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 2.002.0369,7161698  22% 1% 82% 0%
1.0PyPy 3 #4 1.972.0868,9281698  99% 1% 1% 0%
1.0PyPy 3 #4 2.092.1169,2481698  6% 0% 99% 1%
1.2PyPy 2 2.442.4678,424900  5% 2% 1% 99%
1.2PyPy 2 2.452.4978,572900  24% 1% 80% 0%
1.2PyPy 2 2.452.5278,532900  47% 1% 54% 1%
1.4PyPy 3 2.782.8072,704904  5% 1% 99% 1%
1.4PyPy 3 2.772.8173,060904  13% 0% 91% 1%
1.4PyPy 3 #2 2.792.8172,808889  5% 1% 99% 1%
1.4PyPy 3 2.812.8972,444904  44% 1% 56% 3%
1.4PyPy 3 #2 2.762.9172,628889  100% 0% 0% 1%
1.5PyPy 3 #2 2.812.9772,852889  100% 1% 1% 0%
1.6Cython 3.213.218,952945  5% 2% 100% 11%
1.6Cython 3.143.298,968945  100% 0% 0% 16%
1.6Cython 3.183.338,944945  100% 1% 2% 8%
1.6PyPy 3 #3 3.333.3569,8441647  5% 31% 2% 69%
1.7PyPy 3 #3 3.353.3769,5761647  5% 74% 1% 26%
1.7PyPy 3 #3 3.413.4469,5441647  5% 1% 99% 0%
2.0Python development version #5 7.483.98103,8762016  35% 32% 73% 59%
2.0Python development version #5 7.503.99101,9162016  24% 27% 86% 62%
2.0Python development version #5 7.574.00103,4122016  23% 57% 75% 44%
2.0Nuitka #4 4.034.0410,0881698  5% 2% 100% 0%
2.0Nuitka #4 3.914.1010,1241698  100% 1% 1% 1%
2.0Python 3 #5 7.554.1683,8202016  40% 38% 60% 51%
2.1Python 3 #5 7.574.1781,7042016  40% 27% 59% 61%
2.1Python 3 #5 7.824.22104,0842016  50% 75% 56% 10%
2.1Nuitka #5 8.604.25106,6602016  59% 50% 64% 42%
2.1Nuitka #5 8.874.27107,1442016  52% 54% 58% 51%
2.1Nuitka #5 8.874.3099,3002016  73% 47% 42% 61%
2.2PyPy 3 #5 7.124.4182,8042016  26% 26% 75% 42%
2.2Nuitka #3 4.454.4510,1401647  6% 1% 100% 4%
2.2PyPy 3 #5 7.154.5082,6602016  56% 34% 34% 42%
2.2PyPy 3 #5 7.124.5383,0962016  76% 17% 21% 49%
2.3Nuitka #3 4.394.5910,1241647  100% 1% 2% 6%
2.3Nuitka #3 4.404.6010,1681647  100% 0% 0% 5%
2.3Pyston 4.614.6132,988900  100% 0% 0% 0%
2.3Pyston 4.624.6333,028900  100% 0% 0% 0%
2.3Pyston 4.634.6332,960900  0% 26% 0% 74%
2.7Python development version #4 5.545.557,9641698  6% 32% 1% 70%
2.8Python development version #4 5.625.668,0921698  15% 1% 90% 1%
2.8Python 3 #4 5.685.689,0201698  5% 0% 100% 0%
2.8Python development version #4 5.715.718,0001698  6% 2% 100% 1%
2.8Python 3 #4 5.645.749,0121698  43% 1% 59% 0%
2.8Python 3 #4 5.665.779,0041698  44% 0% 59% 1%
3.0Python development version #3 6.086.098,0601647  5% 0% 100% 1%
3.0Nuitka 5.826.0910,020904  100% 0% 0% 7%
3.0Python development version #3 6.096.107,9961647  5% 1% 1% 100%
3.0Nuitka 5.836.1110,120904  100% 2% 1% 2%
3.0Python development version #3 6.146.158,0721647  8% 5% 100% 9%
3.0Python 3 #3 6.176.189,0601647  5% 0% 1% 100%
3.0Python 3 #3 6.186.189,0241647  5% 100% 0% 1%
3.0Nuitka 5.916.2010,200904  100% 1% 3% 7%
3.0Python 3 #3 6.196.209,0201647  5% 100% 0% 0%
3.2Nuitka #2 6.436.4410,076889  5% 1% 100% 3%
3.2Nuitka #2 6.336.4410,180889  39% 67% 4% 2%
3.2Nuitka #2 6.416.4510,068889  19% 1% 86% 4%
3.2Python 2 6.556.5513,516900  2% 3% 2% 100%
3.3Python 2 6.646.6513,444900  3% 100% 4% 4%
3.4Python 2 6.866.8713,316900  15% 2% 88% 3%
3.5Python 3 7.087.089,032904  5% 94% 1% 6%
3.5Python 3 7.097.109,020904  5% 78% 1% 23%
3.5Python 3 7.137.198,948904  19% 1% 85% 0%
3.5Python development version 7.207.208,080904  6% 24% 1% 77%
3.6Python 3 #2 7.237.249,012889  5% 100% 0% 1%
3.6Python 3 #2 7.277.288,992889  6% 100% 1% 1%
3.6Python development version #2 7.287.297,836889  5% 4% 1% 97%
3.6Python development version 7.307.328,084904  6% 1% 99% 1%
3.6Nuitka #4 6.257.3210,1841698  84% 80% 95% 91%
3.6Python development version #2 7.317.328,072889  5% 1% 1% 100%
3.6Python 3 #2 7.287.369,108889  27% 1% 76% 0%
3.6Python development version 7.357.367,960904  5% 1% 100% 1%
3.7Python development version #2 7.377.477,832889  35% 5% 73% 5%
10Jython 24.7620.91305,304900  41% 22% 27% 28%
10Jython 25.4021.24316,384900  31% 32% 36% 20%
11Jython 25.9521.93299,620900  21% 45% 29% 24%
11MicroPython 22.7123.164,488904  64% 38% 18% 9%
11MicroPython 22.4823.194,488904  2% 73% 2% 30%
11MicroPython 22.5423.214,504904  6% 72% 6% 36%
12MicroPython #2 23.6023.654,392889  85% 6% 17% 2%
12MicroPython #2 22.9423.764,544889  1% 82% 2% 21%
12MicroPython #2 22.9023.884,464889  2% 97% 1% 5%
12Graal 38.0524.36534,920904  27% 44% 81% 15%
12Graal #2 37.9624.46547,904889  39% 13% 73% 42%
12Graal 38.6724.59566,688904  27% 37% 82% 19%
12Graal #2 40.0825.00568,324889  21% 38% 79% 30%
12Graal 37.9625.02543,524904  45% 41% 69% 9%
12Graal #2 39.3825.24540,424889  25% 39% 83% 18%
missing benchmark programs
IronPython No program
Shedskin No program
Numba No program
Grumpy No program
RustPython 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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