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 0.190.09?2011  50% 50% 33% 89%
 Python 3 #2 0.090.07?801  14% 14% 14% 88%
 Pyston 0.100.10?593  100% 0% 0% 0%
 PyPy 3 0.140.14?594  0% 0% 7% 100%
 Pyston 0.120.12?593  0% 0% 0% 92%
 Nuitka 0.100.10?594  9% 100% 0% 0%
 Python 3 0.130.13?594  0% 0% 17% 85%
 Python development version #3 0.190.09?2011  38% 50% 38% 89%
 Python 3 #3 0.200.10?2011  90% 40% 36% 42%
 Python 2 0.080.08?593  0% 0% 0% 100%
 Python 2 0.080.08?593  0% 0% 0% 100%
 Nuitka #2 0.150.21?801  100% 100% 100% 100%
 Nuitka #2 0.140.19?801  100% 100% 100% 100%
 Python 3 #2 0.100.08?801  25% 14% 78% 14%
 Cython 0.090.09?618  11% 100% 0% 0%
 Cython 0.090.09?618  0% 100% 0% 0%
 Python 3 #8 0.250.11?777  40% 45% 91% 50%
 Python 2 0.080.08?593  100% 0% 0% 11%
 Python development version #3 0.190.09?2011  50% 88% 43% 38%
 Python 2 #2 0.070.14?801  8% 21% 20% 13%
 Python development version #8 0.240.11?777  90% 55% 40% 55%
 Python 2 #2 0.100.16?801  12% 38% 7% 13%
 Cython 0.090.09?618  20% 100% 0% 0%
 Python 2 #2 0.100.17?801  6% 39% 12% 13%
 Python 3 0.130.13?594  8% 0% 100% 0%
 Python development version #2 0.110.09?801  40% 89% 10% 20%
 Nuitka #3 0.180.09?2011  44% 89% 33% 33%
 Python development version #2 0.100.07?801  86% 17% 29% 14%
 PyPy 3 0.150.15?594  13% 0% 100% 0%
 Python development version #2 0.090.07?801  17% 17% 71% 17%
 Pyston 0.100.10?593  22% 9% 80% 10%
 Python 3 #2 0.100.08?801  38% 14% 71% 13%
 Shedskin 0.060.05?593  0% 0% 0% 100%
 Python development version 0.110.11?594  15% 8% 15% 91%
 Python 3 #8 0.250.11?777  58% 82% 50% 55%
 Python development version 0.110.11?594  17% 0% 15% 100%
 Python development version #3 0.190.09?2011  56% 44% 50% 88%
 PyPy 2 0.120.13?593  75% 25% 0% 7%
 Python development version #8 0.250.11?777  45% 62% 50% 75%
 PyPy 2 0.110.12?593  8% 8% 100% 0%
 Python development version #8 0.240.10?777  55% 100% 50% 45%
 PyPy 2 0.120.12?593  0% 0% 100% 0%
 Python 3 #8 0.250.11?777  60% 83% 50% 45%
 Python development version 0.110.11?594  15% 8% 100% 8%
 Nuitka 0.100.10?594  0% 100% 0% 0%
 Shedskin 0.060.05?593  0% 20% 0% 100%
 Nuitka 0.100.10?594  0% 0% 0% 100%
 Shedskin 0.060.05?593  0% 40% 67% 20%
 Python 3 0.120.13?594  8% 0% 0% 100%
 Python 2 #8 0.190.15?777  33% 33% 40% 50%
 Nuitka #3 0.180.09?2011  44% 78% 50% 44%
 Python 2 #8 0.190.15?777  31% 21% 33% 53%
 Nuitka #3 0.180.09?2011  38% 89% 40% 38%
 Python 2 #8 0.190.15?777  50% 33% 29% 29%
 PyPy 3 0.140.15?594  7% 0% 100% 0%
 Python 3 #3 0.190.09?2011  38% 33% 40% 90%
 Pyston #8 0.300.354777  21% 19% 63% 21%
 PyPy 2 #8 0.380.33972777  15% 18% 12% 79%
 PyPy 2 #8 0.380.34976777  18% 15% 73% 12%
 PyPy 2 #8 0.390.34980777  12% 21% 76% 17%
 PyPy 2 #2 0.370.36992801  14% 14% 69% 14%
 PyPy 2 #2 0.350.361,088801  70% 11% 11% 11%
 PyPy 2 #2 0.340.361,092801  14% 14% 63% 14%
 Nuitka #2 0.160.2855,352801  100% 100% 100% 100%
 PyPy 3 #8 0.600.5370,476777  17% 11% 11% 83%
 PyPy 3 #8 0.590.5570,556777  89% 6% 7% 13%
 PyPy 3 #2 0.530.5770,576801  12% 81% 7% 4%
 PyPy 3 #8 0.600.5670,732777  84% 9% 13% 11%
 PyPy 3 #2 0.510.5770,764801  12% 20% 64% 4%
 PyPy 3 #2 0.520.5670,928801  7% 5% 79% 5%
 PyPy 3 #3 0.600.5671,0042011  13% 11% 9% 82%
 PyPy 3 #3 0.610.5871,0322011  86% 9% 7% 9%
 PyPy 3 #3 0.610.5671,1922011  13% 87% 11% 11%
 IronPython 1.201.5971,668593  34% 11% 3% 39%
 IronPython 1.131.5578,304593  3% 3% 52% 23%
 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 5.552.73490,696594  83% 62% 2% 64%
 Graal 5.602.68491,692594  9% 74% 63% 72%
 Graal 5.652.75492,012594  74% 61% 73% 5%
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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