 1.000803 sec
          Execution time std-deviation : 328.501853 us
         Execution time lower quantile : 1.000068 sec ( 2.5%)
         Execution time upper quantile : 1.001186 sec (97.5%)

By default bench is quiet about its progress. Run with-progress-reporting to get progress information on *out*.

(with-progress-reporting (bench (Thread/sleep 1000) :verbose))
(with-progress-reporting (quick-bench (Thread/sleep 1000) :verbose))

Lower level functions are available, that separate benchmark statistic generation and reporting.

(report-result (benchmark (Thread/sleep 1000) {:verbose true}))
(report-result (quick-benchmark (Thread/sleep 1000)))

Note that results are returned to the user to prevent JIT from recognising that the results are not used.

Measurement Overhead Estimation

Criterium will automatically estimate a time for its measurement overhead. The estimate is normally made once per session, and is available in the criterium.core/estimated-overhead-cache var.

If the estimation is made while there is a lot of other processing going on, then benchmarking quick functions may report small negative times. You can force a recalculation of the overhead by calling criterium.core/estimated-overhead!.

If you want consistency across JVM processes, it might be prudent to explicitly set criterium.core/estimated-overhead! to a constant value.


API Documentation Annotated Source

See Elliptic Group for a Java benchmarking library. The accompanying article describes many of the JVM benchmarking pitfalls.

See Criterion for a Haskell benchmarking library that applies many of the same statistical techniques.


Serial correlation detection. Multimodal distribution detection. Use kernel density estimators?


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