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 0.100.07?801  86% 17% 29% 14%
1.4Python development version #2 0.090.07?801  17% 17% 71% 17%
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%
1.8Python development version #3 0.190.09?2011  50% 88% 43% 38%
1.8Python development version #3 0.190.09?2011  38% 50% 38% 89%
1.8Python development version #3 0.190.09?2011  56% 44% 50% 88%
1.9Cython 0.090.09?618  0% 0% 10% 100%
1.9Cython 0.090.09?618  100% 0% 0% 0%
1.9Python development version #2 0.110.09?801  40% 89% 10% 20%
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%
2.1Python development version #8 0.240.10?777  55% 100% 50% 45%
2.2Nuitka 0.110.11?594  0% 0% 0% 100%
2.2Nuitka 0.110.11?594  0% 100% 8% 8%
2.2Nuitka 0.110.11?594  0% 0% 100% 8%
2.2Python development version #8 0.240.11?777  90% 55% 40% 55%
2.3Python development version 0.110.11?594  15% 8% 15% 91%
2.3Python development version #8 0.250.11?777  45% 62% 50% 75%
2.3Python development version 0.110.11?594  17% 0% 15% 100%
2.3Python development version 0.110.11?594  15% 8% 100% 8%
2.4Python 3 0.120.12?594  100% 8% 8% 0%
2.5Python 3 0.120.12?594  100% 8% 0% 0%
2.5Pyston 0.120.12?593  0% 0% 0% 92%
2.6Python 3 0.130.13?594  0% 8% 0% 100%
2.8PyPy 3 0.140.14?594  0% 0% 100% 7%
2.9PyPy 3 0.140.14?594  0% 7% 7% 100%
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%
3.1PyPy 3 0.150.15?594  7% 100% 6% 0%
3.3Python 2 #2 0.100.16?801  12% 38% 7% 13%
3.3Python 3 #8 0.240.16?777  35% 59% 41% 31%
3.4Python 2 #2 0.100.17?801  6% 39% 12% 13%
3.4Nuitka #8 0.240.17?777  33% 35% 59% 27%
3.4Python 3 #8 0.260.17?777  29% 38% 29% 65%
3.4Python 3 #8 0.260.17?777  31% 35% 71% 29%
3.5Nuitka #8 0.240.17?777  65% 29% 24% 28%
3.5Nuitka #3 0.200.17?2011  19% 47% 19% 29%
3.5Python 3 #2 0.120.17?801  6% 24% 41% 18%
3.6Nuitka #8 0.250.18?777  25% 65% 33% 28%
3.6Nuitka #2 0.110.18?801  35% 0% 6% 21%
3.7Nuitka #3 0.210.18?2011  56% 21% 18% 22%
3.8Python 3 #3 0.210.19?2011  28% 28% 53% 17%
3.8Python 3 #2 0.120.19?801  5% 16% 11% 44%
3.9Python 3 #3 0.220.19?2011  30% 22% 21% 53%
3.9Python 3 #2 0.140.19?801  50% 16% 11% 5%
4.0Nuitka #3 0.220.20?2011  63% 16% 16% 16%
4.0Nuitka #2 0.130.20?801  5% 40% 5% 16%
4.0Nuitka #2 0.120.20?801  14% 11% 5% 50%
4.1Python 3 #3 0.240.20?2011  20% 32% 25% 59%
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%
6.3PyPy 2 0.200.311,096593  100% 100% 100% 100%
6.9PyPy 2 0.190.3419,224593  100% 100% 100% 100%
7.2Pyston #8 0.300.354777  21% 19% 63% 21%
7.2PyPy 2 0.210.3630,332593  100% 100% 100% 100%
9.1PyPy 2 #2 0.380.4576,308801  100% 31% 82% 100%
10PyPy 2 #2 0.390.4974,380801  98% 92% 100% 98%
10PyPy 2 #2 0.400.5075,264801  100% 96% 92% 100%
11PyPy 3 #8 0.600.5370,716777  85% 17% 10% 12%
11PyPy 3 #8 0.600.5370,984777  85% 17% 10% 13%
11PyPy 3 #3 0.640.5370,7282011  11% 14% 11% 89%
11PyPy 3 #3 0.640.5370,8802011  12% 18% 89% 13%
11PyPy 3 #3 0.630.5470,9522011  10% 91% 15% 9%
11PyPy 3 #8 0.590.5470,720777  11% 13% 15% 87%
11PyPy 3 #2 0.500.5470,960801  80% 13% 6% 9%
12PyPy 3 #2 0.500.5771,144801  4% 81% 7% 7%
12PyPy 3 #2 0.500.5771,228801  4% 11% 79% 5%
12PyPy 2 #8 0.500.6075,236777  100% 100% 100% 100%
14PyPy 2 #8 0.510.6979,036777  100% 100% 100% 100%
17PyPy 2 #8 0.500.8274,856777  100% 100% 100% 100%
22Graal 1.591.07410,484594  50% 14% 11% 89%
22Graal 1.561.07409,568594  77% 45% 22% 16%
27Graal 1.871.33426,324594  9% 10% 80% 52%
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
RustPython 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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