fasta benchmark ≈24MB N=2,500,000

Each chart bar shows how many times slower, one ↓ fasta 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
1.0PyPy 3 #4 2.602.6065,7161698  0% 100% 0% 1%
1.0PyPy 3 #4 2.612.6165,5721698  1% 1% 3% 100%
1.0PyPy 3 #4 2.642.6465,6521698  0% 2% 100% 1%
1.2PyPy 2 3.183.1877,736900  1% 0% 100% 1%
1.2PyPy 2 3.193.1978,120900  0% 100% 0% 0%
1.3PyPy 2 3.253.2678,160900  0% 0% 100% 3%
1.4PyPy 3 3.763.7668,764904  100% 1% 1% 0%
1.5PyPy 3 #2 3.793.7968,476889  1% 2% 100% 1%
1.5PyPy 3 3.853.8568,764904  3% 3% 1% 100%
1.5PyPy 3 #2 3.873.8768,688889  1% 1% 0% 100%
1.5PyPy 3 3.873.8868,944904  1% 2% 100% 1%
1.5PyPy 3 #2 3.903.9069,388889  0% 1% 2% 100%
1.5PyPy 3 #3 4.024.0266,0481647  2% 2% 100% 0%
1.6PyPy 3 #3 4.034.0365,5841647  2% 1% 0% 100%
1.6PyPy 3 #3 4.044.0465,8841647  2% 100% 0% 1%
2.1Nuitka #5 12.995.40423,7882016  75% 51% 40% 76%
2.1Nuitka #5 13.435.45423,4762016  70% 64% 53% 63%
2.2PyPy 3 #5 18.765.6077,2482016  83% 97% 80% 78%
2.2PyPy 3 #5 18.775.6077,6042016  92% 82% 84% 79%
2.2PyPy 3 #5 18.665.6277,0082016  83% 96% 78% 79%
2.2Nuitka #5 13.175.62423,4562016  83% 62% 43% 56%
2.4Pyston 6.216.2243,180900  19% 2% 82% 0%
2.4Pyston 6.266.2633,768900  91% 3% 14% 2%
2.4Python 3 #5 15.066.26421,3242016  77% 85% 42% 41%
2.4Python 3 #5 14.996.33421,5922016  68% 78% 49% 48%
2.5Python 3 #5 15.586.37421,5922016  74% 82% 45% 47%
2.5Pyston 6.426.4233,884900  15% 100% 22% 2%
2.6Nuitka #4 6.646.6410,5401698  0% 100% 2% 0%
2.6Nuitka #4 6.696.6910,5281698  100% 1% 0% 0%
2.6Nuitka #4 6.696.7010,3521698  2% 75% 2% 25%
2.7Nuitka #3 7.067.0610,4121647  1% 1% 100% 1%
2.7Nuitka #3 7.077.0710,5441647  1% 1% 0% 100%
2.7Nuitka #3 7.137.1310,5281647  1% 2% 100% 2%
2.9Python development version #5 19.257.59419,8682016  63% 74% 78% 63%
3.0Python development version #5 19.737.72419,8162016  72% 65% 75% 58%
3.2Python 3 #4 8.398.399,0681698  1% 100% 1% 0%
3.2Python 3 #4 8.438.448,9921698  1% 0% 1% 100%
3.3Python 3 #4 8.498.499,0561698  1% 41% 1% 60%
3.3Python development version #5 20.208.70419,5562016  76% 73% 72% 81%
3.4Nuitka 8.748.7410,348904  1% 1% 1% 100%
3.4Nuitka 8.768.7610,524904  1% 100% 0% 0%
3.4Nuitka 8.778.7710,452904  0% 1% 1% 100%
3.5Python 3 #3 8.978.979,0601647  2% 1% 1% 100%
3.5Python 3 #3 8.978.988,9401647  87% 2% 15% 1%
3.5Nuitka #2 9.019.0110,524889  1% 0% 0% 100%
3.5Nuitka #2 9.019.0110,360889  1% 1% 1% 100%
3.5Nuitka #2 9.049.0410,292889  7% 95% 1% 1%
3.5Python 3 #3 9.059.069,0601647  2% 3% 100% 1%
3.9Python development version #4 10.0410.057,6521698  2% 2% 1% 100%
3.9Python 2 10.0910.0912,768900  38% 4% 64% 3%
3.9Python 2 10.1110.1112,696900  2% 100% 2% 4%
3.9Python development version #4 10.1310.147,5321698  72% 4% 29% 3%
3.9Python development version #4 10.1610.167,6201698  20% 3% 83% 2%
4.0Python 2 10.2610.2712,668900  4% 100% 4% 7%
4.0Python development version #3 10.2810.307,7361647  3% 73% 2% 29%
4.2Python 3 10.8310.848,940904  100% 2% 1% 1%
4.2Python 3 10.8710.889,004904  100% 2% 1% 1%
4.2Python 3 10.8910.899,008904  1% 3% 100% 1%
4.2Python 3 #2 10.9910.998,988889  1% 100% 1% 0%
4.3Cython 11.0411.048,400904  4% 100% 3% 5%
4.3Python 3 #2 11.0511.068,908889  2% 100% 1% 2%
4.3Python development version #3 11.0511.067,6721647  41% 76% 16% 13%
4.3Python 3 #2 11.0611.068,944889  1% 1% 2% 100%
4.4Python development version #3 11.4611.487,6601647  19% 24% 79% 39%
4.7Cython 12.2412.268,352904  59% 12% 54% 13%
5.0Python development version 13.0113.027,564904  14% 90% 4% 4%
5.1Python development version 13.1713.177,364904  4% 100% 3% 5%
5.1Python development version #2 13.2013.207,468889  3% 2% 2% 100%
5.1Python development version #2 13.2913.307,404889  100% 2% 3% 1%
5.1Python development version 13.3513.387,504904  15% 9% 92% 11%
5.2Python development version #2 13.3913.407,540889  6% 3% 97% 2%
5.2Cython 13.3813.408,240904  16% 92% 16% 21%
5.4IronPython 13.7614.0085,564900  6% 5% 5% 97%
5.6IronPython 14.2614.4786,020900  15% 86% 12% 22%
6.0IronPython 15.2215.5987,344900  33% 9% 71% 8%
9.7MicroPython 25.2325.244,264904  3% 97% 1% 6%
9.7MicroPython 25.2725.284,256904  3% 2% 100% 1%
10MicroPython 26.1026.114,308904  3% 89% 1% 15%
10MicroPython #2 26.3026.304,264889  1% 100% 1% 1%
10MicroPython #2 26.4326.444,272889  53% 1% 44% 6%
10MicroPython #2 27.1827.194,356889  11% 34% 40% 19%
12Jython 38.5431.81307,288900  40% 31% 33% 44%
12Jython 38.9131.90303,412900  27% 26% 52% 37%
13Jython 40.6433.61305,308900  33% 36% 51% 36%
missing benchmark programs
Shedskin No program
Numba No program
Grumpy No program

 fasta benchmark : Generate and write random DNA sequences

diff program output N = 1000 with this 10KB 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.

Each program should

We'll use the generated FASTA file as input for other benchmarks (reverse-complement, k-nucleotide).

Random DNA sequences can be based on a variety of Random Models (554KB pdf). You can use Markov chains or independently distributed nucleotides to generate random DNA sequences online.

Revised BSD license

  Home   Conclusions   License   Play