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 Which programming languages are fastest?

No. Which of these programming language implementations have the fastest benchmark programs?

This box plot shows how many times slower, the fastest benchmark programs for selected language implementations were, compared to the fastest programs.

Select the language implementations you want to chart (deselect those you want to remove) then click the chart button.

Or follow the links to compare 2 language implementations directly - one-against-another for all the benchmarks - on Time-used, Memory-used and Code-used.

  compare 2  |-  |---  25% median  75%  ---|  -|

Fortran Intel 1.001.001.001.001.492.245.23

Ada 2005 GNAT 1.001.001.151.231.622.315.08

C++ GNU g++ 1.001.001.011.341.581.981.98

ATS 1.011.011.181.341.602.226.77

C GNU gcc 1.001.001.011.361.572.403.30

Java 7 -server 1.121.121.561.752.032.745.58

Scala 1.281.281.612.052.844.707.21

Haskell GHC 1.141.141.522.113.997.287.28

Pascal Free Pascal 1.381.382.012.392.844.085.36

C# Mono 1.441.442.262.715.249.7122.21

JavaScript V8 1.001.002.012.8510.5223.30128.95

Clean 1.761.762.112.924.157.2111.35

OCaml 1.551.552.023.304.916.266.26

Lisp SBCL 1.051.051.873.874.989.6311.81

F# Mono 1.431.432.903.975.8810.3521.11

Racket 1.171.443.904.775.558.0118.89

Go 2.392.392.805.018.288.858.85

Clojure 2.822.825.256.5513.9317.0717.07

Erlang HiPE 3.053.057.2012.7923.5533.3433.34

Smalltalk VisualWorks 4.984.9812.4617.5724.8043.3085.01

Lua 11.3111.3119.5928.5346.8386.8786.87

Ruby JRuby 10.9710.9731.4234.5263.5781.4781.47

Ruby 1.9 6.236.239.7343.8092.87217.57268.75

Python 3 1.121.126.6547.9377.48130.06130.06

PHP 1.681.687.1248.3769.2679.7079.70

Perl 2.222.227.9155.85103.59220.49220.49

Mozart/Oz 6.736.7334.2857.5676.87140.76147.58

C CINT 71.0971.09179.97241.92379.29678.274797.32

 Which programming languages are fastest? Robust Statistics

No. Which of these programming language implementations have the fastest benchmark programs?

For each benchmark, F is the fastest time measurement.
For each language implementation, each time measurement T is then normalized to T ÷ F


There are other ways to analyse and present this data: look for patterns in Code-used Time-used Shapes or ask Which programming language is best? or take the Summary Data and do your own analysis!

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