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%
2.2Python development version #5 8.824.1477,8922016  67% 71% 34% 45%
2.2Cython 4.154.168,588945  6% 10% 9% 100%
2.2Cython 3.994.178,572945  6% 100% 4% 2%
2.2Cython 4.024.178,584945  6% 86% 3% 18%
2.2PyPy 3 #5 6.504.1881,0362016  64% 30% 34% 36%
2.2Python development version #5 8.704.1872,6362016  65% 67% 46% 36%
2.2PyPy 3 #5 6.464.2181,0482016  90% 17% 7% 46%
2.3Python development version #5 8.784.2689,2882016  61% 72% 37% 43%
2.4Python 3 #5 9.564.5089,2322016  54% 75% 75% 48%
2.4Python 3 #5 9.474.5089,8002016  68% 59% 51% 68%
2.5Pyston 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%
2.6Python 3 #5 10.174.9090,4482016  70% 79% 66% 51%
3.2Python development version #4 5.945.958,8361698  1% 1% 100% 1%
3.2Python development version #4 5.955.968,8841698  2% 2% 0% 100%
3.2Python development version #4 6.006.018,7681698  2% 100% 4% 1%
3.3Python 3 #4 6.126.138,8801698  11% 7% 11% 100%
3.3Python 3 #4 6.066.198,8761698  47% 5% 61% 6%
3.4Python 3 #4 6.116.408,9601698  92% 14% 5% 8%
3.4Python development version #3 6.446.458,8841647  1% 100% 1% 1%
3.5Python development version #3 6.466.478,8081647  2% 100% 1% 1%
3.5Python development version #3 6.536.548,8641647  3% 1% 100% 1%
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 6.816.828,8761647  12% 100% 10% 11%
3.7Python 2 6.866.8713,316900  15% 2% 88% 3%
3.7Nuitka 7.007.019,580904  22% 10% 87% 5%
3.7Python 3 #3 6.767.018,8281647  74% 7% 39% 8%
3.8Nuitka 7.097.109,988904  6% 15% 8% 100%
3.8Nuitka 7.097.129,796904  35% 14% 76% 10%
3.9Nuitka #2 7.317.329,824889  100% 9% 7% 4%
4.0Python development version 7.427.438,728904  3% 100% 1% 1%
4.0Python development version 7.517.538,776904  24% 2% 23% 57%
4.0Python development version 7.587.598,840904  2% 3% 100% 1%
4.1Nuitka #2 7.277.619,752889  4% 100% 2% 7%
4.1Python development version #2 7.607.618,832889  2% 1% 0% 100%
4.1Python development version #2 7.647.648,704889  2% 1% 100% 1%
4.1Nuitka #2 7.327.669,792889  4% 100% 3% 10%
4.1Python development version #2 7.757.778,828889  6% 2% 99% 2%
4.2Python 3 7.807.818,900904  14% 100% 10% 10%
4.2Python 3 7.777.838,756904  19% 10% 91% 6%
4.3Python 3 8.018.058,820904  22% 20% 15% 93%
4.3Python 3 #2 7.938.128,816889  53% 6% 57% 6%
4.4Python 3 #2 8.208.238,744889  15% 98% 14% 13%
4.5Python 3 #2 8.458.488,748889  24% 63% 19% 55%
4.8Graal #2 10.869.07524,900889  7% 8% 94% 21%
4.8Graal #2 10.799.08531,368889  5% 24% 24% 76%
4.9Graal 10.889.15529,512904  9% 15% 52% 54%
4.9Graal 10.909.19505,660904  10% 16% 97% 22%
5.1Graal #2 11.339.60542,316889  15% 14% 88% 20%
5.7Graal 12.6110.74535,508904  47% 30% 47% 62%
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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