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%
2.2PyPy 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%
2.4Python 3 #5 14.846.20421,1482016  67% 63% 48% 64%
2.4Pyston 6.216.2243,180900  19% 2% 82% 0%
2.4Pyston 6.266.2633,768900  91% 3% 14% 2%
2.4Python 3 #5 15.296.27421,2482016  64% 82% 58% 41%
2.4Python 3 #5 15.366.28421,4162016  85% 78% 44% 40%
2.5Pyston 6.426.4233,884900  15% 100% 22% 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%
3.2Cython 8.368.378,916904  100% 0% 0% 1%
3.3Cython 8.478.488,916904  1% 100% 1% 1%
3.3Python 3 #4 8.498.498,8321698  100% 0% 0% 0%
3.3Python 3 #4 8.508.508,8441698  0% 0% 0% 100%
3.3Cython 8.538.548,976904  5% 100% 3% 3%
3.3Python 3 #4 8.578.588,9401698  1% 58% 42% 0%
3.3Python development version #5 20.208.70419,5562016  76% 73% 72% 81%
3.4Python 3 #3 8.888.888,7361647  100% 0% 0% 0%
3.4Python 3 #3 8.908.908,9401647  0% 0% 100% 0%
3.5Python 3 #3 9.079.078,9081647  1% 100% 0% 1%
3.5Nuitka #2 9.089.0810,400889  1% 1% 88% 13%
3.5Nuitka #2 9.099.0910,476889  1% 100% 0% 0%
3.7Nuitka 9.569.5710,340904  25% 5% 12% 78%
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%
4.1Nuitka #2 10.7110.7210,388889  4% 100% 5% 3%
4.2Python 3 10.7810.798,800904  0% 76% 0% 24%
4.2Python 3 10.8510.868,840904  0% 100% 0% 0%
4.2Python 3 10.9210.928,796904  0% 100% 0% 0%
4.2Python 3 #2 10.9810.988,896889  0% 0% 100% 0%
4.2Python 3 #2 11.0111.018,972889  0% 0% 0% 100%
4.3Python development version #3 11.0511.067,6721647  41% 76% 16% 13%
4.3Python 3 #2 11.2211.228,780889  0% 0% 0% 100%
4.4Python development version #3 11.4611.487,6601647  19% 24% 79% 39%
4.5Nuitka 11.0111.6210,332904  33% 61% 48% 52%
4.5Nuitka 11.6311.7810,560904  39% 47% 79% 22%
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%
9.7MicroPython 25.2225.224,396904  100% 2% 1% 2%
9.7MicroPython 25.2725.274,344904  2% 2% 2% 100%
10MicroPython #2 26.4526.464,376889  55% 2% 46% 1%
10MicroPython #2 26.6726.674,364889  2% 100% 2% 2%
10MicroPython 26.9226.934,316904  61% 2% 7% 34%
11MicroPython #2 27.3027.334,480889  46% 2% 55% 2%
13Jython 40.4333.20288,488900  31% 44% 52% 31%
13Jython 40.8633.54310,240900  59% 36% 27% 43%
14Jython 41.8335.20290,484900  55% 48% 50% 48%
missing benchmark programs
IronPython No program
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

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