fannkuch-redux benchmark N=10

Each chart bar shows how many times more Memory, one ↓ fannkuch-redux 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
1.0MicroPython #6 8.348.354,164552  1% 7% 100% 2%
1.0MicroPython #6 8.358.574,184552  31% 61% 1% 14%
1.0MicroPython #6 8.368.644,236552  1% 77% 2% 27%
1.9Python development version #6 6.166.167,788552  5% 1% 100% 1%
1.9Python development version #6 6.106.117,832552  6% 1% 1% 100%
1.9Python development version #6 6.196.257,960552  26% 1% 78% 1%
2.0Python 3 #6 6.316.318,416552  5% 0% 100% 0%
2.1Python 3 #6 6.256.268,864552  4% 1% 100% 0%
2.2Python 3 #6 6.256.268,964552  5% 87% 1% 13%
2.4Nuitka #6 4.094.1010,016552  11% 3% 95% 7%
2.4Nuitka #6 4.004.1610,080552  83% 18% 2% 8%
2.4Nuitka #6 3.994.0710,140552  46% 1% 58% 4%
10Python 2 16.554.3741,9321009  97% 98% 96% 96%
10Python 2 16.304.3743,2921009  96% 95% 94% 95%
10Python 2 17.084.6543,6201009  97% 96% 98% 97%
11Python development version #2 18.714.8646,5001008  99% 99% 99% 99%
11Python development version #4 13.813.6046,6641069  98% 99% 96% 99%
11Python development version #3 13.263.5146,752894  99% 95% 96% 97%
11Python development version #3 13.363.5346,800894  98% 98% 93% 99%
11Python development version 21.515.7046,8321271  97% 99% 97% 93%
11Python development version 21.485.7046,9361271  96% 99% 97% 94%
11Python development version #2 18.364.8547,0001008  96% 99% 98% 95%
11Python development version #2 18.554.9547,0241008  96% 98% 97% 94%
11Python development version #3 13.233.5047,140894  98% 98% 92% 99%
11Python development version #4 13.893.6447,1441069  97% 100% 96% 99%
11Python development version #4 13.623.5947,3441069  98% 99% 92% 99%
11Python 3 #3 13.173.4747,392894  97% 99% 91% 99%
11Python 3 #4 13.653.5847,5121069  98% 99% 92% 99%
11Python development version 21.665.6647,5281271  99% 98% 96% 99%
11Python 3 23.146.1247,5481271  100% 96% 96% 94%
11Python 3 #2 19.305.1047,8561008  99% 94% 96% 96%
12Python 3 #4 13.573.5547,9481069  94% 98% 98% 99%
12Python 3 #2 19.765.1148,0241008  99% 99% 97% 99%
12Python 3 #3 13.033.4448,036894  97% 99% 91% 99%
12Python 3 23.676.0848,0881271  99% 100% 98% 99%
12Python 3 #2 19.625.0848,0881008  98% 99% 96% 99%
12Python 3 #3 13.093.4848,124894  100% 92% 96% 95%
12Python 3 23.276.0548,1321271  98% 99% 96% 99%
12Python 3 #4 13.623.6348,6321069  99% 93% 95% 95%
12Nuitka #2 12.233.2251,8281008  98% 98% 97% 98%
13Nuitka 15.538.2352,2481271  99% 100% 100% 100%
13Nuitka #2 12.263.1852,3721008  98% 98% 98% 98%
13Nuitka #2 12.173.2552,7481008  98% 96% 97% 95%
13Nuitka 15.725.3553,0241271  99% 98% 95% 99%
13Nuitka #3 10.892.8553,116894  97% 98% 96% 99%
13Nuitka #3 10.862.8553,296894  99% 99% 96% 98%
13Nuitka 16.204.3453,4601271  96% 99% 98% 96%
13Nuitka #3 10.722.8553,632894  100% 94% 95% 95%
13Nuitka #4 10.656.3754,3881069  100% 100% 100% 100%
13Nuitka #4 11.084.5454,7121069  100% 96% 100% 100%
13Nuitka #4 11.353.1754,7761069  100% 98% 95% 97%
16PyPy 3 #6 1.281.3168,152552  31% 2% 3% 72%
16PyPy 3 #6 1.271.2968,184552  4% 74% 2% 25%
16PyPy 3 #6 1.311.3368,248552  6% 2% 1% 99%
19PyPy 3 #2 3.401.3477,6081008  56% 96% 54% 55%
19PyPy 3 #2 3.411.3777,8441008  87% 52% 66% 51%
19PyPy 3 #2 3.371.3678,0681008  87% 64% 53% 52%
19PyPy 3 #4 4.381.6278,6641069  65% 91% 58% 63%
19PyPy 3 #4 4.391.6178,8081069  66% 64% 92% 63%
19PyPy 3 3.331.3079,2121271  60% 57% 90% 57%
19PyPy 3 #4 4.441.6279,2161069  64% 93% 63% 64%
19PyPy 3 3.291.2779,3081271  61% 57% 91% 57%
19PyPy 3 3.331.3179,3241271  59% 92% 54% 55%
19PyPy 3 #3 4.311.5580,196894  68% 92% 62% 65%
19PyPy 3 #3 4.361.6280,616894  66% 91% 61% 61%
20PyPy 3 #3 4.331.6481,256894  93% 61% 62% 57%
20PyPy 2 3.151.0981,9081009  73% 69% 90% 67%
20PyPy 2 3.151.0981,9721009  73% 93% 69% 67%
20PyPy 2 3.141.0482,1801009  76% 73% 73% 90%
38Pyston 10.422.89157,5841009  87% 90% 89% 96%
38Pyston 10.382.83157,8721009  92% 89% 95% 91%
38Pyston 10.352.73158,5601009  95% 93% 99% 94%
127Graal #6 23.7113.46528,504552  64% 72% 44% 6%
127Graal #6 22.2512.70529,844552  46% 45% 59% 40%
131Graal #6 24.6013.81546,308552  61% 55% 40% 29%
missing benchmark programs
Jython No program
IronPython No program
Cython No program
Shedskin No program
Numba No program
Grumpy No program
RustPython 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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