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.

     sortsortsort
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0PyPy 3 #4 1.891.9168,8081698  3% 99% 1% 3%
1.0PyPy 3 #4 1.891.9168,9721698  7% 2% 93% 6%
1.0PyPy 3 #4 1.961.9868,9521698  41% 4% 60% 3%
1.3PyPy 2 2.532.5580,204900  68% 6% 33% 1%
1.3PyPy 2 2.542.5980,688900  74% 29% 1% 1%
1.4PyPy 2 2.622.6580,640900  2% 6% 99% 1%
1.4PyPy 3 2.712.7472,476904  2% 3% 4% 99%
1.5PyPy 3 2.762.7872,476904  3% 3% 3% 100%
1.5PyPy 3 #2 2.772.7971,868889  2% 2% 99% 4%
1.5PyPy 3 #2 2.802.8371,980889  99% 3% 5% 3%
1.5PyPy 3 2.852.8772,500904  3% 7% 99% 2%
1.5PyPy 3 #2 2.852.8871,936889  38% 66% 3% 3%
1.7PyPy 3 #3 3.133.1569,4001647  57% 1% 45% 3%
1.7PyPy 3 #3 3.173.1969,1281647  2% 4% 2% 99%
1.7PyPy 3 #3 3.203.2269,0601647  2% 43% 2% 60%
2.1Cython 3.983.988,488945  100% 1% 2% 2%
2.1Cython 3.994.008,616945  2% 2% 2% 100%
2.1Cython 4.034.048,428945  100% 1% 1% 2%
2.4Pyston 4.614.6132,988900  100% 0% 0% 0%
2.4Pyston 4.624.6333,028900  100% 0% 0% 0%
2.5Pyston 4.634.6332,960900  0% 26% 0% 74%
2.6Nuitka #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%
3.0Python development version #4 5.625.668,0921698  15% 1% 90% 1%
3.0Python development version #4 5.715.718,0001698  6% 2% 100% 1%
3.1Nuitka #4 5.815.829,9161698  6% 0% 94% 1%
3.2Python 3 #4 6.066.198,8761698  47% 5% 61% 6%
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 3 #4 6.116.408,9601698  92% 14% 5% 8%
3.2Python 3 #4 6.126.138,8801698  11% 7% 11% 100%
3.3Python development version #3 6.146.158,0721647  8% 5% 100% 9%
3.4PyPy 3 #5 6.423.9681,1962016  36% 44% 41% 50%
3.4PyPy 3 #5 6.453.9581,2762016  35% 31% 53% 51%
3.4PyPy 3 #5 6.453.9480,1562016  37% 69% 46% 21%
3.5Python 2 6.556.5513,516900  2% 3% 2% 100%
3.5Python 3 #3 6.586.598,8081647  8% 5% 100% 6%
3.5Python 2 6.646.6513,444900  3% 100% 4% 4%
3.6Python 3 #3 6.767.018,8281647  74% 7% 39% 8%
3.6Python 3 #3 6.816.828,8761647  12% 100% 10% 11%
3.6Python 2 6.866.8713,316900  15% 2% 88% 3%
3.7Nuitka 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%
3.9Python development version #2 7.287.297,836889  5% 4% 1% 97%
3.9Python development version 7.307.328,084904  6% 1% 99% 1%
3.9Python development version #2 7.317.328,072889  5% 1% 1% 100%
3.9Python development version 7.357.367,960904  5% 1% 100% 1%
3.9Python development version #2 7.377.477,832889  35% 5% 73% 5%
3.9Nuitka #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%
4.0Python development version #5 7.483.98103,8762016  35% 32% 73% 59%
4.0Python development version #5 7.503.99101,9162016  24% 27% 86% 62%
4.0Python development version #5 7.574.00103,4122016  23% 57% 75% 44%
4.1Python 3 7.777.838,756904  19% 10% 91% 6%
4.1Python 3 7.807.818,900904  14% 100% 10% 10%
4.2Python 3 #2 7.938.128,816889  53% 6% 57% 6%
4.2Nuitka 7.997.999,804904  1% 17% 1% 83%
4.2Python 3 8.018.058,820904  22% 20% 15% 93%
4.3Python 3 #2 8.208.238,744889  15% 98% 14% 13%
4.5Python 3 #2 8.458.488,748889  24% 63% 19% 55%
5.0Python 3 #5 9.474.5089,8002016  68% 59% 51% 68%
5.0Nuitka #5 9.484.0990,5122016  58% 55% 55% 67%
5.1Python 3 #5 9.564.5089,2322016  54% 75% 75% 48%
5.2Nuitka #5 9.754.2176,0762016  77% 64% 67% 27%
5.2Nuitka #5 9.854.23104,5762016  66% 70% 72% 36%
5.4Python 3 #5 10.174.9090,4482016  70% 79% 66% 51%
12MicroPython 21.9421.964,660904  11% 0% 89% 0%
12MicroPython 21.9822.004,452904  0% 100% 0% 0%
12MicroPython 22.7122.744,516904  0% 0% 22% 78%
12MicroPython #2 23.0823.104,624889  0% 0% 100% 0%
12MicroPython #2 23.1123.134,536889  0% 1% 100% 0%
13MicroPython #2 23.8323.874,608889  0% 15% 0% 85%
13Jython 24.7620.91305,304900  41% 22% 27% 28%
13Jython 25.4021.24316,384900  31% 32% 36% 20%
14Jython 25.9521.93299,620900  21% 45% 29% 24%
162Graal 5 min135.56420,444904  60% 55% 61% 57%
171Graal #2 5 min143.46421,148889  55% 56% 65% 59%
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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