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%
1.4Python development version #2 0.090.07?801  0% 43% 25% 71%
1.5Python development version #2 0.100.07?801  25% 29% 13% 71%
1.5Python development version #2 0.100.07?801  14% 13% 86% 38%
1.6Python 2 0.080.08?593  0% 0% 0% 100%
1.6Python 2 0.080.08?593  0% 0% 0% 100%
1.7Python 2 0.080.08?593  100% 0% 0% 11%
1.9Cython 0.090.09?618  11% 0% 0% 100%
1.9Python development version #3 0.200.09?2011  89% 56% 44% 44%
1.9Python development version #3 0.200.09?2011  38% 44% 56% 82%
1.9Cython 0.090.09?618  0% 100% 0% 0%
1.9Cython 0.090.09?618  10% 11% 100% 0%
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  100% 9% 0% 9%
2.2Nuitka 0.110.11?594  100% 9% 0% 8%
2.2Nuitka 0.110.11?594  8% 0% 0% 100%
2.3PyPy 2 0.110.11?593  100% 9% 9% 0%
2.3Python development version #3 0.220.11?2011  36% 92% 42% 36%
2.3Python development version #8 0.270.11?777  45% 50% 45% 100%
2.3Python development version #8 0.270.11?777  58% 50% 45% 83%
2.3PyPy 2 0.110.12?593  0% 100% 0% 0%
2.3Python development version #8 0.270.12?777  40% 55% 50% 82%
2.4Python 3 0.120.12?594  8% 100% 0% 8%
2.5Python development version 0.120.12?594  0% 14% 100% 0%
2.5Python development version 0.120.12?594  0% 8% 100% 8%
2.5Pyston 0.120.12?593  0% 0% 0% 92%
2.6Python 3 0.120.13?594  100% 8% 14% 8%
2.6Python 3 0.130.13?594  8% 21% 83% 8%
2.8PyPy 2 0.130.14?593  100% 7% 0% 0%
2.8PyPy 3 0.140.14?594  100% 0% 7% 0%
2.8Python development version 0.140.14?594  7% 8% 7% 93%
2.9PyPy 3 0.140.14?594  77% 0% 23% 7%
2.9Python 2 #2 0.070.14?801  8% 21% 20% 13%
3.0Python 2 #8 0.190.15?777  50% 33% 29% 29%
3.0Python 2 #8 0.190.15?777  33% 33% 40% 50%
3.1Python 2 #8 0.190.15?777  31% 21% 33% 53%
3.2Python 3 #8 0.240.16?777  29% 50% 38% 33%
3.3Python 2 #2 0.100.16?801  12% 38% 7% 13%
3.3Python 3 #8 0.240.16?777  35% 56% 41% 33%
3.3Python 3 #3 0.190.16?2011  47% 25% 33% 53%
3.4Python 2 #2 0.100.17?801  6% 39% 12% 13%
3.4Nuitka #8 0.230.17?777  31% 59% 31% 25%
3.4Python 3 #8 0.250.17?777  25% 31% 67% 35%
3.5Nuitka #8 0.230.17?777  65% 29% 24% 28%
3.5PyPy 3 0.160.17?594  6% 6% 0% 88%
3.6Nuitka #3 0.200.17?2011  29% 28% 50% 22%
3.6Python 3 #3 0.190.18?2011  58% 37% 26% 18%
3.6Nuitka #8 0.250.18?777  24% 35% 25% 61%
3.6Nuitka #2 0.110.18?801  33% 12% 22% 0%
3.7Python 3 #3 0.200.18?2011  56% 22% 24% 18%
3.8Python 3 #2 0.120.19?801  11% 11% 39% 16%
3.9Nuitka #2 0.120.19?801  5% 11% 11% 45%
3.9Nuitka #3 0.220.19?2011  32% 21% 50% 25%
4.0Nuitka #3 0.220.19?2011  32% 25% 17% 50%
4.0Python 3 #2 0.120.19?801  45% 11% 5% 6%
4.0Nuitka #2 0.120.20?801  5% 26% 32% 0%
4.4Python 3 #2 0.150.221,812801  33% 41% 33% 23%
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%
6.0PyPy 2 #8 0.350.30222,376777  17% 23% 17% 77%
6.1PyPy 2 #2 0.260.30247,864801  65% 19% 10% 7%
6.2PyPy 2 #8 0.330.30215,716777  17% 66% 16% 16%
6.2PyPy 2 #2 0.250.31249,736801  30% 38% 10% 10%
6.3PyPy 2 #8 0.360.31216,176777  75% 17% 13% 20%
6.4PyPy 2 #2 0.260.31249,312801  13% 65% 9% 9%
7.2Pyston #8 0.300.354777  21% 19% 63% 21%
11PyPy 3 #3 0.630.5270,0242011  87% 19% 12% 12%
11PyPy 3 #3 0.620.5270,8722011  12% 87% 13% 12%
11PyPy 3 #3 0.630.5369,8362011  13% 15% 87% 19%
11PyPy 3 #8 0.620.5370,264777  15% 18% 12% 87%
11PyPy 3 #2 0.490.5470,388801  81% 7% 6% 9%
11PyPy 3 #2 0.490.5470,568801  6% 4% 13% 81%
11PyPy 3 #8 0.630.5470,152777  13% 87% 13% 15%
11PyPy 3 #2 0.480.5470,100801  60% 9% 25% 7%
12PyPy 3 #8 0.640.5770,300777  69% 14% 11% 28%
21Graal 1.541.01320,944594  37% 22% 12% 89%
21Graal 1.551.01392,412594  41% 14% 28% 88%
21Graal 1.541.01390,844594  13% 49% 89% 15%
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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