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  0% 100% 1% 0%
33Fortran Intel #4 2.992.992601327  0% 1% 100% 1%
36C gcc #5 3.293.292841261  2% 1% 1% 100%
36C++ g++ #4 6.566.562841266  0% 0% 1% 100%
36C gcc 7.047.042841185  0% 100% 0% 1%
36C gcc #4 5.055.052841221  1% 100% 1% 1%
66Fortran Intel 6.326.335241155  0% 0% 100% 0%
66Fortran Intel #3 4.444.445241190  0% 1% 100% 0%
75OCaml #3 12.0512.066001042  1% 1% 100% 0%
78C++ g++ #2 4.284.286201105  0% 100% 0% 1%
78C++ g++ 4.744.756201033  0% 100% 1% 2%
95Go 8.778.777601036  0% 1% 1% 100%
109C++ g++ #5 2.312.328721543  1% 0% 0% 100%
109C++ g++ #3 4.214.228721286  0% 100% 0% 1%
151Ada 2005 GNAT 9.099.101,2081346  1% 0% 100% 1%
151Ada 2005 GNAT #5 4.244.241,2082186  0% 1% 1% 100%
173C gcc #6 5.221.801,3841914  96% 89% 99% 9%
305Haskell GHC #2 3.923.652,440979  100% 3% 3% 2%
320Perl #2 7 min7 min2,556886  31% 35% 0% 36%
320Perl #4 5 min5 min2,560934  20% 38% 7% 38%
324PHP #2 6 min6 min2,5881006  1% 35% 22% 44%
324PHP #3 118.72118.762,5921030  0% 100% 1% 1%
345Go #3 6.581.902,7601344  87% 86% 89% 87%
537Python 3 238.94239.194,296792  11% 11% 62% 18%
542Python 3 #3 109.42109.464,332977  23% 2% 100% 53%
606Rust 6.736.744,8481211  0% 1% 1% 100%
619Rust #2 3.773.784,9481392  1% 0% 2% 100%
630Haskell GHC #5 3.812.545,0361378  38% 27% 21% 66%
641Ruby #5 240.42240.855,124987  45% 20% 29% 9%
980Lisp SBCL #6 7.867.867,8361751  0% 0% 100% 1%
981Lisp SBCL #3 11.7211.737,8441579  0% 1% 0% 100%
1,298Go #2 13.538.2710,3801388  33% 57% 49% 27%
1,652Haskell GHC #6 6.052.2113,2161567  61% 61% 91% 62%
1,911Erlang HiPE 97.2997.3215,2881039  100% 1% 0% 1%
1,942Haskell GHC #7 3.342.1115,5321550  44% 21% 44% 50%
2,254Racket 50.1350.1218,0281054  0% 0% 1% 100%
2,427C# Mono #2 7.167.1719,4121180  1% 100% 1% 1%
2,456Racket #3 13.4213.4319,6481276  0% 0% 1% 100%
2,550F# Mono 10.1210.1220,400978  1% 0% 100% 1%
2,755Java  #4 5.145.0622,0401507  2% 1% 3% 98%
2,837Java  #2 7.927.8122,6961240  1% 8% 92% 1%
2,900Dart 8.708.6023,2001386  0% 1% 2% 100%
2,944Scala 10.3810.2423,5521080  1% 16% 85% 1%
2,964Scala #3 6.506.3923,7081053  5% 97% 2% 1%
3,015Erlang HiPE #2 49.8749.8824,1161164  1% 1% 1% 99%
3,061Lisp SBCL #2 16.0516.0624,4841617  0% 1% 100% 1%
3,062Lisp SBCL 14.8314.8324,4921419  1% 0% 100% 0%
3,638Java  #5 5.231.7329,1042457  70% 68% 95% 70%
5,421C# Mono #4 9.589.5943,3681505  1% 90% 0% 11%
6,428Perl 113.68113.7251,420838  1% 0% 0% 100%
6,837Clojure #6 11.089.6954,6961653  6% 7% 96% 7%
7,527Clojure #5 13.0011.2660,2161839  5% 10% 96% 7%
16,030Ruby #2 284.20284.29128,236732  16% 42% 1% 42%
24,648Ruby #4 240.98241.22197,180904  44% 47% 4% 7%
79,436Ruby JRuby 270.58264.02635,484760  14% 14% 43% 33%
309,988Python 3 #5 127.5445.962,479,9041933  76% 65% 78% 80%
Haskell GHC #4 Bad Output1413
Haskell GHC Bad Output1421
OCaml #6 Failed1161
"wrong" (different) algorithm / less comparable programs
50C gcc #2 2.462.464001169
1,005Haskell GHC #3 13.4312.538,0361408
1,904Perl #5 2.302.3015,2321113

 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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