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% 20% 0% 100%
1.0Shedskin 0.060.05?593  0% 0% 0% 100%
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  0% 100% 0% 0%
1.9Cython 0.090.10?618  0% 91% 0% 0%
2.0Pyston 0.100.10?593  100% 0% 0% 0%
2.0Pyston 0.100.10?593  22% 9% 80% 10%
2.2PyPy 2 0.110.11?593  17% 0% 0% 100%
2.2Nuitka 0.100.11?594  100% 9% 0% 0%
2.2PyPy 2 0.110.11?593  0% 100% 9% 0%
2.2Nuitka 0.110.11?594  15% 15% 0% 100%
2.2Nuitka 0.110.11?594  0% 0% 0% 91%
2.2Cython 0.100.11?618  27% 80% 27% 18%
2.3PyPy 3 0.110.11?594  0% 92% 0% 0%
2.3PyPy 3 0.110.11?594  9% 0% 0% 100%
2.3Python development version 0.110.11?594  0% 100% 8% 8%
2.3Python development version 0.110.11?594  0% 8% 100% 0%
2.4Python 3 0.120.12?594  8% 100% 0% 8%
2.5Python development version 0.120.12?594  23% 77% 0% 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%
2.7PyPy 3 0.130.13?594  8% 0% 100% 0%
2.9Python 2 #2 0.070.14?801  8% 21% 20% 13%
2.9PyPy 2 0.120.14?593  7% 53% 31% 0%
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 development version #8 0.240.16?777  50% 50% 33% 31%
3.2Nuitka #8 0.230.16?777  38% 31% 73% 35%
3.2Python 3 #8 0.240.16?777  29% 50% 38% 33%
3.3Python development version #8 0.250.16?777  31% 31% 63% 35%
3.3Python development version #2 0.090.16?801  6% 13% 19% 25%
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%
3.3Nuitka #8 0.230.16?777  59% 35% 29% 25%
3.4Python 2 #2 0.100.17?801  6% 39% 12% 13%
3.4Cython #2 0.100.17?801  50% 35% 44% 63%
3.4Python 3 #8 0.250.17?777  25% 31% 67% 35%
3.5Nuitka #8 0.230.17?777  29% 33% 25% 65%
3.5Python development version #8 0.250.17?777  29% 28% 59% 29%
3.5Nuitka #3 0.200.17?2011  56% 25% 24% 22%
3.6Nuitka #3 0.200.17?2011  19% 19% 33% 47%
3.6Python 3 #3 0.190.18?2011  58% 37% 26% 18%
3.6Nuitka #2 0.110.18?801  29% 26% 12% 16%
3.7Python 3 #3 0.200.18?2011  56% 22% 24% 18%
3.7Nuitka #2 0.110.18?801  6% 21% 16% 39%
3.7Cython #2 0.120.18?801  21% 18% 44% 17%
3.7Nuitka #3 0.210.18?2011  30% 33% 21% 56%
3.8Python development version #3 0.210.19?2011  59% 26% 21% 17%
3.8Python development version #2 0.120.19?801  25% 39% 16% 17%
3.8Python development version #3 0.220.19?2011  37% 47% 21% 17%
3.8Python 3 #2 0.120.19?801  11% 11% 39% 16%
3.8Python development version #3 0.220.19?2011  55% 21% 16% 26%
4.0Python development version #2 0.130.19?801  11% 15% 5% 50%
4.0Python 3 #2 0.120.19?801  45% 11% 5% 6%
4.0Nuitka #2 0.130.20?801  14% 25% 17% 45%
4.1Cython #2 0.140.20?801  24% 48% 20% 10%
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%
5.7PyPy 2 #8 0.320.28218,136777  24% 71% 21% 19%
6.0PyPy 2 #8 0.340.30216,756777  23% 17% 72% 20%
6.1PyPy 2 #2 0.270.30245,568801  13% 10% 10% 67%
6.2PyPy 2 #8 0.350.31219,220777  17% 19% 74% 13%
6.3PyPy 2 #2 0.280.31244,904801  16% 7% 66% 10%
6.5PyPy 2 #2 0.280.32244,404801  66% 15% 10% 9%
7.2Pyston #8 0.300.354777  21% 19% 63% 21%
9.9PyPy 3 #3 0.530.4870,9202011  15% 82% 10% 10%
10PyPy 3 #8 0.540.4971,300777  17% 83% 6% 10%
10PyPy 3 #8 0.540.5071,260777  88% 10% 10% 8%
10PyPy 3 #2 0.460.5070,732801  8% 10% 6% 78%
10PyPy 3 #8 0.560.5071,592777  13% 6% 86% 12%
10PyPy 3 #3 0.550.5070,2682011  14% 10% 84% 10%
10PyPy 3 #2 0.470.5171,444801  10% 8% 6% 78%
11PyPy 3 #2 0.480.5271,132801  8% 4% 79% 6%
11PyPy 3 #3 0.540.5270,1602011  87% 10% 8% 11%
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
Graal 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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