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 3 0.140.20?573  15% 26% 42% 19%
 Python 3 0.140.19?573  17% 19% 20% 42%
 Nuitka 0.120.18?573  22% 39% 11% 11%
 Nuitka 0.140.20?573  5% 5% 50% 10%
 Python development version #5 0.050.05?524  0% 0% 100% 0%
 Python development version 0.100.07?573  17% 100% 29% 29%
 Python development version 0.110.07?573  88% 38% 29% 25%
 Python development version #5 0.050.05?524  20% 100% 0% 0%
 Python 3 #5 0.070.07?524  57% 0% 14% 63%
 Nuitka #5 0.060.06?524  0% 0% 50% 50%
 Pyston #5 0.080.08?501  11% 13% 0% 89%
 Nuitka #5 0.060.06?524  100% 0% 0% 0%
 Python 3 #5 0.050.05?524  0% 17% 100% 0%
 Nuitka #5 0.060.06?524  0% 100% 0% 0%
 Python 2 0.110.17?612  29% 12% 17% 39%
 Python 3 0.120.18?573  6% 24% 31% 17%
 Python 3 #5 0.050.05?524  100% 0% 0% 0%
 Python 2 0.100.16?612  13% 6% 31% 18%
 Python development version #5 0.050.05?524  0% 0% 0% 75%
 Python development version 0.110.07?573  29% 29% 86% 38%
 Cython #5 0.050.05?524  50% 50% 0% 0%
 Nuitka 0.140.19?573  53% 11% 10% 11%
 Cython #5 0.050.05?524  100% 0% 20% 0%
 Pyston #5 0.080.08?501  0% 0% 0% 100%
 Pyston #5 0.080.08?501  13% 0% 0% 100%
 Python 2 0.090.16?612  31% 13% 7% 13%
 Cython #5 0.050.05?524  83% 0% 0% 0%
 PyPy 2 #5 0.180.19?501  78% 84% 58% 43%
 Python 2 #5 0.030.03?501  0% 0% 100% 0%
 PyPy 2 #5 0.140.15?501  0% 100% 7% 7%
 Python 2 #5 0.030.03?501  0% 100% 0% 0%
 Python 2 #5 0.030.04?501  0% 0% 25% 100%
 PyPy 2 #5 0.210.21848501  96% 85% 62% 50%
 PyPy 2 0.380.331,044612  39% 88% 28% 25%
 PyPy 3 #5 0.230.231,112524  0% 9% 4% 100%
 PyPy 3 #5 0.220.221,116524  5% 9% 5% 100%
 PyPy 3 #5 0.240.261,116524  4% 21% 8% 92%
 PyPy 2 0.400.331,120612  24% 24% 79% 27%
 PyPy 2 0.410.341,144612  31% 26% 85% 21%
 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 0.570.5470,224573  8% 15% 13% 85%
 PyPy 3 0.560.5270,456573  6% 15% 87% 8%
 PyPy 3 0.580.5470,536573  18% 19% 87% 11%
 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%
missing benchmark programs
Shedskin No program
Numba No program
MicroPython No program
Grumpy No program
Graal No program
RustPython 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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