fasta benchmark ≈24MB N=2,500,000

Each chart bar shows how many times more Code, one ↓ fasta program used, compared to the program that used least Code.

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.

    sortsortsort 
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0Nuitka #2 7.377.3810,088889  1% 100% 1% 1%
1.0Nuitka #2 7.447.449,804889  19% 2% 83% 2%
1.0Nuitka #2 7.387.3910,044889  1% 71% 1% 30%
1.0PyPy 3 #2 2.742.7673,388889  2% 6% 98% 1%
1.0MicroPython #2 22.9023.884,464889  2% 97% 1% 5%
1.0PyPy 3 #2 2.792.8273,260889  1% 8% 1% 100%
1.0MicroPython #2 23.6023.654,392889  85% 6% 17% 2%
1.0PyPy 3 #2 2.852.8773,184889  2% 6% 100% 1%
1.0Python development version #2 7.317.328,072889  5% 1% 1% 100%
1.0Graal #2 5 min146.73418,748889  60% 54% 62% 62%
1.0Python 3 #2 8.428.808,608889  1% 100% 1% 2%
1.0Python development version #2 7.377.477,832889  35% 5% 73% 5%
1.0Python development version #2 7.287.297,836889  5% 4% 1% 97%
1.0Python 3 #2 8.048.408,852889  1% 100% 1% 2%
1.0MicroPython #2 22.9423.764,544889  1% 82% 2% 21%
1.0Python 3 #2 8.068.428,720889  1% 100% 1% 2%
1.0Pyston 4.624.6333,028900  100% 0% 0% 0%
1.0Pyston 4.614.6132,988900  100% 0% 0% 0%
1.0Pyston 4.634.6332,960900  0% 26% 0% 74%
1.0Python 2 6.866.8713,316900  15% 2% 88% 3%
1.0Jython 24.7620.91305,304900  41% 22% 27% 28%
1.0Python 2 6.646.6513,444900  3% 100% 4% 4%
1.0Python 2 6.556.5513,516900  2% 3% 2% 100%
1.0Jython 25.9521.93299,620900  21% 45% 29% 24%
1.0Jython 25.4021.24316,384900  31% 32% 36% 20%
1.0PyPy 2 2.642.7879,316900  20% 43% 97% 81%
1.0PyPy 2 2.542.5779,716900  99% 100% 13% 7%
1.0PyPy 2 2.702.7479,320900  76% 32% 33% 88%
1.0Python development version 7.207.208,080904  6% 24% 1% 77%
1.0Nuitka 6.966.979,928904  1% 100% 1% 1%
1.0Graal 5 min144.15419,208904  55% 62% 64% 56%
1.0PyPy 3 2.752.9073,004904  2% 99% 2% 1%
1.0Python development version 7.307.328,084904  6% 1% 99% 1%
1.0PyPy 3 2.682.7073,108904  2% 6% 100% 1%
1.0PyPy 3 2.692.8473,336904  1% 100% 2% 0%
1.0Python development version 7.357.367,960904  5% 1% 100% 1%
1.0Python 3 7.797.808,868904  82% 6% 19% 1%
1.0Nuitka 7.997.999,804904  1% 17% 1% 83%
1.0Python 3 7.907.918,740904  2% 7% 2% 100%
1.0Python 3 7.808.108,676904  1% 89% 1% 13%
1.0Nuitka 7.037.039,928904  2% 3% 100% 2%
1.0MicroPython 22.7123.164,488904  64% 38% 18% 9%
1.0MicroPython 22.5423.214,504904  6% 72% 6% 36%
1.0MicroPython 22.4823.194,488904  2% 73% 2% 30%
1.1Cython 4.034.048,428945  100% 1% 1% 2%
1.1Cython 3.994.008,616945  2% 2% 2% 100%
1.1Cython 3.983.988,488945  100% 1% 2% 2%
1.9Python 3 #3 6.736.748,9641647  43% 5% 59% 2%
1.9Python development version #3 6.086.098,0601647  5% 0% 100% 1%
1.9PyPy 3 #3 3.063.0869,1961647  1% 6% 99% 2%
1.9Python 3 #3 6.786.838,8241647  2% 23% 86% 2%
1.9Python 3 #3 6.836.858,9721647  63% 7% 39% 1%
1.9Nuitka #3 5.125.129,8001647  100% 1% 1% 1%
1.9PyPy 3 #3 3.193.3570,1761647  2% 99% 1% 2%
1.9Nuitka #3 5.135.149,8001647  1% 53% 1% 48%
1.9Nuitka #3 5.145.159,7441647  1% 1% 100% 1%
1.9Python development version #3 6.096.107,9961647  5% 1% 1% 100%
1.9Python development version #3 6.146.158,0721647  8% 5% 100% 9%
1.9PyPy 3 #3 3.203.2269,9761647  2% 6% 1% 100%
1.9Nuitka #4 5.815.829,9161698  6% 0% 94% 1%
1.9Python 3 #4 6.276.288,8521698  94% 5% 6% 1%
1.9Python 3 #4 6.336.398,7681698  1% 23% 2% 81%
1.9Python 3 #4 6.386.678,8241698  2% 100% 4% 3%
1.9Python development version #4 5.545.557,9641698  6% 32% 1% 70%
1.9PyPy 3 #4 1.931.9569,1961698  95% 7% 2% 6%
1.9Nuitka #4 4.904.919,9121698  1% 100% 2% 1%
1.9PyPy 3 #4 1.901.9269,7521698  52% 6% 48% 2%
1.9Nuitka #4 4.864.879,8441698  68% 33% 1% 1%
1.9Python development version #4 5.715.718,0001698  6% 2% 100% 1%
1.9Python development version #4 5.625.668,0921698  15% 1% 90% 1%
1.9PyPy 3 #4 1.982.0069,2201698  100% 7% 1% 2%
2.3Python 3 #5 9.314.55106,3402016  65% 57% 50% 41%
2.3PyPy 3 #5 6.604.2580,5242016  41% 60% 32% 32%
2.3Nuitka #5 9.754.2176,0762016  77% 64% 67% 27%
2.3Python development version #5 7.503.99101,9162016  24% 27% 86% 62%
2.3Nuitka #5 9.484.0990,5122016  58% 55% 55% 67%
2.3Python development version #5 7.483.98103,8762016  35% 32% 73% 59%
2.3Nuitka #5 9.854.23104,5762016  66% 70% 72% 36%
2.3Python development version #5 7.574.00103,4122016  23% 57% 75% 44%
2.3Python 3 #5 8.994.4596,8682016  56% 55% 52% 47%
2.3PyPy 3 #5 6.614.2480,9682016  32% 57% 42% 34%
2.3Python 3 #5 9.084.4795,7322016  68% 46% 29% 69%
2.3PyPy 3 #5 7.054.4280,7402016  76% 49% 15% 51%
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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