regex-dna benchmark N=10,000

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

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.

    sortsort sort
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
 Python development version #5 0.060.07?524  17% 86% 25% 0%
 Python 3 0.140.19?573  28% 53% 11% 15%
 Cython 0.140.19?573  19% 16% 11% 50%
 Python 3 0.140.19?573  16% 11% 50% 16%
 Nuitka 0.140.19?573  5% 11% 55% 15%
 PyPy 3 #5 0.140.14?524  13% 0% 0% 100%
 Python 3 #5 0.050.05?524  0% 100% 0% 0%
 PyPy 3 #5 0.140.14?524  0% 100% 0% 0%
 Python development version #5 0.050.05?524  0% 0% 100% 0%
 PyPy 3 #5 0.140.14?524  13% 0% 0% 93%
 Cython 0.100.16?573  0% 13% 63% 19%
 Python 3 0.110.17?573  25% 13% 6% 19%
 Python 3 #5 0.050.05?524  17% 83% 0% 0%
 Python 2 0.100.16?612  13% 6% 31% 18%
 Python 3 #5 0.050.05?524  0% 100% 0% 0%
 Python 2 0.110.17?612  29% 12% 17% 39%
 Python development version #5 0.050.05?524  20% 0% 0% 100%
 Python 2 0.090.16?612  31% 13% 7% 13%
 Nuitka 0.140.19?573  11% 30% 22% 40%
 Python development version 0.120.18?573  17% 6% 6% 41%
 Pyston #5 0.080.08?501  13% 0% 0% 100%
 Python development version 0.130.19?573  11% 40% 11% 16%
 Cython 0.130.18?573  50% 18% 11% 6%
 Nuitka 0.140.20?573  16% 19% 11% 42%
 Python development version 0.120.18?573  12% 17% 35% 6%
 PyPy 2 #5 0.130.13?501  7% 93% 0% 0%
 Cython #5 0.050.05?524  29% 29% 0% 100%
 Cython #5 0.050.05?524  100% 0% 0% 0%
 PyPy 2 #5 0.130.13?501  0% 7% 100% 0%
 Cython #5 0.050.05?524  0% 0% 100% 0%
 PyPy 2 #5 0.130.14?501  50% 7% 57% 0%
 Python 2 #5 0.030.04?501  0% 0% 25% 100%
 Pyston #5 0.080.08?501  0% 0% 0% 100%
 Python 2 #5 0.030.03?501  0% 100% 0% 0%
 Python 2 #5 0.030.03?501  0% 0% 100% 0%
 Pyston #5 0.080.08?501  11% 13% 0% 89%
 IronPython #5 0.730.7344,844501  8% 3% 100% 5%
 IronPython #5 0.730.7345,660501  5% 100% 3% 6%
 IronPython #5 0.700.7046,832501  3% 29% 44% 32%
 PyPy 3 0.530.5070,704573  18% 86% 10% 6%
 PyPy 3 0.520.4970,712573  14% 84% 12% 4%
 PyPy 3 0.540.5170,912573  14% 8% 84% 10%
 Pyston 0.220.2694,520612  15% 62% 11% 12%
 Pyston 0.200.2594,660612  71% 21% 35% 27%
 Pyston 0.210.2594,996612  12% 23% 16% 60%
 Jython #5 3.401.41173,756501  50% 56% 52% 96%
 Jython #5 3.351.42176,284501  46% 92% 52% 57%
 Jython #5 3.701.57190,872501  69% 52% 72% 64%
 PyPy 2 0.340.30210,336612  77% 17% 17% 10%
 PyPy 2 0.330.29210,652612  27% 17% 17% 69%
 PyPy 2 0.310.27210,712612  19% 15% 75% 15%
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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