Python Interpreters Benchmarks
x64 ArchLinux : Intel® i5-7200U®

 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 28.1123.693,372900  36% 20% 33% 40%
1.3MicroPython 22.3422.804,284904  47% 30% 1% 29%
1.3MicroPython #2 23.4223.454,412889  6% 1% 2% 100%
2.4Python development version #2 6.026.038,016889  6% 1% 2% 100%
2.4Python development version #3 5.475.488,0481647  7% 2% 100% 2%
2.4Python development version 5.865.878,052904  6% 2% 1% 100%
2.4Python development version #4 4.544.758,0601698  100% 1% 1% 2%
2.5Python 3 6.516.528,560904  7% 1% 100% 1%
2.6Cython 3.343.498,652945  100% 2% 2% 1%
2.6Python 3 #2 6.576.588,672889  6% 2% 3% 100%
2.7Python 3 #4 5.135.148,9521698  6% 100% 2% 1%
2.7Python 3 #3 5.835.839,0561647  7% 100% 2% 2%
2.9Nuitka #4 4.164.169,7601698  5% 1% 100% 2%
2.9Nuitka #2 6.406.419,852889  6% 1% 2% 100%
2.9Nuitka 5.825.989,864904  62% 42% 2% 1%
2.9Nuitka #3 4.774.999,9001647  100% 1% 1% 1%
3.8Python 3 #5 7.304.0812,8322016  44% 58% 54% 34%
3.9Python development version #5 7.124.2513,1562016  66% 36% 42% 34%
3.9Python 2 6.466.4713,176900  6% 2% 100% 2%
4.0Nuitka #5 9.384.6013,6282016  51% 62% 46% 55%
9.3Pyston 5.965.9731,404900  7% 2% 99% 2%
20PyPy 3 #4 1.992.1068,2681698  99% 2% 3% 2%
20PyPy 3 #3 3.453.4768,9121647  7% 3% 97% 1%
21PyPy 3 #2 2.712.7471,692889  8% 97% 3% 2%
21PyPy 3 2.702.7271,836904  8% 2% 2% 100%
23PyPy 2 2.512.5478,516900  7% 3% 99% 1%
24PyPy 3 #5 7.114.4981,5602016  45% 39% 57% 29%
165Graal 19.3515.07557,268904  56% 17% 45% 21%
166Graal #2 19.8514.95558,920889  19% 31% 74% 20%
missing benchmark programs
IronPython No program
Shedskin No program
Numba 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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