https://github.com/hugoduncan/criterium.git
git clone 'https://github.com/hugoduncan/criterium.git'
(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"]
<dependency>
<groupId>criterium</groupId>
<artifactId>criterium</artifactId>
<version>0.4.5</version>
</dependency>
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.
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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