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 2.810.87297,7001009  78% 78% 94% 78%
1.0PyPy 2 2.790.88296,7041009  75% 81% 77% 95%
1.0PyPy 2 2.760.90296,6761009  78% 94% 74% 71%
1.5PyPy 3 3.281.2978,4201271  93% 67% 61% 60%
1.5PyPy 3 3.311.3078,5681271  85% 63% 58% 65%
1.5PyPy 3 #6 1.291.3167,848552  98% 4% 8% 3%
1.5PyPy 3 3.311.3178,3561271  57% 57% 89% 58%
1.5PyPy 3 #6 1.301.3168,044552  2% 9% 92% 5%
1.5PyPy 3 #6 1.301.3267,704552  2% 98% 2% 5%
1.5PyPy 3 #2 3.201.3378,5201008  60% 80% 97% 63%
1.6PyPy 3 #2 3.231.3678,4041008  66% 55% 75% 66%
1.6PyPy 3 #3 3.871.3778,744894  67% 94% 66% 67%
1.6PyPy 3 #3 3.821.4278,744894  67% 90% 64% 65%
1.7PyPy 3 #3 3.851.4578,768894  92% 62% 65% 63%
1.7PyPy 3 #2 3.181.4778,5201008  70% 60% 75% 70%
1.7PyPy 3 #4 4.251.4978,2961069  97% 66% 67% 68%
1.8PyPy 3 #4 4.181.5478,2681069  71% 72% 73% 92%
1.9PyPy 3 #4 4.041.6578,5241069  70% 68% 96% 68%
3.1Pyston 10.352.73158,5601009  95% 93% 99% 94%
3.2Pyston 10.382.83157,8721009  92% 89% 95% 91%
3.3Nuitka #4 10.962.8658,2841069  98% 93% 98% 96%
3.3Nuitka #4 10.922.8756,9041069  96% 97% 98% 93%
3.3Nuitka #3 10.732.8755,688894  96% 93% 96% 91%
3.3Graal #6 3.332.88422,792552  8% 1% 15% 95%
3.3Pyston 10.422.89157,5841009  87% 90% 89% 96%
3.3Graal #6 3.342.89421,480552  14% 95% 8% 1%
3.3Nuitka #3 10.862.9156,888894  95% 90% 97% 93%
3.3Graal #6 3.372.92423,180552  61% 15% 30% 16%
3.4Nuitka #3 10.872.9457,452894  90% 97% 93% 93%
3.4Nuitka #4 11.072.9857,4161069  93% 95% 92% 95%
4.0Python 3 #3 11.833.4652,828894  97% 96% 90% 97%
4.0Python development version #3 13.233.5047,140894  98% 98% 92% 99%
4.0Python development version #3 13.263.5146,752894  99% 95% 96% 97%
4.0Python development version #3 13.363.5346,800894  98% 98% 93% 99%
4.1Python 3 #4 12.583.5552,9441069  95% 98% 90% 94%
4.1Python 3 #4 12.493.5852,4961069  96% 91% 92% 93%
4.1Python development version #4 13.623.5947,3441069  98% 99% 92% 99%
4.1Python development version #4 13.813.6046,6641069  98% 99% 96% 99%
4.2Python 3 #4 12.493.6253,0521069  93% 92% 96% 90%
4.2Python development version #4 13.893.6447,1441069  97% 100% 96% 99%
4.2Python 3 #3 12.113.6652,808894  96% 93% 93% 95%
4.2Nuitka #2 14.013.6758,3881008  98% 94% 97% 96%
4.2Python 3 #3 11.923.6853,056894  94% 95% 93% 96%
4.2Nuitka #2 13.843.6858,2761008  95% 95% 92% 97%
4.3Nuitka #2 14.323.7155,7281008  97% 100% 95% 97%
4.9Nuitka 16.564.2758,1361271  97% 98% 97% 99%
5.0Python 2 16.554.3741,9321009  97% 98% 96% 96%
5.0Python 2 16.304.3743,2921009  96% 95% 94% 95%
5.0Nuitka 16.584.3857,1281271  97% 93% 97% 95%
5.1Nuitka 16.794.4155,0401271  99% 96% 96% 94%
5.3Python 2 17.084.6543,6201009  97% 96% 98% 97%
5.6Python development version #2 18.364.8547,0001008  96% 99% 98% 95%
5.6Python development version #2 18.714.8646,5001008  99% 99% 99% 99%
5.6Nuitka #6 4.874.889,884552  100% 0% 1% 1%
5.7Nuitka #6 4.934.939,632552  1% 1% 100% 1%
5.7Python development version #2 18.554.9547,0241008  96% 98% 97% 94%
5.9Python 3 #2 18.195.1752,8721008  97% 99% 95% 96%
6.0Python 3 #2 18.255.2752,8681008  99% 98% 97% 97%
6.2Python 3 #2 18.155.3853,0001008  93% 98% 98% 97%
6.5Python development version 21.665.6647,5281271  99% 98% 96% 99%
6.5Python development version 21.515.7046,8321271  97% 99% 97% 93%
6.5Python development version 21.485.7046,9361271  96% 99% 97% 94%
6.7Python 3 21.445.8852,5641271  97% 98% 94% 97%
6.8Python 3 21.465.9353,2081271  94% 98% 97% 93%
6.9Python 3 21.766.0153,3241271  95% 95% 98% 95%
7.0Python development version #6 6.106.117,832552  6% 1% 1% 100%
7.1Python development version #6 6.166.167,788552  5% 1% 100% 1%
7.2Python development version #6 6.196.257,960552  26% 1% 78% 1%
7.4Python 3 #6 6.496.508,548552  12% 100% 10% 8%
7.6Python 3 #6 6.586.598,440552  20% 100% 10% 11%
7.8Python 3 #6 6.506.798,452552  90% 19% 9% 7%
9.8MicroPython #6 8.538.544,260552  0% 0% 0% 100%
9.8MicroPython #6 8.558.564,220552  0% 100% 1% 1%
10MicroPython #6 8.928.944,244552  1% 59% 0% 42%
17Nuitka #6 14.5314.549,888552  1% 100% 1% 1%
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

  Home   Conclusions   License   Play