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 1.921.9567,9841698  4% 99% 3% 5%
1.0PyPy 3 #4 1.931.9567,4881698  37% 3% 65% 3%
1.0PyPy 3 #4 1.982.0067,7281698  4% 98% 5% 6%
1.3PyPy 2 2.492.5280,708900  99% 4% 4% 4%
1.3PyPy 2 2.562.5980,976900  99% 3% 2% 2%
1.4PyPy 2 2.692.7280,960900  10% 99% 5% 6%
1.4PyPy 3 2.762.7871,764904  1% 100% 4% 3%
1.4PyPy 3 #2 2.782.8171,824889  84% 2% 16% 1%
1.5PyPy 3 2.802.8272,044904  1% 100% 2% 5%
1.5PyPy 3 #2 2.812.8371,612889  1% 2% 99% 2%
1.5PyPy 3 2.832.8671,804904  59% 5% 44% 6%
1.5PyPy 3 #2 2.872.9072,312889  99% 3% 4% 2%
1.6PyPy 3 #3 3.183.2168,3201647  1% 2% 2% 99%
1.7PyPy 3 #3 3.193.2167,8681647  99% 1% 1% 2%
1.7PyPy 3 #3 3.263.2967,9081647  5% 4% 99% 2%
2.0PyPy 3 #5 6.353.9280,6442016  64% 11% 35% 59%
2.0PyPy 3 #5 6.383.9680,4522016  35% 32% 64% 36%
2.1PyPy 3 #5 6.483.9980,2362016  52% 44% 35% 37%
2.1Nuitka #5 9.484.0593,2522016  60% 67% 80% 39%
2.1Nuitka #5 9.584.1190,3762016  53% 77% 77% 39%
2.1Python development version #5 8.824.1477,8922016  67% 71% 34% 45%
2.1Nuitka #5 9.274.1482,6082016  57% 73% 70% 35%
2.1Python development version #5 8.704.1872,6362016  65% 67% 46% 36%
2.2Python development version #5 8.784.2689,2882016  61% 72% 37% 43%
2.3Python 3 #5 9.564.5089,2322016  54% 75% 75% 48%
2.3Python 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%
2.5Python 3 #5 10.174.9090,4482016  70% 79% 66% 51%
2.8Nuitka #4 5.375.389,9361698  7% 99% 4% 5%
2.8Nuitka #3 5.465.479,7681647  6% 3% 3% 100%
2.9Nuitka #4 5.575.589,6881698  6% 12% 1% 90%
2.9Nuitka #4 5.625.629,7481698  6% 3% 2% 100%
2.9Nuitka #3 5.635.649,7361647  9% 5% 100% 4%
3.1Python development version #4 5.945.958,8361698  1% 1% 100% 1%
3.1Python development version #4 5.955.968,8841698  2% 2% 0% 100%
3.1Nuitka #3 5.996.009,8881647  7% 3% 2% 100%
3.1Python development version #4 6.006.018,7681698  2% 100% 4% 1%
3.1Python 3 #4 6.126.138,8801698  11% 7% 11% 100%
3.2Python 3 #4 6.066.198,8761698  47% 5% 61% 6%
3.3Python 3 #4 6.116.408,9601698  92% 14% 5% 8%
3.3Python development version #3 6.446.458,8841647  1% 100% 1% 1%
3.3Python development version #3 6.466.478,8081647  2% 100% 1% 1%
3.4Python development version #3 6.536.548,8641647  3% 1% 100% 1%
3.4Python 2 6.556.5513,516900  2% 3% 2% 100%
3.4Python 3 #3 6.586.598,8081647  8% 5% 100% 6%
3.4Python 2 6.646.6513,444900  3% 100% 4% 4%
3.5Nuitka 6.806.819,744904  7% 2% 2% 100%
3.5Python 3 #3 6.816.828,8761647  12% 100% 10% 11%
3.5Python 2 6.866.8713,316900  15% 2% 88% 3%
3.6Nuitka 6.966.979,724904  7% 2% 100% 2%
3.6Python 3 #3 6.767.018,8281647  74% 7% 39% 8%
3.7Nuitka #2 7.277.289,748889  7% 4% 3% 100%
3.8Python development version 7.427.438,728904  3% 100% 1% 1%
3.9Python development version 7.517.538,776904  24% 2% 23% 57%
3.9Python development version 7.587.598,840904  2% 3% 100% 1%
3.9Python development version #2 7.607.618,832889  2% 1% 0% 100%
3.9Python development version #2 7.647.648,704889  2% 1% 100% 1%
3.9Nuitka 7.667.679,784904  7% 86% 2% 16%
4.0Python development version #2 7.757.778,828889  6% 2% 99% 2%
4.0Python 3 7.807.818,900904  14% 100% 10% 10%
4.0Python 3 7.777.838,756904  19% 10% 91% 6%
4.0Nuitka #2 7.857.869,932889  7% 68% 2% 35%
4.1Python 3 8.018.058,820904  22% 20% 15% 93%
4.2Nuitka #2 8.098.109,784889  6% 69% 2% 34%
4.2Python 3 #2 7.938.128,816889  53% 6% 57% 6%
4.2Python 3 #2 8.208.238,744889  15% 98% 14% 13%
4.4Python 3 #2 8.458.488,748889  24% 63% 19% 55%
6.0Graal #2 16.5411.67492,424889  32% 6% 17% 93%
6.0Graal 16.6311.69477,176904  71% 21% 23% 32%
6.1Graal 16.9311.78473,520904  52% 26% 41% 29%
6.1Graal #2 17.1611.94477,712889  51% 20% 30% 49%
6.3Graal #2 16.9912.29477,020889  67% 42% 33% 8%
6.3Graal 16.7712.35505,224904  38% 35% 17% 57%
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%
11MicroPython 21.9421.964,660904  11% 0% 89% 0%
11MicroPython 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%
12MicroPython #2 23.8323.874,608889  0% 15% 0% 85%
missing benchmark programs
IronPython No program
Cython 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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