fasta benchmark ≈240MB N=25,000,000

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
1.0Pascal Free Pascal #4 7.937.9381112  1% 1% 0% 100%
33Fortran Intel #4 2.992.992601327  1% 1% 1% 100%
66Fortran Intel 6.336.345241155  0% 0% 1% 100%
66Fortran Intel #3 4.424.435241190  1% 0% 1% 100%
75C++ g++ #4 6.266.265961266  0% 1% 1% 100%
75OCaml #3 12.0512.066001042  0% 0% 1% 100%
83C gcc #5 3.233.246641261  1% 0% 1% 100%
83C gcc #4 5.025.026641221  1% 0% 0% 100%
84C gcc 7.047.056681185  1% 1% 0% 100%
96Go 8.768.777641036  0% 0% 1% 100%
142C++ g++ #2 4.284.291,1321105  1% 0% 0% 100%
142C++ g++ 4.854.851,1361033  0% 1% 1% 100%
149C++ g++ #3 4.324.331,1881286  1% 0% 0% 100%
149C++ g++ #5 2.902.911,1881543  1% 1% 0% 100%
194Go #3 6.536.531,5521344  1% 1% 1% 100%
205Ada 2005 GNAT 8.538.541,6361346  1% 0% 0% 100%
213Ada 2005 GNAT #5 4.314.311,7002186  1% 1% 0% 100%
229C gcc #6 3.773.781,8281914  1% 1% 0% 100%
283Perl #4 254.30254.402,264934  1% 1% 0% 100%
283Perl #2 5 min5 min2,264886  1% 1% 0% 100%
310Haskell GHC #2 3.303.302,476979  0% 2% 1% 100%
323PHP #2 6 min6 min2,5841006  1% 0% 1% 100%
324PHP #3 118.32118.362,5881030  0% 0% 1% 100%
377Haskell GHC #7 2.812.823,0121550  2% 1% 0% 100%
536Python 3 229.21229.294,288792  1% 1% 1% 100%
540Haskell GHC #6 5.945.954,3201567  1% 1% 1% 100%
541Python 3 #3 108.42108.464,328977  0% 0% 1% 100%
607Haskell GHC #5 3.043.054,8521378  10% 54% 27% 100%
635Ruby #5 241.85241.945,080987  0% 1% 1% 100%
762Rust 5.155.156,0921211  1% 0% 0% 100%
980Lisp SBCL #6 7.847.857,8361751  1% 0% 1% 100%
981Lisp SBCL #3 11.7211.737,8441579  0% 0% 1% 100%
1,061JavaScript V8 71.7471.768,488791  0% 1% 1% 100%
1,287Go #2 11.8011.8110,2961388  0% 1% 1% 100%
1,308Erlang HiPE 88.9488.9710,4641039  1% 0% 1% 100%
1,394Erlang HiPE #2 49.2249.2511,1481164  1% 0% 0% 100%
2,392Racket 50.4850.5019,1321054  0% 0% 0% 100%
2,772Racket #3 13.4113.4222,1761276  1% 14% 2% 100%
2,937Dart 8.708.7023,4921386  0% 1% 1% 100%
3,061Lisp SBCL #2 16.0516.0624,4841617  1% 0% 1% 100%
3,062Lisp SBCL 14.8514.8624,4921419  0% 1% 0% 100%
3,105Java  #4 4.804.8124,8361507  1% 1% 0% 100%
3,138Java  #2 7.527.5325,1001240  1% 0% 1% 100%
3,296Smalltalk VisualWorks 46.1246.1426,3681315  0% 0% 0% 100%
3,717Scala 10.2910.3029,7361080  1% 0% 1% 100%
3,725JavaScript V8 #2 22.2822.3029,800923  0% 1% 1% 100%
3,735Scala #3 6.436.4429,8801053  1% 1% 1% 100%
3,981Java  #5 4.974.9831,8482457  1% 1% 0% 100%
4,932C# Mono #2 7.477.4839,4561180  0% 1% 1% 100%
5,076F# Mono 10.9911.0040,608978  0% 0% 1% 100%
6,424Perl 99.6899.7251,388838  1% 0% 0% 100%
6,848Clojure #6 10.9610.9754,7841653  1% 1% 1% 100%
7,112Clojure #5 12.6012.6156,8961839  1% 1% 0% 100%
12,312C# Mono #4 10.2610.2798,4921505  0% 1% 1% 100%
16,029Ruby #2 284.32284.41128,228732  0% 0% 1% 100%
24,751Ruby #4 242.5216 min198,008904  1% 1% 0% 100%
79,230Ruby JRuby 231.53231.81633,836760  0% 1% 1% 100%
309,644Python 3 #5 126.89127.022,477,1481933  1% 1% 0% 100%
C CINT Bad Output1163
Haskell GHC #4 Bad Output1413
Haskell GHC Bad Output1421
Lua Failed1049
OCaml #6 Failed1161
"wrong" (different) algorithm / less comparable programs
160C gcc #2 2.402.401,2761169
243Haskell GHC #3 11.8311.831,9441408
1,875Perl #5 1.981.9814,9961113

 fasta benchmark : Generate and write random DNA sequences

You can write your own program for this task and contribute to the benchmarks game by following these general instructions.

More specifically:

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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