Python Interpreters Benchmarks
x64 ArchLinux : AMD® Ryzen 7 4700U®

 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 1.481.5068,2161698  1% 0% 0% 1% 0% 0% 0% 99%
1.2PyPy 2 1.731.7374,192900  100% 0% 1% 0% 0% 0% 0% 0%
1.2PyPy 3 #2 1.851.8669,432889  99% 1% 0% 0% 0% 0% 0% 0%
1.2PyPy 3 1.851.8769,116904  99% 0% 0% 0% 1% 0% 0% 0%
1.4Cython 2.102.108,772945  1% 0% 0% 0% 0% 100% 0% 0%
1.4Graal #4 4.072.11760,2761698  41% 26% 19% 19% 16% 37% 88% 48%
1.4Graal #3 4.112.17766,2681647  41% 21% 12% 27% 93% 6% 8% 31%
1.5Pyston #4 2.172.188,8441698  1% 2% 100% 2% 4% 2% 2% 4%
1.6PyPy 3 #3 2.312.3468,3801647  99% 0% 0% 0% 0% 0% 0% 1%
1.7Nuitka #5 5.582.6214,6842016  21% 32% 13% 23% 73% 7% 43% 12%
1.9Pyston #3 2.802.818,9081647  4% 2% 2% 2% 100% 5% 5% 4%
2.0Python development version #5 5.692.9915,3402016  38% 64% 45% 26% 20% 71% 31% 18%
2.2Nuitka #4 3.233.2310,7601698  1% 0% 1% 1% 0% 100% 0% 0%
2.4Pyston #2 3.553.558,824889  7% 3% 4% 4% 3% 8% 4% 100%
2.4Nuitka #3 3.553.5610,8561647  2% 100% 0% 1% 0% 1% 0% 0%
2.4Python development version #4 3.563.579,7321698  95% 6% 6% 1% 1% 3% 4% 1%
2.4PyPy 3 #5 5.853.6482,7282016  17% 0% 7% 57% 2% 0% 14% 65%
2.7Python development version 4.054.059,812904  9% 100% 5% 4% 2% 3% 6% 3%
2.8Python development version #3 4.124.139,7001647  6% 100% 2% 3% 5% 7% 8% 5%
2.8Python development version #2 4.204.209,452889  7% 100% 3% 5% 5% 2% 3% 4%
3.0Python 3 #5 7.494.4412,9362016  24% 55% 29% 21% 18% 71% 16% 18%
3.1Python 3 #4 4.584.588,1641698  14% 12% 10% 13% 8% 9% 9% 100%
3.1Nuitka 4.674.6810,688904  1% 0% 0% 0% 100% 0% 0% 0%
3.2Python 3 #3 4.834.838,3121647  3% 5% 4% 4% 100% 2% 2% 2%
3.3Nuitka #2 4.894.9010,656889  1% 1% 100% 2% 1% 0% 1% 0%
3.7Python 3 5.575.588,256904  20% 9% 8% 9% 49% 49% 6% 7%
3.9Python 3 #2 5.885.898,204889  38% 17% 12% 13% 15% 9% 71% 9%
3.9Python 2 5.875.9112,988900  0% 0% 1% 0% 0% 99% 0% 0%
7.6Graal 16.3011.40918,804904  28% 11% 78% 8% 24% 13% 19% 20%
7.7Graal #2 16.3511.51894,296889  26% 17% 25% 19% 96% 17% 10% 11%
14Jython 25.5020.973,540900  28% 13% 13% 12% 22% 32% 1% 2%
missing benchmark programs
IronPython No program
Shedskin No program
Numba No program
MicroPython No program
Grumpy No program
RustPython 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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