fannkuch-redux benchmark N=10

Each chart bar shows how many times slower, one ↓ fannkuch-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 sortsort
  ×   Program Source Code CPU secs Elapsed secs Memory KB Code B ≈ CPU Load
1.0PyPy 2 2.861.00295,1601009  75% 71% 80% 76%
1.1PyPy 2 2.891.0580,6361009  71% 84% 67% 70%
1.1PyPy 2 2.881.0580,8961009  72% 63% 88% 66%
1.3PyPy 3 3.201.2683,2801271  60% 58% 56% 90%
1.3PyPy 3 3.301.3283,0081271  65% 56% 80% 70%
1.3PyPy 3 #6 1.321.3469,036552  14% 4% 95% 6%
1.3PyPy 3 #6 1.331.3569,132552  17% 99% 16% 17%
1.4PyPy 3 3.361.3682,7401271  60% 89% 57% 62%
1.4PyPy 3 #6 1.361.3868,996552  19% 17% 22% 99%
1.5PyPy 3 #3 3.991.4882,268894  95% 70% 67% 66%
1.5PyPy 3 #3 4.031.5282,236894  68% 65% 82% 72%
1.6PyPy 3 #4 4.351.5683,8241069  71% 69% 94% 68%
1.6PyPy 3 #4 4.291.5783,7321069  71% 65% 90% 63%
1.6PyPy 3 #4 4.381.5882,8081069  69% 66% 92% 66%
1.6PyPy 3 #2 3.281.6081,1241008  66% 99% 97% 67%
1.7PyPy 3 #3 4.141.6982,324894  75% 79% 75% 85%
2.3PyPy 3 #2 3.592.2980,9001008  78% 88% 100% 97%
2.6PyPy 3 #2 3.512.6182,4201008  80% 82% 85% 99%
2.7Pyston 10.352.73158,5601009  95% 93% 99% 94%
2.8Pyston 10.382.83157,8721009  92% 89% 95% 91%
2.9Pyston 10.422.89157,5841009  87% 90% 89% 96%
3.0Nuitka #3 10.602.9756,428894  95% 95% 90% 97%
3.0Nuitka #4 10.722.9757,0041069  95% 95% 96% 89%
3.0Nuitka #3 10.593.0056,904894  99% 97% 94% 97%
3.0Nuitka #4 10.813.0355,9001069  96% 96% 94% 88%
3.0Nuitka #3 10.643.0456,044894  94% 92% 88% 94%
3.2Nuitka #4 11.473.1756,9601069  96% 94% 93% 93%
3.3Graal #6 3.783.26510,436552  28% 2% 3% 94%
3.3Python development version #3 12.393.2652,732894  96% 94% 98% 95%
3.3Graal #6 3.783.27514,456552  9% 10% 100% 10%
3.3Graal #6 3.803.27515,452552  8% 24% 94% 4%
3.3Python development version #3 12.483.2852,352894  92% 96% 96% 98%
3.4Python development version #4 12.763.3652,5801069  95% 96% 95% 99%
3.4Python development version #3 12.513.4052,376894  96% 89% 95% 92%
3.4Python development version #4 12.933.4052,5921069  100% 92% 97% 96%
3.5Python 3 #3 11.833.4652,828894  97% 96% 90% 97%
3.5Python development version #4 12.803.4652,6161069  96% 96% 90% 95%
3.5Python 3 #4 12.583.5552,9441069  95% 98% 90% 94%
3.6Python 3 #4 12.493.5852,4961069  96% 91% 92% 93%
3.6Python 3 #4 12.493.6253,0521069  93% 92% 96% 90%
3.7Python 3 #3 12.113.6652,808894  96% 93% 93% 95%
3.7Python 3 #3 11.923.6853,056894  94% 95% 93% 96%
4.0Nuitka #2 14.574.0456,8481008  97% 95% 97% 94%
4.4Python 2 16.554.3741,9321009  97% 98% 96% 96%
4.4Python 2 16.304.3743,2921009  96% 95% 94% 95%
4.5Nuitka #2 14.844.5256,4721008  98% 95% 95% 94%
4.6Nuitka 16.434.5757,0161271  99% 97% 99% 98%
4.6Python 2 17.084.6543,6201009  97% 96% 98% 97%
5.0Python development version #2 18.734.9752,1801008  97% 95% 94% 95%
5.0Python development version #2 19.214.9952,3241008  98% 97% 98% 96%
5.0Nuitka #6 4.995.009,524552  3% 7% 2% 100%
5.1Python development version #2 19.245.0952,0521008  94% 95% 95% 98%
5.1Nuitka #6 5.105.119,360552  8% 8% 100% 3%
5.2Python 3 #2 18.195.1752,8721008  97% 99% 95% 96%
5.2Nuitka #2 14.115.1756,2841008  98% 99% 97% 99%
5.2Nuitka #6 5.225.239,468552  10% 14% 3% 100%
5.3Python 3 #2 18.255.2752,8681008  99% 98% 97% 97%
5.4Python 3 #2 18.155.3853,0001008  93% 98% 98% 97%
5.6Python development version 21.685.5652,1601271  98% 99% 99% 98%
5.7Python development version 21.785.6752,8041271  95% 98% 98% 99%
5.7Python development version 21.885.7152,2041271  99% 95% 97% 98%
5.9Python 3 21.445.8852,5641271  97% 98% 94% 97%
5.9Python 3 21.465.9353,2081271  94% 98% 97% 93%
6.0Python 3 21.766.0153,3241271  95% 95% 98% 95%
6.4Python development version #6 6.366.378,456552  2% 100% 0% 0%
6.4Python development version #6 6.406.418,516552  3% 3% 1% 100%
6.4Python development version #6 6.426.438,412552  2% 1% 100% 1%
6.5Python 3 #6 6.496.508,548552  12% 100% 10% 8%
6.5Nuitka 16.516.5058,0961271  99% 99% 100% 99%
6.6Python 3 #6 6.586.598,440552  20% 100% 10% 11%
6.7Nuitka 16.596.7257,4321271  98% 99% 99% 98%
6.8Python 3 #6 6.506.798,452552  90% 19% 9% 7%
8.5MicroPython #6 8.538.544,260552  0% 0% 0% 100%
8.6MicroPython #6 8.558.564,220552  0% 100% 1% 1%
8.9MicroPython #6 8.928.944,244552  1% 59% 0% 42%
missing benchmark programs
Jython No program
IronPython No program
Cython No program
Shedskin No program
Numba No program
Grumpy No program

 fannkuch-redux benchmark : Indexed-access to tiny integer-sequence

diff program output N = 7 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.

For N = 7 programs should generate these permutations (40KB) - which, incidentally, seem to be in the same order as permutations generated by the Tompkins-Paige algorithm, see pages 150-151 Permutation Generation Methods Robert Sedgewick.

The fannkuch benchmark is defined by programs in Performing Lisp Analysis of the FANNKUCH Benchmark, Kenneth R. Anderson and Duane Rettig.

Each program should

The conjecture is that this maximum count is approximated by n*log(n) when n goes to infinity.

FANNKUCH is an abbreviation for the German word Pfannkuchen, or pancakes, in analogy to flipping pancakes.


Thanks to Oleg Mazurov for insisting on a checksum and providing this helpful description of the approach he took -

Revised BSD license

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