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% 0% 0% 100%
1.0Shedskin 0.060.05?593  0% 40% 67% 20%
1.0Shedskin 0.060.05?593  0% 20% 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% 0% 100% 0%
2.0Pyston 0.100.10?593  100% 0% 0% 0%
2.0Pyston 0.100.10?593  22% 9% 80% 10%
2.2Cython 0.110.11?618  22% 10% 100% 18%
2.2Nuitka 0.110.11?594  9% 0% 0% 100%
2.2Nuitka 0.110.11?594  18% 100% 17% 17%
2.2PyPy 2 0.110.11?593  100% 0% 0% 0%
2.2Nuitka 0.110.11?594  8% 0% 100% 0%
2.3PyPy 2 0.110.11?593  0% 100% 0% 0%
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%
2.4Cython 0.120.12?618  15% 58% 8% 100%
2.5Python development version 0.120.12?594  23% 77% 0% 8%
2.5Pyston 0.120.12?593  0% 0% 0% 92%
2.5PyPy 2 0.110.12?593  91% 7% 8% 8%
2.6Python 3 0.120.13?594  100% 8% 14% 8%
2.6Python 3 0.130.13?594  8% 21% 83% 8%
2.9Python 2 #2 0.070.14?801  8% 21% 20% 13%
2.9PyPy 3 0.140.14?594  7% 100% 0% 13%
2.9PyPy 3 0.140.14?594  100% 7% 0% 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%
3.2PyPy 3 0.150.16?594  0% 100% 0% 0%
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%
3.4Python 2 #2 0.100.17?801  6% 39% 12% 13%
3.4Python 3 #8 0.250.17?777  25% 31% 67% 35%
3.5Python development version #8 0.250.17?777  29% 28% 59% 29%
3.5Nuitka #3 0.200.17?2011  24% 22% 19% 56%
3.6Python 3 #3 0.190.18?2011  58% 37% 26% 18%
3.6Nuitka #8 0.250.18?777  35% 38% 25% 56%
3.6Nuitka #2 0.100.18?801  24% 47% 32% 12%
3.6Nuitka #2 0.110.18?801  26% 47% 17% 11%
3.7Python 3 #3 0.200.18?2011  56% 22% 24% 18%
3.7Nuitka #8 0.250.18?777  35% 28% 29% 61%
3.8Nuitka #8 0.260.19?777  42% 33% 28% 61%
3.8Python development version #3 0.210.19?2011  59% 26% 21% 17%
3.8Python 3 #2 0.120.19?801  11% 11% 39% 16%
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%
3.8Nuitka #3 0.220.19?2011  26% 17% 21% 63%
3.8Python development version #3 0.220.19?2011  55% 21% 16% 26%
3.9Nuitka #3 0.220.19?2011  22% 25% 28% 50%
4.0Python 3 #2 0.120.19?801  45% 11% 5% 6%
4.0Python development version #2 0.130.19?801  11% 15% 5% 50%
4.1Nuitka #2 0.130.20?801  11% 0% 20% 43%
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%
5.8PyPy 2 #8 0.330.28221,564777  72% 15% 18% 18%
5.8PyPy 2 #8 0.310.29218,144777  72% 32% 21% 21%
5.9PyPy 2 #2 0.240.29245,392801  68% 7% 10% 24%
6.1PyPy 2 #2 0.250.30243,644801  7% 10% 61% 10%
6.2PyPy 2 #8 0.340.30217,500777  20% 19% 20% 74%
6.5PyPy 2 #2 0.280.32244,632801  68% 22% 31% 30%
7.2Pyston #8 0.300.354777  21% 19% 63% 21%
11PyPy 3 #8 0.600.5269,028777  90% 12% 16% 11%
11PyPy 3 #3 0.590.5269,2242011  12% 11% 10% 89%
11PyPy 3 #8 0.600.5368,932777  88% 13% 15% 13%
11PyPy 3 #3 0.620.5369,9042011  88% 15% 20% 19%
11PyPy 3 #2 0.480.5368,948801  6% 4% 9% 81%
11PyPy 3 #8 0.600.5468,692777  17% 85% 22% 11%
11PyPy 3 #3 0.620.5469,2922011  13% 19% 13% 89%
11PyPy 3 #2 0.470.5468,672801  81% 8% 6% 4%
11PyPy 3 #2 0.500.5569,200801  82% 11% 6% 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%
43Graal 3.962.09430,424594  12% 47% 52% 89%
43Graal 3.952.09432,112594  64% 4% 42% 87%
43Graal 3.852.10437,944594  54% 23% 57% 59%
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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