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 0.060.05?593  0% 40% 67% 20%
1.0Shedskin 0.060.05?593  0% 0% 0% 100%
1.0Shedskin 0.060.05?593  0% 20% 0% 100%
1.5Python 2 0.070.08?593  0% 100% 0% 0%
1.6Python 2 0.080.08?593  0% 11% 100% 0%
1.8Cython 0.090.09?618  0% 0% 10% 100%
1.8Cython 0.090.09?618  0% 100% 10% 10%
1.8Cython 0.090.09?618  0% 0% 100% 0%
1.9Python 2 0.090.09?593  0% 10% 0% 89%
2.0Pyston 0.100.10?593  100% 0% 0% 0%
2.0Pyston 0.100.10?593  22% 9% 80% 10%
2.2Nuitka 0.110.11?594  0% 0% 100% 0%
2.2Nuitka 0.110.11?594  0% 0% 100% 0%
2.2Nuitka 0.110.11?594  9% 9% 100% 0%
2.3Python 3 0.110.11?594  0% 100% 0% 0%
2.3Python 3 0.110.11?594  92% 0% 0% 0%
2.3PyPy 2 0.110.12?593  100% 0% 0% 0%
2.4PyPy 2 0.120.12?593  0% 9% 100% 0%
2.5Python development version 0.120.12?594  0% 0% 100% 0%
2.5Python development version 0.120.12?594  8% 0% 100% 8%
2.5Pyston 0.120.12?593  0% 0% 0% 92%
2.6PyPy 3 0.130.13?594  100% 79% 0% 8%
2.6PyPy 2 0.130.13?593  100% 8% 0% 15%
2.7Python 3 0.130.13?594  0% 0% 0% 93%
2.9Python 2 #8 0.180.14?777  29% 50% 21% 21%
2.9PyPy 3 0.140.14?594  60% 100% 0% 44%
2.9Python 2 #8 0.190.14?777  27% 27% 29% 43%
3.0PyPy 3 0.140.15?594  7% 43% 21% 100%
3.1Python development version 0.150.15?594  25% 80% 0% 0%
3.1Python 2 #8 0.210.15?777  36% 40% 27% 31%
3.2Python 2 #2 0.080.16?801  24% 19% 38% 13%
3.3Python 2 #2 0.090.16?801  13% 13% 22% 44%
3.4Python development version #3 0.220.17?2011  24% 19% 56% 29%
3.4Python 2 #2 0.090.17?801  6% 31% 12% 6%
3.4Python development version #8 0.290.17?777  39% 41% 76% 35%
3.5Python development version #8 0.290.17?777  47% 44% 67% 31%
3.6Nuitka #2 0.100.18?801  11% 16% 41% 28%
3.6Python 3 #8 0.260.18?777  41% 29% 25% 69%
3.7Nuitka #2 0.110.18?801  0% 6% 37% 12%
3.7Nuitka #8 0.250.18?777  24% 32% 67% 22%
3.7Python development version #3 0.230.18?2011  26% 61% 26% 22%
3.7Nuitka #3 0.210.18?2011  61% 22% 17% 21%
3.7Nuitka #8 0.240.18?777  61% 22% 29% 26%
3.7Python 3 #3 0.210.18?2011  50% 22% 17% 33%
3.7Python 3 #2 0.120.18?801  33% 33% 12% 0%
3.8Nuitka #3 0.210.18?2011  21% 53% 25% 32%
3.8Python development version #8 0.300.19?777  32% 32% 65% 39%
3.9Python 3 #3 0.220.19?2011  30% 20% 21% 47%
3.9Cython #2 0.110.19?801  6% 5% 33% 17%
3.9Python 3 #8 0.270.19?777  39% 37% 65% 30%
3.9Python 3 #2 0.120.19?801  53% 5% 5% 11%
3.9Nuitka #8 0.240.19?777  21% 26% 32% 63%
3.9Cython #2 0.120.19?801  37% 15% 11% 12%
3.9Python 3 #3 0.220.19?2011  53% 30% 20% 25%
3.9Python 3 #8 0.270.19?777  22% 28% 30% 61%
4.0Python development version #3 0.240.19?2011  65% 22% 28% 38%
4.1Cython #2 0.150.20?801  55% 22% 25% 15%
4.1Python 3 #2 0.130.20?801  14% 6% 11% 45%
4.3Python development version #2 0.140.21?801  38% 48% 50% 43%
4.3Python development version #2 0.110.214801  19% 45% 14% 27%
4.5Nuitka #3 0.230.221,7602011  26% 27% 18% 59%
4.5Nuitka #2 0.150.221,752801  10% 27% 41% 23%
4.6Python development version #2 0.160.22832801  5% 18% 14% 45%
5.0Pyston #8 0.270.24116,440777  67% 16% 17% 17%
5.0Pyston #8 0.280.25117,132777  69% 16% 13% 13%
5.2Pyston #2 0.190.25125,780801  31% 16% 16% 58%
5.2Pyston #2 0.200.25123,824801  12% 12% 56% 12%
5.2Pyston #2 0.190.26128,384801  12% 15% 8% 58%
6.5PyPy 2 #8 0.330.32205,516777  15% 75% 10% 10%
6.7PyPy 2 #2 0.260.3390,604801  15% 9% 63% 6%
6.7PyPy 2 #2 0.260.331,324801  6% 69% 3% 13%
6.9PyPy 2 #8 0.350.341,448777  76% 25% 21% 15%
7.0PyPy 2 #2 0.290.341,644801  54% 15% 24% 36%
7.2Pyston #8 0.300.354777  21% 19% 63% 21%
7.3PyPy 2 #8 0.370.361,592777  72% 17% 46% 14%
11PyPy 3 #8 0.570.5369,356777  11% 83% 11% 8%
11PyPy 3 #3 0.630.5368,4962011  87% 11% 11% 13%
11PyPy 3 #3 0.620.5368,3522011  9% 11% 10% 88%
11PyPy 3 #3 0.630.5467,8002011  87% 9% 11% 13%
11PyPy 3 #2 0.510.5568,336801  5% 5% 80% 5%
11PyPy 3 #2 0.510.5567,944801  5% 5% 4% 80%
11PyPy 3 #8 0.600.5667,752777  13% 86% 14% 19%
12PyPy 3 #2 0.540.5968,468801  2% 7% 81% 5%
12PyPy 3 #8 0.660.6068,252777  74% 29% 15% 25%
32IronPython 1.131.5578,304593  3% 3% 52% 23%
32IronPython 1.151.5780,896593  56% 4% 16% 3%
32IronPython 1.201.5971,668593  34% 11% 3% 39%
33Jython 3.851.61188,480593  81% 45% 63% 59%
34Jython 3.951.66197,900593  66% 50% 61% 77%
37Jython 4.271.80198,752593  67% 70% 65% 68%
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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