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 1.901.9269,7521698  52% 6% 48% 2%
1.0PyPy 3 #4 1.931.9569,1961698  95% 7% 2% 6%
1.0PyPy 3 #4 1.982.0069,2201698  100% 7% 1% 2%
1.3PyPy 2 2.542.5779,716900  99% 100% 13% 7%
1.4PyPy 3 2.682.7073,108904  2% 6% 100% 1%
1.4PyPy 2 2.702.7479,320900  76% 32% 33% 88%
1.4PyPy 3 #2 2.742.7673,388889  2% 6% 98% 1%
1.5PyPy 2 2.642.7879,316900  20% 43% 97% 81%
1.5PyPy 3 #2 2.792.8273,260889  1% 8% 1% 100%
1.5PyPy 3 2.692.8473,336904  1% 100% 2% 0%
1.5PyPy 3 #2 2.852.8773,184889  2% 6% 100% 1%
1.5PyPy 3 2.752.9073,004904  2% 99% 2% 1%
1.6PyPy 3 #3 3.063.0869,1961647  1% 6% 99% 2%
1.7PyPy 3 #3 3.203.2269,9761647  2% 6% 1% 100%
1.7PyPy 3 #3 3.193.3570,1761647  2% 99% 1% 2%
2.1Python development version #5 7.483.98103,8762016  35% 32% 73% 59%
2.1Cython 3.983.988,488945  100% 1% 2% 2%
2.1Python development version #5 7.503.99101,9162016  24% 27% 86% 62%
2.1Cython 3.994.008,616945  2% 2% 2% 100%
2.1Python development version #5 7.574.00103,4122016  23% 57% 75% 44%
2.1Cython 4.034.048,428945  100% 1% 1% 2%
2.1Nuitka #5 9.484.0990,5122016  58% 55% 55% 67%
2.2Nuitka #5 9.754.2176,0762016  77% 64% 67% 27%
2.2Nuitka #5 9.854.23104,5762016  66% 70% 72% 36%
2.2PyPy 3 #5 6.614.2480,9682016  32% 57% 42% 34%
2.2PyPy 3 #5 6.604.2580,5242016  41% 60% 32% 32%
2.3PyPy 3 #5 7.054.4280,7402016  76% 49% 15% 51%
2.3Python 3 #5 8.994.4596,8682016  56% 55% 52% 47%
2.3Python 3 #5 9.084.4795,7322016  68% 46% 29% 69%
2.4Python 3 #5 9.314.55106,3402016  65% 57% 50% 41%
2.4Pyston 4.614.6132,988900  100% 0% 0% 0%
2.4Pyston 4.624.6333,028900  100% 0% 0% 0%
2.4Pyston 4.634.6332,960900  0% 26% 0% 74%
2.5Nuitka #4 4.864.879,8441698  68% 33% 1% 1%
2.6Nuitka #4 4.904.919,9121698  1% 100% 2% 1%
2.7Nuitka #3 5.125.129,8001647  100% 1% 1% 1%
2.7Nuitka #3 5.135.149,8001647  1% 53% 1% 48%
2.7Nuitka #3 5.145.159,7441647  1% 1% 100% 1%
2.9Python development version #4 5.545.557,9641698  6% 32% 1% 70%
2.9Python development version #4 5.625.668,0921698  15% 1% 90% 1%
3.0Python development version #4 5.715.718,0001698  6% 2% 100% 1%
3.0Nuitka #4 5.815.829,9161698  6% 0% 94% 1%
3.2Python development version #3 6.086.098,0601647  5% 0% 100% 1%
3.2Python development version #3 6.096.107,9961647  5% 1% 1% 100%
3.2Python development version #3 6.146.158,0721647  8% 5% 100% 9%
3.3Python 3 #4 6.276.288,8521698  94% 5% 6% 1%
3.3Python 3 #4 6.336.398,7681698  1% 23% 2% 81%
3.4Python 2 6.556.5513,516900  2% 3% 2% 100%
3.5Python 2 6.646.6513,444900  3% 100% 4% 4%
3.5Python 3 #4 6.386.678,8241698  2% 100% 4% 3%
3.5Python 3 #3 6.736.748,9641647  43% 5% 59% 2%
3.6Python 3 #3 6.786.838,8241647  2% 23% 86% 2%
3.6Python 3 #3 6.836.858,9721647  63% 7% 39% 1%
3.6Python 2 6.866.8713,316900  15% 2% 88% 3%
3.6Nuitka 6.966.979,928904  1% 100% 1% 1%
3.7Nuitka 7.037.039,928904  2% 3% 100% 2%
3.8Python development version 7.207.208,080904  6% 24% 1% 77%
3.8Python development version #2 7.287.297,836889  5% 4% 1% 97%
3.8Python development version 7.307.328,084904  6% 1% 99% 1%
3.8Python development version #2 7.317.328,072889  5% 1% 1% 100%
3.8Python development version 7.357.367,960904  5% 1% 100% 1%
3.8Nuitka #2 7.377.3810,088889  1% 100% 1% 1%
3.9Nuitka #2 7.387.3910,044889  1% 71% 1% 30%
3.9Nuitka #2 7.447.449,804889  19% 2% 83% 2%
3.9Python development version #2 7.377.477,832889  35% 5% 73% 5%
4.1Python 3 7.797.808,868904  82% 6% 19% 1%
4.1Python 3 7.907.918,740904  2% 7% 2% 100%
4.2Nuitka 7.997.999,804904  1% 17% 1% 83%
4.2Python 3 7.808.108,676904  1% 89% 1% 13%
4.4Python 3 #2 8.048.408,852889  1% 100% 1% 2%
4.4Python 3 #2 8.068.428,720889  1% 100% 1% 2%
4.6Python 3 #2 8.428.808,608889  1% 100% 1% 2%
11Jython 24.7620.91305,304900  41% 22% 27% 28%
11Jython 25.4021.24316,384900  31% 32% 36% 20%
11Jython 25.9521.93299,620900  21% 45% 29% 24%
12MicroPython 22.7123.164,488904  64% 38% 18% 9%
12MicroPython 22.4823.194,488904  2% 73% 2% 30%
12MicroPython 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%
75Graal 5 min144.15419,208904  55% 62% 64% 56%
77Graal #2 5 min146.73418,748889  60% 54% 62% 62%
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

  Home   Conclusions   License   Play