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 2.802.8371,980889  99% 3% 5% 3%
1.0MicroPython #2 23.1123.134,536889  0% 1% 100% 0%
1.0PyPy 3 #2 2.772.7971,868889  2% 2% 99% 4%
1.0MicroPython #2 23.8323.874,608889  0% 15% 0% 85%
1.0PyPy 3 #2 2.852.8871,936889  38% 66% 3% 3%
1.0Python development version #2 7.317.328,072889  5% 1% 1% 100%
1.0Graal #2 5 min143.46421,148889  55% 56% 65% 59%
1.0Python 3 #2 8.458.488,748889  24% 63% 19% 55%
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 8.208.238,744889  15% 98% 14% 13%
1.0MicroPython #2 23.0823.104,624889  0% 0% 100% 0%
1.0Python 3 #2 7.938.128,816889  53% 6% 57% 6%
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 2.532.5580,204900  68% 6% 33% 1%
1.0PyPy 2 2.542.5980,688900  74% 29% 1% 1%
1.0PyPy 2 2.622.6580,640900  2% 6% 99% 1%
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 min135.56420,444904  60% 55% 61% 57%
1.0PyPy 3 2.762.7872,476904  3% 3% 3% 100%
1.0Python development version 7.307.328,084904  6% 1% 99% 1%
1.0PyPy 3 2.712.7472,476904  2% 3% 4% 99%
1.0PyPy 3 2.852.8772,500904  3% 7% 99% 2%
1.0Python development version 7.357.367,960904  5% 1% 100% 1%
1.0Python 3 8.018.058,820904  22% 20% 15% 93%
1.0Nuitka 7.997.999,804904  1% 17% 1% 83%
1.0Python 3 7.777.838,756904  19% 10% 91% 6%
1.0Python 3 7.807.818,900904  14% 100% 10% 10%
1.0Nuitka 7.037.039,928904  2% 3% 100% 2%
1.0MicroPython 21.9822.004,452904  0% 100% 0% 0%
1.0MicroPython 21.9421.964,660904  11% 0% 89% 0%
1.0MicroPython 22.7122.744,516904  0% 0% 22% 78%
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 6.767.018,8281647  74% 7% 39% 8%
1.9Python development version #3 6.086.098,0601647  5% 0% 100% 1%
1.9PyPy 3 #3 3.133.1569,4001647  57% 1% 45% 3%
1.9Python 3 #3 6.586.598,8081647  8% 5% 100% 6%
1.9Python 3 #3 6.816.828,8761647  12% 100% 10% 11%
1.9Nuitka #3 5.125.129,8001647  100% 1% 1% 1%
1.9PyPy 3 #3 3.203.2269,0601647  2% 43% 2% 60%
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 3.173.1969,1281647  2% 4% 2% 99%
1.9Nuitka #4 5.815.829,9161698  6% 0% 94% 1%
1.9Python 3 #4 6.066.198,8761698  47% 5% 61% 6%
1.9Python 3 #4 6.116.408,9601698  92% 14% 5% 8%
1.9Python 3 #4 6.126.138,8801698  11% 7% 11% 100%
1.9Python development version #4 5.545.557,9641698  6% 32% 1% 70%
1.9PyPy 3 #4 1.961.9868,9521698  41% 4% 60% 3%
1.9Nuitka #4 4.904.919,9121698  1% 100% 2% 1%
1.9PyPy 3 #4 1.891.9168,8081698  3% 99% 1% 3%
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 1.891.9168,9721698  7% 2% 93% 6%
2.3Python 3 #5 9.474.5089,8002016  68% 59% 51% 68%
2.3PyPy 3 #5 6.453.9480,1562016  37% 69% 46% 21%
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 10.174.9090,4482016  70% 79% 66% 51%
2.3PyPy 3 #5 6.423.9681,1962016  36% 44% 41% 50%
2.3Python 3 #5 9.564.5089,2322016  54% 75% 75% 48%
2.3PyPy 3 #5 6.453.9581,2762016  35% 31% 53% 51%
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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