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 1.891.9168,8081698  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%
1.4PyPy 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%
2.1Python development version #5 8.013.9489,4682016  54% 65% 64% 29%
2.1PyPy 3 #5 6.453.9480,1562016  37% 69% 46% 21%
2.1PyPy 3 #5 6.453.9581,2762016  35% 31% 53% 51%
2.1PyPy 3 #5 6.423.9681,1962016  36% 44% 41% 50%
2.1Cython 4.074.078,500945  6% 1% 100% 1%
2.1Nuitka #5 9.364.1089,7322016  74% 59% 37% 67%
2.2Cython 4.114.118,740945  7% 2% 100% 1%
2.2Python development version #5 8.034.1389,2042016  59% 20% 55% 68%
2.2Python development version #5 7.994.1389,9442016  43% 29% 66% 64%
2.2Nuitka #5 9.444.1487,9522016  71% 64% 34% 67%
2.2Cython 4.054.178,784945  65% 1% 37% 1%
2.2Nuitka #5 9.644.1884,7922016  67% 63% 34% 74%
2.4Python 3 #5 9.564.5089,2322016  54% 75% 75% 48%
2.4Python 3 #5 9.474.5089,8002016  68% 59% 51% 68%
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%
2.6Python 3 #5 10.174.9090,4482016  70% 79% 66% 51%
2.6Nuitka #4 4.954.969,8521698  100% 5% 1% 1%
2.6Nuitka #4 4.964.979,6481698  1% 5% 100% 1%
2.6Nuitka #4 4.965.049,6281698  0% 40% 1% 63%
2.8Nuitka #3 5.375.389,6521647  1% 6% 1% 100%
2.8Nuitka #3 5.385.389,6681647  1% 5% 0% 100%
2.8Nuitka #3 5.435.449,6281647  1% 5% 0% 100%
3.1Python development version #4 5.845.858,1721698  1% 5% 100% 1%
3.1Python development version #4 5.875.888,1481698  100% 5% 1% 1%
3.1Python development version #4 5.996.008,3441698  100% 5% 1% 0%
3.2Python development version #3 6.036.048,1961647  1% 5% 1% 100%
3.2Python development version #3 6.046.048,1041647  0% 5% 0% 100%
3.2Python 3 #4 6.126.138,8801698  11% 7% 11% 100%
3.2Python development version #3 6.096.158,2521647  1% 25% 1% 79%
3.2Python 3 #4 6.066.198,8761698  47% 5% 61% 6%
3.4Python 3 #4 6.116.408,9601698  92% 14% 5% 8%
3.4Python 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 6.816.828,8761647  12% 100% 10% 11%
3.6Python 2 6.866.8713,316900  15% 2% 88% 3%
3.6Nuitka 6.956.969,764904  77% 5% 24% 1%
3.7Nuitka 7.007.009,720904  1% 5% 1% 100%
3.7Python 3 #3 6.767.018,8281647  74% 7% 39% 8%
3.7Nuitka 7.027.039,904904  1% 5% 1% 100%
3.8Python development version 7.307.318,088904  84% 5% 17% 0%
3.8Python development version 7.337.338,088904  1% 5% 100% 1%
3.8Nuitka #2 7.347.359,904889  1% 5% 100% 1%
3.9Nuitka #2 7.347.359,892889  100% 5% 1% 0%
3.9Nuitka #2 7.387.399,956889  1% 5% 100% 1%
4.0Python development version 7.387.568,296904  1% 56% 1% 46%
4.0Python development version #2 7.557.568,244889  100% 5% 0% 1%
4.1Python 3 7.807.818,900904  14% 100% 10% 10%
4.1Python development version #2 7.647.828,208889  1% 54% 1% 49%
4.1Python 3 7.777.838,756904  19% 10% 91% 6%
4.1Python development version #2 7.547.898,180889  1% 100% 1% 0%
4.2Python 3 8.018.058,820904  22% 20% 15% 93%
4.3Python 3 #2 7.938.128,816889  53% 6% 57% 6%
4.3Python 3 #2 8.208.238,744889  15% 98% 14% 13%
4.4Python 3 #2 8.458.488,748889  24% 63% 19% 55%
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 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%
60Graal 287.84115.15399,556904  57% 72% 67% 70%
65Graal #2 5 min123.66395,616889  48% 72% 76% 64%
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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