fasta benchmark ≈240MB N=25,000,000

Each chart bar shows how many times slower, one ↓ fasta 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 sortsort
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0Java  #5 5.341.7229,4842457  69% 75% 73% 96%
1.9Fortran Intel #4 3.183.192601327  0% 0% 2% 100%
1.9C gcc #5 3.293.292841261  2% 1% 1% 100%
2.1Haskell GHC #2 3.943.662,464979  4% 100% 3% 4%
2.5C++ g++ #3 4.214.228721286  0% 100% 0% 1%
2.5Ada 2005 GNAT #5 4.244.241,2082186  0% 1% 1% 100%
2.5C++ g++ #2 4.284.286201105  0% 100% 0% 1%
2.6Fortran Intel #3 4.544.555241190  0% 0% 1% 100%
2.8C++ g++ 4.744.756201033  0% 100% 1% 2%
2.9Java  #4 5.135.0516,4441507  2% 1% 1% 100%
2.9C gcc #4 5.055.052841221  1% 100% 1% 1%
3.7Fortran Intel 6.346.352521155  0% 0% 1% 100%
3.7Scala #3 6.526.4120,3721053  91% 1% 11% 1%
3.8C++ g++ #4 6.566.562841266  0% 0% 1% 100%
4.1C gcc 7.047.042841185  0% 100% 0% 1%
4.2C# Mono #2 7.287.2919,3641180  100% 1% 1% 1%
4.6Java  #2 7.927.8216,7121240  54% 1% 46% 1%
4.6Pascal Free Pascal #4 7.937.9381112  0% 100% 1% 0%
4.7Lisp SBCL #6 8.018.025,7801751  1% 1% 0% 100%
5.3Ada 2005 GNAT 9.099.101,2081346  1% 0% 100% 1%
5.5Dart 9.459.4051,4281386  1% 1% 1% 99%
5.6C# Mono #4 9.689.6843,3841505  100% 0% 1% 1%
5.6Clojure #6 11.049.7050,2081653  25% 79% 6% 5%
6.0Scala 10.3910.2620,4841080  1% 85% 1% 16%
6.0F# Mono 10.2810.2920,348978  1% 100% 0% 1%
6.5Clojure #5 12.6111.1453,7641839  75% 23% 10% 6%
6.8Lisp SBCL #3 11.6311.645,5361579  0% 1% 100% 0%
7.1OCaml #3 12.1612.171,6561042  1% 0% 1% 100%
8.1Racket #3 13.9013.9117,6601276  0% 0% 100% 0%
8.6Lisp SBCL 14.7414.7523,2281419  1% 0% 0% 100%
9.5Lisp SBCL #2 16.2216.2323,2121617  1% 0% 0% 100%
9.6Go 16.4516.457721036  0% 0% 100% 1%
29Erlang HiPE #2 49.7449.758,7881164  0% 100% 0% 1%
30Racket 50.6350.6216,3641054  0% 1% 100% 0%
55Erlang HiPE 95.1095.128,6561039  99% 0% 2% 0%
68Perl 116.40116.4599,960838  0% 94% 0% 7%
89PHP #3 148.94152.272,5881030  42% 27% 10% 21%
129Ruby #5 217.79221.465,284987  36% 18% 33% 15%
129Ruby #4 218.28221.88190,380904  21% 34% 23% 25%
141Ruby JRuby 246.02241.84612,796760  23% 37% 31% 16%
144Python 3 #2 241.56246.324,324788  30% 24% 15% 33%
147Python 3 248.61253.034,324792  22% 24% 25% 31%
158Ruby #2 270.96271.73176,156732  1% 59% 1% 41%
183Perl #4 5 min5 min2,280934  1% 0% 100% 1%
220PHP #2 6 min6 min2,5921006  39% 13% 5% 45%
253Perl #2 7 min7 min2,280886  62% 0% 38% 0%
Haskell GHC #4 Bad Output1413
Haskell GHC Bad Output1421
OCaml #6 Failed1161
"wrong" (different) algorithm / less comparable programs
1.3Perl #5 2.302.3015,3081113
1.4C gcc #2 2.462.464001169
6.7Haskell GHC #3 12.5911.571,9401408
missing benchmark programs
Rust 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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