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 1.861.8867,5201698  8% 4% 1% 99%
1.0PyPy 3 #4 1.921.9467,2441698  7% 5% 99% 3%
1.0PyPy 3 #4 1.851.9667,5441698  99% 1% 1% 15%
1.3PyPy 2 2.392.4180,148900  8% 2% 1% 99%
1.3PyPy 2 2.422.4480,060900  7% 96% 1% 1%
1.3PyPy 2 2.462.4880,312900  6% 0% 1% 99%
1.5PyPy 3 2.732.7570,252904  7% 99% 1% 1%
1.5PyPy 3 2.752.7770,540904  6% 99% 1% 0%
1.5PyPy 3 #2 2.752.7770,292889  6% 1% 1% 99%
1.5PyPy 3 #2 2.762.7870,632889  7% 1% 1% 99%
1.5PyPy 3 2.792.8170,560904  6% 6% 99% 1%
1.5PyPy 3 #2 2.802.8270,472889  6% 99% 1% 1%
1.7PyPy 3 #3 3.083.1067,0161647  6% 0% 1% 99%
1.7PyPy 3 #3 3.103.1267,4881647  6% 99% 0% 0%
1.7PyPy 3 #3 3.133.1667,5681647  6% 0% 1% 99%
2.2PyPy 3 #5 6.664.1381,1202016  23% 85% 63% 7%
2.2PyPy 3 #5 6.504.1881,0362016  64% 30% 34% 36%
2.2PyPy 3 #5 6.464.2181,0482016  90% 17% 7% 46%
2.3Cython 4.214.228,996945  2% 2% 100% 2%
2.3Python 3 #5 8.804.2369,3642016  77% 75% 38% 22%
2.3Python 3 #5 8.804.2370,6242016  67% 68% 31% 46%
2.3Python 3 #5 8.794.2475,1322016  55% 63% 63% 32%
2.3Cython 4.244.258,916945  2% 100% 2% 3%
2.3Cython 4.254.258,964945  2% 3% 3% 100%
2.4Python development version #5 9.874.4791,1282016  66% 73% 25% 57%
2.4Python development version #5 9.964.5189,8282016  61% 67% 75% 19%
2.4Python development version #5 10.034.5791,7162016  63% 73% 75% 10%
2.5Pyston 4.614.6132,988900  100% 0% 0% 0%
2.5Pyston 4.624.6333,028900  100% 0% 0% 0%
2.5Pyston 4.634.6332,960900  0% 26% 0% 74%
3.2Python 3 #4 5.955.969,0321698  3% 3% 1% 98%
3.2Python 3 #4 5.965.979,1681698  1% 3% 100% 4%
3.2Python 3 #4 5.965.978,9361698  9% 37% 3% 65%
3.5Python 2 6.556.5513,516900  2% 3% 2% 100%
3.5Python 3 #3 6.576.598,9961647  1% 1% 1% 100%
3.5Python 3 #3 6.616.628,9801647  1% 100% 2% 1%
3.5Python 3 #3 6.626.638,9841647  2% 1% 100% 2%
3.5Python 2 6.646.6513,444900  3% 100% 4% 4%
3.6Nuitka 6.806.8110,660904  2% 2% 100% 2%
3.6Nuitka 6.816.8210,648904  18% 3% 83% 2%
3.7Nuitka 6.856.8610,420904  5% 87% 13% 2%
3.7Python 2 6.866.8713,316900  15% 2% 88% 3%
3.7Nuitka #2 6.896.8910,736889  2% 2% 3% 100%
3.7Nuitka #2 6.896.9010,736889  4% 4% 3% 100%
3.7Nuitka #2 6.906.9110,452889  3% 3% 3% 100%
3.7Python development version #4 7.027.027,8201698  0% 94% 0% 6%
3.7Python development version #4 7.027.027,7761698  100% 0% 0% 0%
3.8Python development version #4 7.037.037,5201698  0% 25% 29% 46%
3.8Python development version #3 7.187.187,7721647  0% 19% 0% 81%
3.8Python development version #3 7.207.207,5361647  0% 100% 0% 0%
3.8Python development version #3 7.207.207,4921647  69% 0% 31% 0%
4.2Python 3 7.787.799,140904  2% 2% 2% 100%
4.2Python 3 7.797.818,976904  1% 1% 100% 1%
4.2Python 3 #2 7.877.888,972889  1% 1% 2% 100%
4.2Python 3 #2 7.887.909,168889  11% 0% 90% 1%
4.2Python 3 7.907.919,140904  1% 100% 1% 1%
4.3Python 3 #2 7.957.979,216889  4% 2% 2% 100%
4.6Python development version 8.688.687,732904  0% 51% 0% 49%
4.6Python development version 8.708.707,428904  0% 100% 0% 0%
4.7Python development version 8.788.787,480904  100% 0% 0% 0%
5.0Python development version #2 9.389.387,684889  0% 0% 100% 0%
5.0Python development version #2 9.419.417,640889  100% 0% 0% 0%
5.0Python development version #2 9.429.427,528889  0% 0% 0% 100%
7.9Graal #2 16.8314.78515,420889  11% 97% 7% 6%
7.9Graal #2 16.8814.79494,224889  17% 13% 85% 6%
8.0Graal 17.1614.95511,932904  65% 11% 7% 43%
8.1Graal 17.3315.12509,700904  33% 33% 8% 70%
8.3Graal 17.6515.49458,936904  16% 42% 13% 64%
8.3Graal #2 17.7515.63455,788889  12% 81% 9% 20%
11Jython 24.7620.91305,304900  41% 22% 27% 28%
11Jython 25.4021.24316,384900  31% 32% 36% 20%
11MicroPython 21.3821.464,448904  27% 41% 7% 45%
12Jython 25.9521.93299,620900  21% 45% 29% 24%
12MicroPython #2 22.0722.124,148889  2% 3% 3% 100%
12MicroPython #2 22.1122.174,324889  87% 5% 6% 10%
12MicroPython 22.1422.214,276904  15% 31% 54% 19%
12MicroPython 22.5322.654,172904  21% 33% 82% 17%
12MicroPython #2 22.8222.874,304889  20% 2% 81% 4%
missing benchmark programs
IronPython No program
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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