regex-dna benchmark N=10,000

Each chart bar shows how many times more Code, one ↓ regex-dna program used, compared to the program that used least Code.

These are not the only programs that could be written. These are not the only compilers and interpreters. These are not the only programming languages.

Column × shows how many times more each program used compared to the benchmark program that used least.

    sortsortsort 
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0Pyston #5 0.080.08?501  0% 0% 0% 100%
1.0Pyston #5 0.080.08?501  13% 0% 0% 100%
1.0Jython #5 3.401.41173,756501  50% 56% 52% 96%
1.0Jython #5 3.701.57190,872501  69% 52% 72% 64%
1.0PyPy 2 #5 0.140.16?501  82% 0% 6% 6%
1.0IronPython #5 0.730.7344,844501  8% 3% 100% 5%
1.0Pyston #5 0.080.08?501  11% 13% 0% 89%
1.0IronPython #5 0.730.7345,660501  5% 100% 3% 6%
1.0IronPython #5 0.700.7046,832501  3% 29% 44% 32%
1.0PyPy 2 #5 0.140.14?501  0% 13% 0% 100%
1.0Python 2 #5 0.030.04?501  0% 0% 25% 100%
1.0Jython #5 3.351.42176,284501  46% 92% 52% 57%
1.0PyPy 2 #5 0.140.14?501  0% 7% 100% 13%
1.0Python 2 #5 0.030.03?501  0% 100% 0% 0%
1.0Python 2 #5 0.030.03?501  0% 0% 100% 0%
1.0Cython #5 0.050.05?524  0% 29% 100% 0%
1.0Python development version #5 0.050.05?524  0% 0% 0% 100%
1.0Nuitka #5 0.050.06?524  0% 0% 83% 0%
1.0Python development version #5 0.050.06?524  17% 0% 0% 100%
1.0Nuitka #5 0.060.06?524  0% 14% 0% 100%
1.0Python development version #5 0.050.05?524  0% 0% 0% 100%
1.0Nuitka #5 0.060.06?524  17% 0% 0% 100%
1.0PyPy 3 #5 0.220.221,116524  5% 100% 0% 9%
1.0Python 3 #5 0.050.05?524  0% 100% 0% 0%
1.0PyPy 3 #5 0.220.22992524  0% 100% 0% 9%
1.0PyPy 3 #5 0.220.241,136524  8% 8% 91% 13%
1.0Python 3 #5 0.050.05?524  17% 83% 0% 0%
1.0Cython #5 0.050.05?524  83% 0% 0% 0%
1.0Python 3 #5 0.050.05?524  0% 100% 0% 0%
1.0Cython #5 0.050.05?524  0% 0% 100% 0%
1.1Nuitka 0.140.19?573  6% 12% 40% 11%
1.1PyPy 3 0.580.5669,696573  86% 11% 16% 16%
1.1Nuitka 0.140.19?573  16% 6% 45% 5%
1.1PyPy 3 0.550.5270,760573  15% 13% 10% 86%
1.1Nuitka 0.120.18?573  39% 17% 21% 11%
1.1PyPy 3 0.570.5470,208573  83% 15% 13% 9%
1.1Python development version 0.100.07?573  86% 20% 17% 29%
1.1Python development version 0.110.10?573  33% 55% 11% 40%
1.1Python 3 0.140.19?573  28% 53% 11% 15%
1.1Python 3 0.140.19?573  16% 11% 50% 16%
1.1Python 3 0.110.17?573  25% 13% 6% 19%
1.1Python development version 0.110.07?573  83% 38% 29% 17%
1.2Pyston 0.220.2694,520612  15% 62% 11% 12%
1.2Pyston 0.210.2594,996612  12% 23% 16% 60%
1.2Python 2 0.110.17?612  29% 12% 17% 39%
1.2Python 2 0.090.16?612  31% 13% 7% 13%
1.2PyPy 2 0.330.30210,360612  16% 17% 20% 71%
1.2Python 2 0.100.16?612  13% 6% 31% 18%
1.2PyPy 2 0.340.29208,284612  14% 20% 76% 21%
1.2PyPy 2 0.340.29208,448612  14% 11% 24% 72%
1.2Pyston 0.200.2594,660612  71% 21% 35% 27%
missing benchmark programs
Shedskin No program
Numba No program
MicroPython No program
Grumpy No program
Graal No program

 regex-dna benchmark : Match DNA 8-mers and substitute nucleotides for IUB codes

diff program output for this 100KB input file (generated with the fasta program N = 10000) with this output file to check your program is correct before contributing.

We are trying to show the performance of various programming language implementations - so we ask that contributed programs not only give the correct result, but also use the same algorithm to calculate that result.

We use FASTA files generated by the fasta benchmark as input for this benchmark. Note: the file may include both lowercase and uppercase codes.

Each program should

Revised BSD license

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