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% 20% 0% 100%
1.0Shedskin 0.060.05?593  0% 0% 0% 100%
1.0Shedskin 0.060.05?593  0% 40% 67% 20%
1.5Python 2 0.070.08?593  0% 100% 0% 0%
1.6Python 2 0.080.08?593  0% 11% 100% 0%
1.9Cython 0.090.09?618  0% 0% 100% 0%
1.9Cython 0.090.09?618  0% 0% 0% 100%
1.9Cython 0.090.09?618  0% 0% 0% 100%
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%
2.3PyPy 2 0.110.12?593  100% 0% 0% 0%
2.3Python 3 0.110.12?594  100% 0% 0% 0%
2.4Python 3 0.110.12?594  0% 0% 0% 92%
2.4PyPy 2 0.120.12?593  0% 9% 100% 0%
2.4Python 3 0.120.12?594  15% 0% 100% 8%
2.5PyPy 3 0.120.12?594  0% 0% 8% 100%
2.5Python development version 0.120.12?594  0% 0% 100% 0%
2.5PyPy 3 0.120.12?594  8% 15% 8% 100%
2.5Python development version 0.120.12?594  8% 0% 100% 8%
2.5Pyston 0.120.12?593  0% 0% 0% 92%
2.6PyPy 2 0.130.13?593  100% 8% 0% 15%
2.7PyPy 3 0.130.13?594  0% 0% 0% 100%
2.9Python 2 #8 0.180.14?777  29% 50% 21% 21%
2.9Python 2 #8 0.190.14?777  27% 27% 29% 43%
3.0Nuitka 0.140.15?594  29% 100% 33% 33%
3.0Nuitka 0.150.15?594  33% 100% 29% 40%
3.1Python development version 0.150.15?594  25% 80% 0% 0%
3.1Nuitka 0.150.15?594  44% 53% 44% 87%
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%
3.6Python 3 #3 0.210.18?2011  22% 44% 28% 29%
3.6Cython #2 0.100.18?801  24% 17% 17% 0%
3.7Python development version #3 0.230.18?2011  26% 61% 26% 22%
3.7Nuitka #3 0.210.18?2011  40% 22% 61% 22%
3.8Python development version #8 0.300.19?777  32% 32% 65% 39%
3.8Python 3 #8 0.270.19?777  68% 32% 24% 26%
3.8Nuitka #8 0.250.19?777  39% 47% 53% 74%
3.8Nuitka #8 0.250.19?777  22% 42% 26% 56%
3.8Python 3 #2 0.120.19?801  0% 50% 11% 0%
3.8Python 3 #8 0.280.19?777  37% 32% 58% 33%
3.9Python 3 #8 0.270.19?777  26% 68% 26% 26%
3.9Python 3 #3 0.220.19?2011  17% 17% 53% 30%
3.9Python 3 #2 0.120.19?801  10% 42% 16% 15%
3.9Nuitka #3 0.220.19?2011  50% 35% 21% 17%
3.9Python 3 #3 0.220.19?2011  17% 53% 32% 17%
3.9Python 3 #2 0.120.19?801  11% 15% 10% 42%
4.0Python development version #3 0.240.19?2011  65% 22% 28% 38%
4.0Nuitka #3 0.220.19?2011  17% 24% 21% 65%
4.1Cython #2 0.120.20?801  16% 10% 42% 10%
4.1Nuitka #8 0.260.20?777  40% 40% 38% 70%
4.2Cython #2 0.130.20?801  10% 43% 10% 11%
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%
4.6Python development version #2 0.160.22832801  5% 18% 14% 45%
4.8Nuitka #2 0.160.23?801  36% 44% 43% 76%
4.9Nuitka #2 0.170.24?801  54% 56% 54% 68%
5.0Pyston #8 0.270.24116,440777  67% 16% 17% 17%
5.0Nuitka #2 0.160.243,296801  32% 52% 40% 21%
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 #3 0.630.5468,7082011  15% 15% 19% 89%
11PyPy 3 #3 0.620.5468,0682011  11% 16% 87% 18%
11PyPy 3 #3 0.640.5468,3562011  18% 91% 25% 22%
12PyPy 3 #2 0.550.6067,980801  20% 17% 83% 19%
13PyPy 3 #2 0.580.6470,480801  87% 21% 22% 30%
16PyPy 3 #8 0.820.7670,076777  91% 49% 61% 61%
16PyPy 3 #8 0.840.7870,588777  56% 65% 65% 94%
16PyPy 3 #8 0.820.7970,140777  96% 56% 65% 65%
18PyPy 3 #2 0.770.8970,340801  83% 97% 90% 90%
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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