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

 fasta benchmark ≈24MB N=2,500,000

Each chart bar shows how many times more Memory, one ↓ fasta program used, compared to the program that used least Memory.

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.

    sortsort sort
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0Jython 25.5020.973,540900  28% 13% 13% 12% 22% 32% 1% 2%
2.3Pyston #4 2.012.048,1401698  2% 1% 0% 0% 100% 1% 0% 1%
2.3Pyston #2 2.942.958,168889  2% 0% 0% 0% 0% 0% 100% 1%
2.3Pyston #3 2.642.648,2641647  100% 0% 1% 0% 0% 1% 1% 0%
2.5Cython 2.512.528,880945  4% 0% 0% 100% 0% 1% 0% 0%
2.5Python development version 3.703.708,940904  99% 0% 0% 0% 1% 1% 1% 1%
2.5Python development version #2 3.823.848,964889  0% 0% 0% 100% 1% 0% 2% 0%
2.6Python development version #4 3.233.249,0321698  1% 0% 0% 0% 1% 1% 0% 100%
2.6Python development version #3 3.763.779,0361647  2% 0% 0% 100% 1% 1% 1% 0%
2.9Python 3 #2 4.084.0810,368889  1% 100% 1% 0% 0% 0% 0% 0%
2.9Python 3 3.883.8810,380904  1% 1% 0% 100% 0% 1% 0% 1%
3.0Python 3 #4 3.483.4810,5081698  0% 100% 0% 0% 0% 0% 1% 0%
3.0Python 3 #3 3.943.9410,6361647  1% 0% 1% 0% 0% 1% 100% 0%
3.1Nuitka #4 3.173.2111,0081698  99% 1% 3% 1% 1% 1% 0% 0%
3.1Nuitka #3 3.383.3911,0081647  1% 1% 0% 100% 0% 0% 1% 0%
3.1Nuitka #2 4.824.8311,136889  2% 0% 1% 0% 100% 1% 0% 0%
3.1Nuitka 4.564.5611,136904  3% 0% 1% 0% 0% 0% 1% 100%
3.7Python 2 5.875.9112,988900  0% 0% 1% 0% 0% 99% 0% 0%
19Python 3 #5 4.912.7165,7202016  44% 38% 4% 3% 60% 0% 26% 12%
19Python development version #5 4.622.6165,9722016  28% 66% 2% 0% 37% 7% 3% 38%
19Pyston #5 4.172.1466,2762016  73% 4% 22% 8% 69% 9% 8% 8%
20PyPy 3 #4 1.361.3770,1481698  4% 1% 2% 1% 3% 100% 2% 2%
20PyPy 3 #3 2.322.3270,2081647  3% 2% 1% 2% 0% 1% 100% 0%
20PyPy 3 1.771.7871,184904  4% 2% 2% 1% 100% 0% 1% 2%
20PyPy 3 #2 1.871.8871,188889  5% 1% 1% 1% 3% 2% 99% 1%
20Nuitka #5 5.592.5972,2242016  69% 14% 2% 63% 38% 7% 23% 5%
22PyPy 2 1.751.7577,640900  2% 2% 2% 2% 100% 3% 2% 1%
215Graal #4 4.072.11760,2761698  41% 26% 19% 19% 16% 37% 88% 48%
216Graal #3 4.112.17766,2681647  41% 21% 12% 27% 93% 6% 8% 31%
253Graal #2 16.3511.51894,296889  26% 17% 25% 19% 96% 17% 10% 11%
260Graal 16.3011.40918,804904  28% 11% 78% 8% 24% 13% 19% 20%
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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