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 2.911.0580,7081009  70% 69% 86% 64%
1.0PyPy 2 2.941.0880,9801009  65% 67% 63% 88%
1.0PyPy 2 3.001.0980,5561009  88% 70% 69% 63%
1.2PyPy 3 3.231.2378,3521271  61% 59% 61% 89%
1.2PyPy 3 #6 1.261.2866,928552  98% 2% 2% 3%
1.2PyPy 3 #6 1.281.3067,088552  2% 5% 3% 98%
1.2PyPy 3 #6 1.291.3167,296552  2% 5% 4% 98%
1.3PyPy 3 3.291.3179,3881271  58% 60% 58% 92%
1.3PyPy 3 #3 3.881.3279,000894  65% 98% 68% 68%
1.3PyPy 3 #3 3.891.3278,976894  67% 69% 68% 97%
1.3PyPy 3 #2 3.201.3378,3281008  55% 96% 54% 53%
1.3PyPy 3 #2 3.211.3378,5081008  52% 93% 51% 52%
1.3PyPy 3 #2 3.181.3478,5561008  82% 51% 52% 67%
1.3PyPy 3 3.281.4177,5721271  87% 55% 57% 62%
1.4PyPy 3 #3 3.911.4277,268894  65% 63% 66% 86%
1.4PyPy 3 #4 4.341.5077,5881069  67% 68% 68% 99%
1.5PyPy 3 #4 4.331.6178,4121069  65% 64% 62% 91%
1.6PyPy 3 #4 4.271.6477,9121069  71% 93% 65% 68%
2.6Pyston 10.352.73158,5601009  95% 93% 99% 94%
2.7Pyston 10.382.83157,8721009  92% 89% 95% 91%
2.7Nuitka #3 10.822.8656,320894  97% 97% 97% 99%
2.7Nuitka #3 10.542.8656,000894  98% 93% 97% 92%
2.8Pyston 10.422.89157,5841009  87% 90% 89% 96%
2.8Nuitka #4 10.852.9655,7201069  94% 94% 94% 98%
2.8Nuitka #4 10.832.9755,6961069  99% 91% 95% 94%
2.8Nuitka #4 10.812.9757,4041069  97% 94% 94% 92%
2.8Nuitka #3 10.552.9855,940894  91% 91% 93% 93%
3.1Graal #6 3.743.21412,464552  6% 94% 18% 2%
3.1Graal #6 3.753.22412,268552  23% 4% 2% 95%
3.1Graal #6 3.763.23412,392552  13% 93% 14% 2%
3.1Python development version #3 12.393.2652,732894  96% 94% 98% 95%
3.1Python development version #3 12.483.2852,352894  92% 96% 96% 98%
3.2Python development version #4 12.763.3652,5801069  95% 96% 95% 99%
3.3Python development version #4 12.933.4052,5921069  100% 92% 97% 96%
3.3Python development version #3 12.513.4052,376894  96% 89% 95% 92%
3.3Python 3 #3 11.833.4652,828894  97% 96% 90% 97%
3.3Python development version #4 12.803.4652,6161069  96% 96% 90% 95%
3.4Python 3 #4 12.583.5552,9441069  95% 98% 90% 94%
3.4Python 3 #4 12.493.5852,4961069  96% 91% 92% 93%
3.5Python 3 #4 12.493.6253,0521069  93% 92% 96% 90%
3.5Python 3 #3 12.113.6652,808894  96% 93% 93% 95%
3.5Python 3 #3 11.923.6853,056894  94% 95% 93% 96%
3.6Nuitka #2 13.753.7857,0161008  94% 94% 96% 93%
3.6Nuitka #2 13.773.7956,0641008  93% 94% 95% 92%
3.6Nuitka #2 13.983.8156,4161008  96% 96% 94% 94%
4.2Python 2 16.554.3741,9321009  97% 98% 96% 96%
4.2Python 2 16.304.3743,2921009  96% 95% 94% 95%
4.3Nuitka 16.604.5059,7481271  97% 95% 93% 95%
4.3Nuitka 16.654.5258,1961271  96% 96% 94% 94%
4.4Nuitka 16.764.5756,9121271  95% 96% 98% 93%
4.4Python 2 17.084.6543,6201009  97% 96% 98% 97%
4.7Nuitka #6 4.874.889,632552  7% 100% 3% 3%
4.7Nuitka #6 4.904.919,748552  7% 2% 29% 73%
4.7Nuitka #6 4.914.929,528552  6% 1% 100% 2%
4.8Python development version #2 18.734.9752,1801008  97% 95% 94% 95%
4.8Python development version #2 19.214.9952,3241008  98% 97% 98% 96%
4.9Python development version #2 19.245.0952,0521008  94% 95% 95% 98%
4.9Python 3 #2 18.195.1752,8721008  97% 99% 95% 96%
5.0Python 3 #2 18.255.2752,8681008  99% 98% 97% 97%
5.1Python 3 #2 18.155.3853,0001008  93% 98% 98% 97%
5.3Python development version 21.685.5652,1601271  98% 99% 99% 98%
5.4Python development version 21.785.6752,8041271  95% 98% 98% 99%
5.5Python development version 21.885.7152,2041271  99% 95% 97% 98%
5.6Python 3 21.445.8852,5641271  97% 98% 94% 97%
5.7Python 3 21.465.9353,2081271  94% 98% 97% 93%
5.7Python 3 21.766.0153,3241271  95% 95% 98% 95%
6.1Python development version #6 6.366.378,456552  2% 100% 0% 0%
6.1Python development version #6 6.406.418,516552  3% 3% 1% 100%
6.1Python development version #6 6.426.438,412552  2% 1% 100% 1%
6.2Python 3 #6 6.496.508,548552  12% 100% 10% 8%
6.3Python 3 #6 6.586.598,440552  20% 100% 10% 11%
6.5Python 3 #6 6.506.798,452552  90% 19% 9% 7%
8.2MicroPython #6 8.538.544,260552  0% 0% 0% 100%
8.2MicroPython #6 8.558.564,220552  0% 100% 1% 1%
8.5MicroPython #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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