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 2.252.2540,6121698  4% 3% 1% 100%
1.0PyPy 3 #4 2.252.2640,6481698  7% 100% 3% 1%
1.2PyPy 3 #4 2.752.7640,5921698  16% 100% 16% 15%
1.3PyPy 2 3.003.0076,460900  1% 69% 0% 32%
1.4PyPy 2 3.053.0676,456900  14% 3% 87% 3%
1.4PyPy 2 3.063.0776,404900  5% 1% 100% 1%
1.6PyPy 3 #3 3.613.6140,5881647  2% 100% 3% 1%
1.6PyPy 3 #3 3.613.6240,5961647  100% 2% 1% 1%
1.6PyPy 3 #3 3.683.6840,8761647  2% 4% 2% 100%
1.7PyPy 3 #2 3.803.8046,816889  1% 4% 1% 100%
1.7PyPy 3 3.803.8146,692904  4% 3% 0% 100%
1.7PyPy 3 3.823.8346,540904  1% 2% 100% 2%
1.7PyPy 3 #2 3.883.8946,836889  100% 2% 4% 3%
1.7PyPy 3 #2 3.913.9146,784889  3% 5% 100% 3%
1.8PyPy 3 3.953.9646,684904  3% 100% 4% 3%
2.6Nuitka #5 14.315.96423,2722016  54% 81% 59% 53%
2.7Nuitka #5 14.216.04422,8282016  78% 40% 67% 56%
2.7Nuitka #5 14.756.06423,2842016  68% 82% 46% 55%
2.8Pyston 6.216.2243,180900  19% 2% 82% 0%
2.8Pyston 6.266.2633,768900  91% 3% 14% 2%
2.9Pyston 6.426.4233,884900  15% 100% 22% 2%
3.1Nuitka #4 6.876.8810,1041698  3% 1% 23% 78%
3.1Python 3 #5 16.116.94421,3082016  74% 70% 59% 67%
3.1Nuitka #4 6.956.9510,3481698  2% 100% 2% 1%
3.1Nuitka #4 6.966.9610,1081698  1% 100% 2% 3%
3.2Python 3 #5 16.477.10421,1882016  63% 68% 38% 84%
3.2Python 3 #5 16.517.24421,0882016  61% 80% 52% 55%
3.3Nuitka #3 7.347.3410,2281647  3% 100% 1% 0%
3.3Nuitka #3 7.347.3510,3401647  2% 1% 1% 100%
3.3Nuitka #3 7.457.4610,0801647  2% 100% 2% 2%
3.4Python development version #5 19.257.59419,8682016  63% 74% 78% 63%
3.4Python development version #5 19.737.72419,8162016  72% 65% 75% 58%
3.8Python 3 #4 8.588.598,7201698  4% 3% 4% 100%
3.9Python 3 #4 8.688.688,8761698  8% 49% 6% 53%
3.9Python development version #5 20.208.70419,5562016  76% 73% 72% 81%
4.0Nuitka 9.079.0710,100904  1% 100% 1% 2%
4.0Python 3 #3 9.079.088,8561647  15% 5% 86% 4%
4.0Nuitka 9.099.1010,044904  2% 100% 2% 1%
4.1Nuitka #2 9.269.2710,180889  1% 1% 100% 2%
4.1Nuitka #2 9.279.2810,240889  1% 3% 100% 2%
4.1Python 3 #3 9.319.328,8921647  10% 5% 8% 100%
4.2Nuitka 9.359.3610,236904  5% 2% 100% 2%
4.2Nuitka #2 9.439.4310,240889  2% 3% 1% 100%
4.4Python 3 #3 9.889.898,9361647  5% 4% 6% 100%
4.5Python development version #4 10.0410.057,6521698  2% 2% 1% 100%
4.5Python 2 10.0910.0912,768900  38% 4% 64% 3%
4.5Python 2 10.1110.1112,696900  2% 100% 2% 4%
4.5Python development version #4 10.1310.147,5321698  72% 4% 29% 3%
4.5Python development version #4 10.1610.167,6201698  20% 3% 83% 2%
4.6Python 2 10.2610.2712,668900  4% 100% 4% 7%
4.6Python development version #3 10.2810.307,7361647  3% 73% 2% 29%
4.9Python 3 10.9110.918,788904  2% 5% 100% 4%
4.9Cython 11.0411.048,400904  4% 100% 3% 5%
4.9Python development version #3 11.0511.067,6721647  41% 76% 16% 13%
5.0Python 3 #4 11.1711.188,7681698  3% 3% 100% 2%
5.0Python 3 #2 11.1811.188,484889  3% 1% 100% 1%
5.1Python 3 #2 11.4411.448,664889  5% 5% 100% 5%
5.1Python development version #3 11.4611.487,6601647  19% 24% 79% 39%
5.2Python 3 #2 11.5911.608,720889  17% 92% 9% 9%
5.2Python 3 11.7711.808,720904  29% 19% 81% 16%
5.5Cython 12.2412.268,352904  59% 12% 54% 13%
5.5Python 3 12.2812.318,792904  21% 90% 21% 30%
5.8Python development version 13.0113.027,564904  14% 90% 4% 4%
5.9Python development version 13.1713.177,364904  4% 100% 3% 5%
5.9Python development version #2 13.2013.207,468889  3% 2% 2% 100%
5.9Python development version #2 13.2913.307,404889  100% 2% 3% 1%
5.9Python development version 13.3513.387,504904  15% 9% 92% 11%
6.0Python development version #2 13.3913.407,540889  6% 3% 97% 2%
6.0Cython 13.3813.408,240904  16% 92% 16% 21%
6.2IronPython 13.7614.0085,564900  6% 5% 5% 97%
6.4IronPython 14.2614.4786,020900  15% 86% 12% 22%
6.9IronPython 15.2215.5987,344900  33% 9% 71% 8%
11MicroPython 25.2325.244,264904  3% 97% 1% 6%
11MicroPython 25.2725.284,256904  3% 2% 100% 1%
12MicroPython 26.1026.114,308904  3% 89% 1% 15%
12MicroPython #2 26.3026.304,264889  1% 100% 1% 1%
12MicroPython #2 26.4326.444,272889  53% 1% 44% 6%
12MicroPython #2 27.1827.194,356889  11% 34% 40% 19%
14Jython 38.5431.81307,288900  40% 31% 33% 44%
14Jython 38.9131.90303,412900  27% 26% 52% 37%
15Jython 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

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