fasta-redux benchmark N=2,500,000
Each chart bar shows how many times slower, one ↓ fasta-redux 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 |
|---|---|---|---|---|---|---|
| 1.0 | Cython | 0.11 | 0.12 | ? | 1169 | 100% 0% 0% 0% 0% 0% 0% 0% |
| 1.1 | Pyston | 0.13 | 0.13 | ? | 1115 | 0% 100% 8% 0% 0% 0% 0% 0% |
| 1.1 | Pyston #7 | 0.13 | 0.13 | ? | 1115 | 7% 25% 13% 0% 69% 7% 0% 7% |
| 1.3 | Python development version | 0.15 | 0.15 | ? | 1115 | 0% 7% 100% 0% 0% 0% 7% 6% |
| 1.3 | Python development version #7 | 0.15 | 0.15 | ? | 1115 | 12% 100% 0% 0% 0% 0% 7% 0% |
| 1.3 | PyPy 3 | 0.15 | 0.15 | ? | 1115 | 100% 0% 0% 0% 6% 0% 0% 0% |
| 1.3 | PyPy 3 #7 | 0.15 | 0.15 | ? | 1115 | 6% 100% 0% 0% 0% 12% 0% 0% |
| 1.5 | Python 3 | 0.17 | 0.17 | ? | 1115 | 0% 12% 88% 0% 6% 0% 11% 0% |
| 1.5 | Python 3 #7 | 0.17 | 0.17 | ? | 1115 | 100% 6% 6% 0% 0% 0% 0% 6% |
| 1.6 | PyPy 2 | 0.19 | 0.19 | ? | 1115 | 0% 0% 5% 100% 5% 5% 0% 0% |
| 1.7 | Nuitka #7 | 0.19 | 0.19 | ? | 1115 | 5% 0% 100% 0% 0% 0% 0% 0% |
| 1.7 | Nuitka | 0.19 | 0.19 | ? | 1115 | 0% 0% 100% 0% 0% 0% 0% 0% |
| 3.8 | PyPy 3 #6 | 0.44 | 0.44 | 72,692 | 1040 | 0% 0% 0% 100% 2% 0% 0% 2% |
| 10 | Pyston #6 | 1.19 | 1.19 | 11,208 | 1040 | 3% 4% 3% 3% 100% 2% 2% 3% |
| 12 | Nuitka #6 | 1.42 | 1.42 | 14,532 | 1040 | 1% 2% 0% 0% 0% 100% 0% 0% |
| 13 | Python 3 #6 | 1.54 | 1.54 | 14,028 | 1040 | 1% 0% 100% 0% 1% 1% 0% 1% |
| 14 | Python development version #6 | 1.56 | 1.57 | 12,884 | 1040 | 3% 1% 100% 1% 1% 3% 1% 1% |
| 46 | Graal #7 | 8.62 | 5.34 | 816,376 | 1115 | 34% 64% 16% 22% 26% 21% 42% 13% |
| 48 | Graal | 9.11 | 5.56 | 812,096 | 1115 | 34% 16% 43% 44% 46% 23% 24% 28% |
| 81 | Graal #6 | 14.32 | 9.29 | 917,104 | 1040 | 65% 17% 16% 21% 26% 60% 24% 12% |
| missing benchmark programs | ||||||
| Python 2 | No program | |||||
| Jython | No program | |||||
| IronPython | No program | |||||
| Shedskin | No program | |||||
| Numba | No program | |||||
| MicroPython | No program | |||||
| Grumpy | No program | |||||
| RustPython | No program | |||||
fasta-redux 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
- generate DNA sequences, by copying from a given sequence
- generate DNA sequences, by weighted random selection from 2 alphabets
- convert the expected probability of selecting each nucleotide into cumulative probabilities
- encode the cumulative probabilities as blocks of duplicate lookup-table items
- select an initial lookup-table item using a random number as an approximate index
- match that random number against the cumulative probabilities to select the nucleotide
- use this linear congruential generator to calculate a random number each time a nucleotide needs to be selected (don't cache the random number sequence)
IM = 139968 IA = 3877 IC = 29573 Seed = 42 Random () Seed = (Seed * IA + IC) modulo IM = Seed / IM
- write 3 sequences line-by-line in FASTA format