fasta benchmark ≈24MB N=2,500,000

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

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.

    sortsort sort
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0MicroPython #2 23.6023.654,392889  85% 6% 17% 2%
1.0MicroPython #2 22.9023.884,464889  2% 97% 1% 5%
1.0MicroPython 22.4823.194,488904  2% 73% 2% 30%
1.0MicroPython 22.7123.164,488904  64% 38% 18% 9%
1.0MicroPython 22.5423.214,504904  6% 72% 6% 36%
1.0MicroPython #2 22.9423.764,544889  1% 82% 2% 21%
1.8Python development version #2 7.377.477,832889  35% 5% 73% 5%
1.8Python development version #2 7.287.297,836889  5% 4% 1% 97%
1.8Python development version 7.357.367,960904  5% 1% 100% 1%
1.8Python development version #4 5.545.557,9641698  6% 32% 1% 70%
1.8Python development version #3 6.096.107,9961647  5% 1% 1% 100%
1.8Python development version #4 5.715.718,0001698  6% 2% 100% 1%
1.8Python development version #3 6.086.098,0601647  5% 0% 100% 1%
1.8Python development version #3 6.146.158,0721647  8% 5% 100% 9%
1.8Python development version #2 7.317.328,072889  5% 1% 1% 100%
1.8Python development version 7.207.208,080904  6% 24% 1% 77%
1.8Python development version 7.307.328,084904  6% 1% 99% 1%
1.8Python development version #4 5.625.668,0921698  15% 1% 90% 1%
1.9Cython 4.034.048,428945  100% 1% 1% 2%
1.9Cython 3.983.988,488945  100% 1% 2% 2%
2.0Python 3 #2 8.428.808,608889  1% 100% 1% 2%
2.0Cython 3.994.008,616945  2% 2% 2% 100%
2.0Python 3 7.808.108,676904  1% 89% 1% 13%
2.0Python 3 #2 8.068.428,720889  1% 100% 1% 2%
2.0Python 3 7.907.918,740904  2% 7% 2% 100%
2.0Python 3 #4 6.336.398,7681698  1% 23% 2% 81%
2.0Python 3 #3 6.786.838,8241647  2% 23% 86% 2%
2.0Python 3 #4 6.386.678,8241698  2% 100% 4% 3%
2.0Python 3 #4 6.276.288,8521698  94% 5% 6% 1%
2.0Python 3 #2 8.048.408,852889  1% 100% 1% 2%
2.0Python 3 7.797.808,868904  82% 6% 19% 1%
2.0Python 3 #3 6.736.748,9641647  43% 5% 59% 2%
2.0Python 3 #3 6.836.858,9721647  63% 7% 39% 1%
2.2Nuitka #3 5.145.159,7441647  1% 1% 100% 1%
2.2Nuitka #3 5.135.149,8001647  1% 53% 1% 48%
2.2Nuitka #3 5.125.129,8001647  100% 1% 1% 1%
2.2Nuitka #2 7.447.449,804889  19% 2% 83% 2%
2.2Nuitka 7.997.999,804904  1% 17% 1% 83%
2.2Nuitka #4 4.864.879,8441698  68% 33% 1% 1%
2.3Nuitka #4 4.904.919,9121698  1% 100% 2% 1%
2.3Nuitka #4 5.815.829,9161698  6% 0% 94% 1%
2.3Nuitka 6.966.979,928904  1% 100% 1% 1%
2.3Nuitka 7.037.039,928904  2% 3% 100% 2%
2.3Nuitka #2 7.387.3910,044889  1% 71% 1% 30%
2.3Nuitka #2 7.377.3810,088889  1% 100% 1% 1%
3.0Python 2 6.866.8713,316900  15% 2% 88% 3%
3.1Python 2 6.646.6513,444900  3% 100% 4% 4%
3.1Python 2 6.556.5513,516900  2% 3% 2% 100%
7.5Pyston 4.634.6332,960900  0% 26% 0% 74%
7.5Pyston 4.614.6132,988900  100% 0% 0% 0%
7.5Pyston 4.624.6333,028900  100% 0% 0% 0%
16PyPy 3 #3 3.063.0869,1961647  1% 6% 99% 2%
16PyPy 3 #4 1.931.9569,1961698  95% 7% 2% 6%
16PyPy 3 #4 1.982.0069,2201698  100% 7% 1% 2%
16PyPy 3 #4 1.901.9269,7521698  52% 6% 48% 2%
16PyPy 3 #3 3.203.2269,9761647  2% 6% 1% 100%
16PyPy 3 #3 3.193.3570,1761647  2% 99% 1% 2%
17PyPy 3 2.752.9073,004904  2% 99% 2% 1%
17PyPy 3 2.682.7073,108904  2% 6% 100% 1%
17PyPy 3 #2 2.852.8773,184889  2% 6% 100% 1%
17PyPy 3 #2 2.792.8273,260889  1% 8% 1% 100%
17PyPy 3 2.692.8473,336904  1% 100% 2% 0%
17PyPy 3 #2 2.742.7673,388889  2% 6% 98% 1%
17Nuitka #5 9.754.2176,0762016  77% 64% 67% 27%
18PyPy 2 2.642.7879,316900  20% 43% 97% 81%
18PyPy 2 2.702.7479,320900  76% 32% 33% 88%
18PyPy 2 2.542.5779,716900  99% 100% 13% 7%
18PyPy 3 #5 6.604.2580,5242016  41% 60% 32% 32%
18PyPy 3 #5 7.054.4280,7402016  76% 49% 15% 51%
18PyPy 3 #5 6.614.2480,9682016  32% 57% 42% 34%
21Nuitka #5 9.484.0990,5122016  58% 55% 55% 67%
22Python 3 #5 9.084.4795,7322016  68% 46% 29% 69%
22Python 3 #5 8.994.4596,8682016  56% 55% 52% 47%
23Python development version #5 7.503.99101,9162016  24% 27% 86% 62%
24Python development version #5 7.574.00103,4122016  23% 57% 75% 44%
24Python development version #5 7.483.98103,8762016  35% 32% 73% 59%
24Nuitka #5 9.854.23104,5762016  66% 70% 72% 36%
24Python 3 #5 9.314.55106,3402016  65% 57% 50% 41%
68Jython 25.9521.93299,620900  21% 45% 29% 24%
70Jython 24.7620.91305,304900  41% 22% 27% 28%
72Jython 25.4021.24316,384900  31% 32% 36% 20%
95Graal #2 5 min146.73418,748889  60% 54% 62% 62%
95Graal 5 min144.15419,208904  55% 62% 64% 56%
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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