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%
1.1PyPy 2 0.070.07?593  0% 13% 0% 100%
1.1PyPy 2 0.070.07?593  88% 0% 0% 0%
1.2PyPy 2 0.080.08?593  0% 88% 89% 0%
1.5PyPy 3 0.100.10?594  91% 10% 0% 0%
1.5PyPy 3 0.100.10?594  0% 100% 9% 0%
1.6PyPy 3 0.100.11?594  0% 60% 50% 0%
1.6Python 2 0.100.11?593  0% 100% 0% 0%
1.8Python 2 0.120.12?593  100% 17% 0% 0%
2.0Python 2 0.130.13?593  8% 15% 100% 8%
2.1Python 2 #8 0.270.14?777  50% 71% 46% 57%
2.1Python 2 #2 0.080.14?801  27% 13% 21% 21%
2.1Nuitka 0.140.14?594  0% 0% 100% 7%
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%
2.2Python 3 0.140.15?594  0% 100% 0% 0%
2.2Python 3 0.140.15?594  0% 0% 0% 100%
2.3Python 2 #2 0.070.15?801  40% 19% 7% 31%
2.3Nuitka 0.150.15?594  20% 7% 7% 100%
2.3Python 3 0.150.15?594  0% 94% 13% 0%
2.3Python 2 #8 0.280.15?777  43% 47% 47% 65%
2.4Pyston 0.160.16?593  56% 0% 0% 50%
2.5Python 3 #8 0.310.17?777  47% 69% 56% 29%
2.5Python 3 #8 0.330.17?777  44% 44% 75% 39%
2.5Python 3 #8 0.320.17?777  35% 44% 38% 76%
2.5Python 3 #3 0.250.17?2011  25% 33% 25% 59%
2.6Python 3 #2 0.120.17?801  19% 6% 18% 41%
2.6Cython 0.170.17?594  35% 94% 18% 21%
2.6Python 3 #2 0.100.17?801  18% 44% 6% 0%
2.6Python development version #8 0.400.17?777  56% 44% 88% 56%
2.6Python 3 #2 0.100.17?801  24% 13% 13% 17%
2.6Cython #2 0.110.17?801  6% 38% 12% 6%
2.6Python 3 #3 0.250.17?2011  29% 61% 35% 28%
2.6Python development version #8 0.370.17?777  59% 78% 56% 44%
2.7Python development version #3 0.250.18?2011  65% 39% 33% 39%
2.7Python 3 #3 0.260.18?2011  50% 53% 29% 29%
2.7Python development version #3 0.300.18?2011  68% 41% 29% 33%
2.7Cython #2 0.100.18?801  44% 17% 5% 6%
2.7Nuitka #2 0.100.18?801  6% 6% 44% 0%
2.7Nuitka #3 0.260.18?2011  29% 28% 28% 63%
2.7Cython 0.180.18?594  13% 18% 100% 21%
2.7PyPy 2 #8 0.240.18?777  33% 59% 33% 18%
2.7Nuitka #8 0.290.18?777  45% 74% 24% 28%
2.8Nuitka #8 0.300.18?777  35% 78% 32% 24%
2.8Nuitka #3 0.260.18?2011  28% 28% 63% 29%
2.8Python development version #8 0.420.18?777  47% 83% 59% 50%
2.8PyPy 2 #8 0.250.18?777  72% 32% 29% 26%
2.8Cython #2 0.130.18?801  17% 0% 22% 37%
2.8Nuitka #8 0.310.18?777  42% 37% 67% 33%
2.8PyPy 2 #8 0.250.19?777  32% 61% 24% 22%
2.8Cython 0.180.19?594  30% 22% 100% 21%
2.9PyPy 2 #2 0.120.19?801  11% 47% 15% 6%
2.9Nuitka #3 0.270.19?2011  44% 68% 29% 26%
2.9Python development version #3 0.290.19?2011  63% 59% 37% 35%
2.9Nuitka #2 0.140.19?801  42% 21% 0% 25%
2.9Nuitka #2 0.130.19?801  55% 19% 21% 20%
2.9PyPy 2 #2 0.120.19?801  10% 5% 45% 15%
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%
3.2PyPy 2 #2 0.140.211,476801  10% 14% 48% 14%
3.2PyPy 3 #8 0.330.211,376777  25% 43% 68% 38%
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%
3.3Nuitka 0.210.221,760594  55% 82% 57% 57%
3.3PyPy 3 #8 0.330.221,432777  67% 43% 24% 41%
3.3PyPy 3 #8 0.360.221,432777  43% 45% 55% 95%
3.5Python development version 0.220.234,908594  29% 24% 30% 100%
3.5PyPy 3 #2 0.180.23178,540801  27% 67% 24% 14%
3.5PyPy 3 #2 0.180.23178,108801  65% 33% 25% 30%
3.6PyPy 3 #2 0.180.24178,236801  25% 17% 32% 60%
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%
30IronPython 1.631.9794,392593  2% 5% 3% 77%
30IronPython 1.641.98100,084593  18% 49% 2% 19%
30IronPython 1.581.9898,324593  39% 6% 37% 3%
40Jython 6.502.66270,864593  49% 58% 64% 78%
41Jython 7.392.73279,848593  57% 59% 79% 81%
42Jython 7.302.77281,100593  79% 65% 53% 78%
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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