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 2.800.89293,3921009  80% 94% 74% 78%
1.1PyPy 2 2.860.9580,3201009  73% 94% 68% 72%
1.1PyPy 2 2.840.98296,1041009  74% 82% 71% 71%
1.3PyPy 3 #2 3.131.2077,4201008  63% 60% 56% 91%
1.4PyPy 3 3.251.2578,7601271  94% 58% 58% 58%
1.4PyPy 3 3.261.2579,4401271  94% 58% 59% 59%
1.4PyPy 3 #6 1.241.2665,064552  5% 2% 1% 99%
1.4PyPy 3 3.291.2679,0801271  61% 58% 56% 92%
1.4PyPy 3 #2 3.211.2777,3001008  59% 57% 87% 58%
1.4PyPy 3 #2 3.201.2877,5481008  60% 87% 56% 57%
1.4PyPy 3 #3 3.791.2979,048894  71% 68% 68% 95%
1.5PyPy 3 #6 1.271.2964,904552  9% 2% 98% 2%
1.5PyPy 3 #6 1.241.3065,192552  100% 1% 0% 0%
1.5PyPy 3 #3 3.811.3579,412894  69% 62% 65% 94%
1.6PyPy 3 #4 4.101.3979,5481069  74% 92% 69% 68%
1.6PyPy 3 #4 4.131.3979,0681069  72% 94% 71% 69%
1.6PyPy 3 #3 3.831.3977,532894  64% 92% 63% 64%
1.7PyPy 3 #4 4.231.4778,9521069  95% 66% 66% 68%
3.0Nuitka #3 9.892.6861,248894  94% 97% 91% 96%
3.0Nuitka #3 10.012.6960,260894  97% 95% 94% 94%
3.0Nuitka #3 9.962.7061,040894  95% 93% 96% 91%
3.1Pyston 10.352.73158,5601009  95% 93% 99% 94%
3.1Nuitka #4 10.022.7859,8361069  96% 93% 97% 95%
3.1Nuitka #4 10.152.7861,3441069  95% 93% 93% 92%
3.2Cython 10.672.8254,0001027  95% 96% 96% 99%
3.2Pyston 10.382.83157,8721009  92% 89% 95% 91%
3.2Nuitka #4 10.242.8361,0921069  92% 92% 96% 89%
3.2Cython 10.612.8753,9601027  94% 95% 92% 96%
3.3Cython 10.612.8954,0721027  96% 95% 96% 95%
3.3Pyston 10.422.89157,5841009  87% 90% 89% 96%
3.7Python 3 #3 12.623.2953,628894  99% 94% 98% 97%
3.8Python 3 #3 12.573.3353,100894  95% 96% 98% 92%
3.8Python 3 #3 12.653.4053,480894  91% 96% 93% 96%
3.8Python 3 #4 13.083.4152,7641069  97% 97% 99% 94%
3.9Python 3 #4 13.033.4754,1121069  95% 96% 92% 95%
4.0Python 3 #4 13.083.5354,4001069  93% 95% 93% 93%
4.2Python development version #3 14.293.7649,848894  96% 95% 96% 92%
4.2Nuitka #2 14.503.7758,9921008  98% 98% 98% 97%
4.3Python development version #3 14.273.7851,924894  95% 94% 92% 97%
4.3Python development version #3 14.453.8151,844894  98% 95% 96% 92%
4.3Python development version #4 14.823.8649,7201069  98% 96% 97% 93%
4.3Python development version #4 14.763.8649,7761069  97% 96% 93% 98%
4.4Python development version #4 15.003.9151,8081069  94% 99% 95% 96%
4.5Nuitka #2 14.943.9759,7161008  95% 98% 96% 97%
4.5Nuitka #2 14.933.9861,0641008  97% 95% 96% 95%
4.9Python 2 16.554.3741,9321009  97% 98% 96% 96%
4.9Python 2 16.304.3743,2921009  96% 95% 94% 95%
5.2Nuitka 17.114.5959,0761271  96% 95% 97% 97%
5.2Nuitka 17.084.6358,1241271  96% 96% 95% 93%
5.2Nuitka 17.094.6457,6761271  96% 92% 96% 94%
5.2Python 2 17.084.6543,6201009  97% 96% 98% 97%
5.4Python 3 #2 18.444.7753,6321008  99% 96% 99% 98%
5.4Python 3 #2 18.324.8053,9081008  97% 95% 99% 97%
5.5Python 3 #2 18.244.8653,8041008  96% 95% 94% 96%
5.5Nuitka #6 4.864.8710,172552  5% 3% 100% 3%
5.5Nuitka #6 4.864.8710,204552  5% 5% 5% 100%
5.5Nuitka #6 4.894.9010,372552  3% 2% 100% 2%
6.2Python development version #2 21.025.5249,6961008  96% 97% 93% 95%
6.3Python 3 21.375.5753,5441271  98% 97% 98% 95%
6.3Python 3 21.515.5954,0801271  96% 97% 96% 99%
6.3Python development version #2 21.555.6149,6761008  98% 96% 97% 94%
6.3Python development version #2 21.705.6149,7201008  98% 94% 98% 96%
6.3Python 3 21.375.6253,3201271  96% 97% 98% 96%
6.7Python 3 #6 5.975.989,024552  2% 2% 1% 100%
6.8Python 3 #6 6.016.029,048552  2% 1% 100% 1%
6.8Python 3 #6 6.036.048,844552  100% 1% 1% 1%
6.8Graal #6 7.336.05462,992552  22% 100% 3% 3%
6.8Graal #6 7.316.05463,356552  95% 27% 5% 35%
7.3Graal #6 8.236.51461,284552  14% 91% 42% 20%
7.4Python development version 25.536.5751,8801271  98% 97% 98% 96%
7.4Python development version 25.526.6051,8161271  97% 97% 95% 98%
7.8Python development version 26.616.9150,0361271  97% 95% 97% 96%
7.9Python development version #6 6.997.007,676552  0% 0% 100% 0%
7.9Python development version #6 7.027.027,660552  0% 33% 0% 67%
8.1Python development version #6 7.167.167,476552  100% 0% 0% 0%
9.5MicroPython #6 8.438.464,188552  87% 15% 7% 6%
9.7MicroPython #6 8.548.584,048552  6% 58% 6% 49%
9.7MicroPython #6 8.618.654,192552  88% 19% 7% 7%
missing benchmark programs
Jython No program
IronPython 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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