k-nucleotide benchmark N=10,000

Each chart bar shows how many times more Memory, one ↓ k-nucleotide program used, compared to the program that used least Memory.

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.

    sortsort sort
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
 Python 3 #3 0.240.20?2011  20% 32% 25% 59%
 Nuitka #8 0.240.17?777  33% 35% 59% 27%
 Python development version #2 0.100.07?801  86% 17% 29% 14%
 Python 2 #2 0.070.14?801  8% 21% 20% 13%
 Cython 0.090.09?618  0% 0% 10% 100%
 Python 3 #2 0.120.17?801  6% 24% 41% 18%
 Cython 0.090.09?618  0% 0% 100% 0%
 Python 2 #2 0.100.17?801  6% 39% 12% 13%
 Cython 0.090.09?618  100% 0% 0% 0%
 Python 3 #2 0.120.19?801  5% 16% 11% 44%
 Nuitka #2 0.110.18?801  35% 0% 6% 21%
 Python 2 0.080.08?593  100% 0% 0% 11%
 Nuitka #2 0.130.20?801  5% 40% 5% 16%
 Python 2 #2 0.100.16?801  12% 38% 7% 13%
 Nuitka #2 0.120.20?801  14% 11% 5% 50%
 Python development version #2 0.110.09?801  40% 89% 10% 20%
 Python 2 0.080.08?593  0% 0% 0% 100%
 Python development version #2 0.090.07?801  17% 17% 71% 17%
 Python 2 0.080.08?593  0% 0% 0% 100%
 Python 3 #2 0.140.19?801  50% 16% 11% 5%
 Python development version #8 0.240.11?777  90% 55% 40% 55%
 Nuitka #8 0.250.18?777  25% 65% 33% 28%
 Nuitka #8 0.240.17?777  65% 29% 24% 28%
 Python development version #3 0.190.09?2011  56% 44% 50% 88%
 Python development version 0.110.11?594  15% 8% 15% 91%
 Nuitka 0.110.11?594  0% 0% 100% 8%
 Python development version #8 0.240.10?777  55% 100% 50% 45%
 Python 3 0.130.13?594  0% 8% 0% 100%
 Python development version 0.110.11?594  15% 8% 100% 8%
 Python 3 0.120.12?594  100% 8% 8% 0%
 Python development version #8 0.250.11?777  45% 62% 50% 75%
 Python 2 #8 0.190.15?777  33% 33% 40% 50%
 Python 3 #3 0.220.19?2011  30% 22% 21% 53%
 Python 2 #8 0.190.15?777  31% 21% 33% 53%
 Python development version 0.110.11?594  17% 0% 15% 100%
 Python 2 #8 0.190.15?777  50% 33% 29% 29%
 Python development version #3 0.190.09?2011  50% 88% 43% 38%
 Pyston 0.120.12?593  0% 0% 0% 92%
 Python development version #3 0.190.09?2011  38% 50% 38% 89%
 Pyston 0.100.10?593  22% 9% 80% 10%
 Python 3 #8 0.260.17?777  29% 38% 29% 65%
 Pyston 0.100.10?593  100% 0% 0% 0%
 Python 3 #3 0.210.19?2011  28% 28% 53% 17%
 Shedskin 0.060.05?593  0% 20% 0% 100%
 Python 3 0.120.12?594  100% 8% 0% 0%
 Shedskin 0.060.05?593  0% 40% 67% 20%
 Nuitka 0.110.11?594  0% 0% 0% 100%
 Nuitka 0.110.11?594  0% 100% 8% 8%
 Shedskin 0.060.05?593  0% 0% 0% 100%
 Python 3 #8 0.240.16?777  35% 59% 41% 31%
 Nuitka #3 0.200.17?2011  19% 47% 19% 29%
 PyPy 3 0.150.15?594  7% 100% 6% 0%
 Nuitka #3 0.220.20?2011  63% 16% 16% 16%
 PyPy 3 0.140.14?594  0% 7% 7% 100%
 Python 3 #8 0.260.17?777  31% 35% 71% 29%
 PyPy 3 0.140.14?594  0% 0% 100% 7%
 Nuitka #3 0.210.18?2011  56% 21% 18% 22%
 Pyston #8 0.300.354777  21% 19% 63% 21%
 PyPy 2 0.200.311,096593  100% 100% 100% 100%
 PyPy 2 0.190.3419,224593  100% 100% 100% 100%
 PyPy 2 0.210.3630,332593  100% 100% 100% 100%
 PyPy 3 #8 0.600.5370,716777  85% 17% 10% 12%
 PyPy 3 #8 0.590.5470,720777  11% 13% 15% 87%
 PyPy 3 #3 0.640.5370,7282011  11% 14% 11% 89%
 PyPy 3 #3 0.640.5370,8802011  12% 18% 89% 13%
 PyPy 3 #3 0.630.5470,9522011  10% 91% 15% 9%
 PyPy 3 #2 0.500.5470,960801  80% 13% 6% 9%
 PyPy 3 #8 0.600.5370,984777  85% 17% 10% 13%
 PyPy 3 #2 0.500.5771,144801  4% 81% 7% 7%
 PyPy 3 #2 0.500.5771,228801  4% 11% 79% 5%
 IronPython 1.201.5971,668593  34% 11% 3% 39%
 PyPy 2 #2 0.390.4974,380801  98% 92% 100% 98%
 PyPy 2 #8 0.500.8274,856777  100% 100% 100% 100%
 PyPy 2 #8 0.500.6075,236777  100% 100% 100% 100%
 PyPy 2 #2 0.400.5075,264801  100% 96% 92% 100%
 PyPy 2 #2 0.380.4576,308801  100% 31% 82% 100%
 IronPython 1.131.5578,304593  3% 3% 52% 23%
 PyPy 2 #8 0.510.6979,036777  100% 100% 100% 100%
 IronPython 1.151.5780,896593  56% 4% 16% 3%
 Pyston #8 0.270.24116,440777  67% 16% 17% 17%
 Pyston #8 0.280.25117,132777  69% 16% 13% 13%
 Pyston #2 0.200.25123,824801  12% 12% 56% 12%
 Pyston #2 0.190.25125,780801  31% 16% 16% 58%
 Pyston #2 0.190.26128,384801  12% 15% 8% 58%
 Jython 3.851.61188,480593  81% 45% 63% 59%
 Jython 3.951.66197,900593  66% 50% 61% 77%
 Jython 4.271.80198,752593  67% 70% 65% 68%
 Graal 1.561.07409,568594  77% 45% 22% 16%
 Graal 1.591.07410,484594  50% 14% 11% 89%
 Graal 1.871.33426,324594  9% 10% 80% 52%
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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