Each chart bar shows how many times slower, one ↓ reverse-complement 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 | sort | sort | ||||
× | Program Source Code | CPU secs | Elapsed secs | Memory KB | Code B | ≈ CPU Load |
---|---|---|---|---|---|---|
Python development version #4 | 0.01 | ? | 449 | |||
Python 2 #4 | 0.01 | ? | 432 | |||
Python 2 #5 | 0.01 | 0.01 | ? | 455 | 100% 0% 0% 0% 0% 0% 0% 0% | |
RustPython #5 | 0.01 | 0.01 | ? | 458 | 50% 0% 0% 0% 0% 50% 0% 0% | |
Python 2 #3 | 0.01 | 0.01 | ? | 431 | 100% 0% 33% 0% 0% 0% 0% 0% | |
Pyston #6 | 0.01 | 0.01 | ? | 878 | 0% 50% 0% 0% 0% 0% 100% 0% | |
Python development version #5 | 0.01 | 0.01 | ? | 458 | 0% 0% 0% 100% 0% 0% 0% 0% | |
Python development version #6 | 0.01 | 0.01 | ? | 878 | 100% 0% 0% 0% 0% 0% 0% 0% | |
Python development version #3 | 0.02 | 0.02 | ? | 410 | 0% 67% 0% 0% 0% 0% 33% 50% | |
Cython #4 | 0.02 | 0.02 | ? | 449 | 0% 0% 0% 0% 0% 0% 100% 0% | |
Cython #5 | 0.02 | 0.02 | ? | 458 | 100% 0% 0% 0% 0% 0% 0% 0% | |
Cython #3 | 0.02 | 0.02 | ? | 410 | 0% 0% 100% 0% 0% 0% 0% 50% | |
Python 3 #4 | 0.02 | 0.02 | ? | 449 | 0% 0% 0% 0% 0% 0% 100% 0% | |
Python 3 #6 | 0.02 | 0.02 | ? | 878 | 0% 0% 67% 33% 0% 0% 0% 0% | |
Python 3 #5 | 0.02 | 0.02 | ? | 458 | 33% 0% 100% 0% 0% 0% 0% 0% | |
Python 3 #3 | 0.02 | 0.03 | ? | 410 | 100% 33% 33% 0% 0% 0% 25% 0% | |
Nuitka #5 | 0.03 | 0.03 | ? | 458 | 0% 0% 0% 0% 0% 0% 100% 0% | |
Nuitka #3 | 0.03 | 0.03 | ? | 410 | 0% 0% 0% 100% 0% 0% 0% 0% | |
Nuitka #4 | 0.03 | 0.03 | ? | 449 | 25% 0% 100% 0% 25% 0% 0% 0% | |
Nuitka #6 | 0.03 | 0.03 | ? | 878 | 40% 0% 0% 100% 0% 0% 0% 0% | |
PyPy 3 #4 | 0.06 | 0.06 | ? | 449 | 14% 0% 14% 0% 0% 17% 0% 100% | |
PyPy 3 #5 | 0.06 | 0.06 | ? | 458 | 0% 0% 0% 0% 0% 0% 0% 100% | |
PyPy 3 #6 | 0.06 | 0.06 | ? | 878 | 0% 0% 100% 0% 0% 0% 0% 0% | |
PyPy 2 #4 | 0.06 | 0.06 | ? | 432 | 0% 14% 0% 0% 0% 14% 14% 100% | |
PyPy 2 #3 | 0.06 | 0.07 | ? | 431 | 0% 14% 14% 0% 14% 100% 25% 25% | |
PyPy 2 #5 | 0.06 | 0.07 | ? | 455 | 0% 0% 0% 0% 0% 0% 14% 86% | |
PyPy 3 #3 | 0.12 | 0.13 | ? | 410 | 23% 0% 0% 100% 0% 0% 8% 0% | |
Graal #4 | 0.49 | 0.28 | 1,012 | 449 | 26% 0% 25% 0% 11% 7% 21% 89% | |
Graal #5 | 0.50 | 0.28 | 1,008 | 458 | 72% 24% 0% 28% 7% 29% 4% 25% | |
Graal #6 | 0.52 | 0.29 | 3,412 | 878 | 3% 28% 0% 25% 13% 29% 86% 4% | |
Graal #3 | 1.84 | 0.73 | 655,756 | 410 | 56% 11% 0% 46% 92% 0% 51% 1% | |
Jython #3 | 4.28 | 1.94 | 3,360 | 431 | 19% 35% 30% 31% 62% 10% 26% 13% | |
Jython #4 | 4.20 | 2.00 | 3,600 | 432 | 30% 17% 18% 14% 25% 28% 18% 60% | |
Jython #5 | 4.25 | 2.03 | 3,544 | 455 | 28% 27% 44% 4% 30% 24% 32% 22% | |
missing benchmark programs | ||||||
IronPython | No program | |||||
Shedskin | No program | |||||
Numba | No program | |||||
MicroPython | No program | |||||
Grumpy | No program |
diff program output for this 10KB input file (generated with the fasta program N = 1000) with this 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.
We use the FASTA file generated by the fasta benchmark as input for this benchmark. Note: the file may include both lowercase and uppercase codes.
Each program should
We use these code complements:
code meaning complement A A T C C G G G C T/U T A M A or C K R A or G Y W A or T W S C or G S Y C or T R K G or T M V A or C or G B H A or C or T D D A or G or T H B C or G or T V N G or A or T or C N
"by knowing the sequence of bases of one strand of DNA we immediately know the sequence of the DNA strand which will bind to it, this strand is called the reverse complement"
DNA: Structure and Function