k-nucleotide benchmark N=10,000

Each chart bar shows how many times slower, one ↓ k-nucleotide program was, compared to the fastest program.

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.

    sort sortsort
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0Shedskin 0.080.07?593  17% 0% 0% 100%
1.0Shedskin 0.080.07?593  0% 14% 14% 100%
1.0Shedskin 0.090.07?593  86% 14% 14% 25%
1.6Python 2 0.100.11?593  0% 100% 0% 0%
1.8Cython 0.120.12?594  8% 0% 100% 0%
1.8Python 2 0.120.12?593  100% 17% 0% 0%
1.9Cython 0.120.12?594  100% 0% 0% 0%
2.0Cython 0.130.13?594  14% 100% 0% 15%
2.0Python 2 0.130.13?593  8% 15% 100% 8%
2.1Python 2 #8 0.270.14?777  50% 71% 46% 57%
2.1Nuitka 0.140.14?594  0% 100% 0% 0%
2.1Nuitka 0.140.14?594  0% 7% 0% 100%
2.1Python 2 #2 0.080.14?801  27% 13% 21% 21%
2.1Python 2 #2 0.070.14?801  7% 23% 33% 15%
2.2Pyston 0.140.14?593  0% 0% 100% 0%
2.2Python 2 #8 0.270.14?777  47% 69% 36% 50%
2.2Pyston 0.140.14?593  0% 0% 0% 100%
2.2Nuitka 0.140.14?594  6% 7% 7% 94%
2.2PyPy 2 0.140.15?593  0% 0% 0% 93%
2.2PyPy 2 0.150.15?593  100% 0% 0% 0%
2.2PyPy 2 0.140.15?593  33% 0% 0% 67%
2.2Python 3 0.150.15?594  0% 0% 6% 100%
2.3Python 2 #2 0.070.15?801  40% 19% 7% 31%
2.3Python 3 0.150.15?594  100% 0% 0% 0%
2.3Python 3 0.150.15?594  0% 100% 0% 0%
2.3Python 2 #8 0.280.15?777  43% 47% 47% 65%
2.3PyPy 3 0.150.15?594  6% 0% 0% 100%
2.4Pyston 0.160.16?593  56% 0% 0% 50%
2.4PyPy 3 0.160.16?594  0% 0% 100% 0%
2.6Python development version #8 0.400.17?777  56% 44% 88% 56%
2.6PyPy 3 0.170.17?594  0% 6% 6% 94%
2.6Python 3 #3 0.270.17?2011  39% 29% 61% 29%
2.6Python 3 #8 0.360.17?777  44% 82% 38% 50%
2.6Python development version #8 0.370.17?777  59% 78% 56% 44%
2.6Python 3 #2 0.140.17?801  56% 6% 17% 6%
2.7Python development version #3 0.250.18?2011  65% 39% 33% 39%
2.7Cython #2 0.110.18?801  12% 50% 6% 0%
2.7Python 3 #3 0.270.18?2011  29% 65% 29% 29%
2.7Python 3 #8 0.360.18?777  38% 39% 47% 78%
2.7Python 3 #2 0.120.18?801  6% 17% 11% 39%
2.7Cython #2 0.130.18?801  20% 6% 18% 39%
2.7Python 3 #2 0.120.18?801  11% 11% 47% 0%
2.7Python 3 #3 0.280.18?2011  33% 63% 35% 29%
2.7Python development version #3 0.300.18?2011  68% 41% 29% 33%
2.7Python 3 #8 0.360.18?777  88% 41% 35% 39%
2.7Cython #2 0.110.18?801  29% 22% 11% 6%
2.8Nuitka #8 0.350.18?777  79% 44% 39% 37%
2.8Python development version #8 0.420.18?777  47% 83% 59% 50%
2.8Nuitka #8 0.340.18?777  83% 37% 37% 37%
2.8Nuitka #3 0.280.19?2011  32% 37% 32% 65%
2.8Nuitka #3 0.280.19?2011  28% 33% 22% 63%
2.8Nuitka #8 0.330.19?777  33% 39% 35% 67%
2.8Nuitka #3 0.280.19?2011  26% 74% 28% 32%
2.8Nuitka #2 0.140.19?801  40% 16% 5% 25%
2.8Nuitka #2 0.120.19?801  11% 11% 42% 6%
2.9Python development version #3 0.290.19?2011  63% 59% 37% 35%
2.9Nuitka #2 0.120.19?801  53% 0% 5% 10%
3.0Python development version #2 0.140.20?801  63% 43% 25% 30%
3.1Python development version 0.200.20?594  29% 100% 20% 16%
3.3Python development version #2 0.190.221,684801  43% 30% 67% 33%
3.3Python development version #2 0.160.221,664801  57% 29% 52% 62%
3.3Python development version 0.220.221,704594  27% 26% 14% 100%
3.5Python development version 0.220.234,908594  29% 24% 30% 100%
4.4Pyston #2 0.240.29132,088801  3% 13% 67% 18%
4.5Pyston #8 0.470.30150,224777  28% 30% 50% 59%
4.5Pyston #8 0.380.30133,804777  26% 16% 20% 80%
4.6Pyston #2 0.260.30141,832801  65% 17% 7% 16%
4.6Pyston #2 0.250.30132,320801  13% 67% 7% 10%
4.7Pyston #8 0.380.31121,700777  20% 81% 23% 17%
5.1PyPy 2 #8 0.420.341,552777  18% 15% 81% 15%
5.1PyPy 2 #8 0.420.341,612777  79% 17% 18% 12%
5.2PyPy 2 #2 0.300.341,568801  71% 9% 6% 9%
5.2PyPy 2 #8 0.420.341,580777  80% 12% 18% 15%
5.2PyPy 2 #2 0.310.341,608801  74% 9% 6% 6%
5.3PyPy 2 #2 0.310.351,572801  71% 6% 9% 6%
9.4PyPy 3 #8 0.760.6270,572777  11% 13% 11% 92%
9.5PyPy 3 #8 0.780.6370,412777  11% 10% 11% 90%
9.6PyPy 3 #8 0.770.6368,668777  14% 11% 94% 22%
9.6PyPy 3 #2 0.610.6370,652801  5% 3% 84% 5%
9.6PyPy 3 #2 0.600.6369,996801  86% 5% 5% 2%
9.6PyPy 3 #3 0.730.6471,1122011  91% 11% 8% 14%
9.8PyPy 3 #3 0.740.6569,1042011  11% 90% 9% 9%
9.9PyPy 3 #3 0.740.6570,2962011  35% 77% 9% 8%
10PyPy 3 #2 0.640.6669,100801  15% 86% 5% 6%
25IronPython 1.351.6475,996593  77% 2% 2% 2%
25IronPython 1.361.6675,308593  11% 68% 4% 1%
25IronPython 1.371.6670,332593  3% 1% 1% 79%
52Jython 8.013.46264,928593  70% 55% 64% 63%
54Jython 8.303.54277,160593  73% 57% 62% 59%
54Jython 8.413.57276,560593  59% 83% 63% 46%
missing benchmark programs
Numba No program
MicroPython No program
Grumpy No program

 k-nucleotide benchmark : Hashtable update and k-nucleotide strings

diff program output for this 250KB input file (generated with the fasta program N = 25000) 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

In practice, less brute-force would be used to calculate k-nucleotide frequencies, for example Virus Classification using k-nucleotide Frequencies and A Fast Algorithm for the Exhaustive Analysis of 12-Nucleotide-Long DNA Sequences. Applications to Human Genomics (105KB pdf).

Revised BSD license

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