Review Request 110262: KRandomSequence::randomize: use the Fisher-Yates Algorithm to achieve O(N) complexity
Frank Reininghaus
frank78ac at googlemail.com
Thu May 2 10:43:38 BST 2013
> On May 1, 2013, 9:03 p.m., Parker Coates wrote:
> > Since KRandomSequence is a class for generating a predictable random sequence, is it not possible that this change would break applications that relied on the code below producing a consistent result between kdelibs releases?
> >
> > QList list = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
> > KRandomSequence pseudoRand( 123545 );
> > pseudoRand.randomize( list );
> > return list;
> >
> >
> >
I had thought about that as well, this is the reason why I propose to push that to master only, and not to KDE/4.10. Even though I think that a design which relies on the order of the items in a randomized sequence might be questionable in an application, it might make sense in unit tests. If the patch is pushed to master only, it would give people some time to react if anything breaks. At first sight, I did not notice anything in http://lxr.kde.org/ident?i=randomize that looks dangerous though.
On the other hand, if people are worried that this change might break things, then we could just deprecate the existing method and implement Fisher-Yates in a new one (maybe KRandomSequence::shuffle ?). The disadvantage would be that every application that uses the old method needs to be ported to the new one in order to stop wasting CPU cycles and prevent freezes (like when you set up a Plasma picture frame slideshow with many pictures).
- Frank
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On May 1, 2013, 8:34 p.m., Frank Reininghaus wrote:
>
> -----------------------------------------------------------
> This is an automatically generated e-mail. To reply, visit:
> http://git.reviewboard.kde.org/r/110262/
> -----------------------------------------------------------
>
> (Updated May 1, 2013, 8:34 p.m.)
>
>
> Review request for kdelibs.
>
>
> Description
> -------
>
> The current algorithm that is used to shuffle lists is rather inefficient. It works by removing the first item of the list repeatedly and inserting it at a random position in a new list, which is finally used to replace the original list. Unfortunately, this results in O(N^2) run time complexity because inserting into a list, which is done N itmes, is O(N).
>
> I propose to replace this algorithm by the Fisher-Yates algorithm, which works by swapping items N - 1 times. One could modify the entire thing further, like providing randomization also for other containers and not only QList, but that would probably be frameworks material.
>
>
> Diffs
> -----
>
> kdecore/util/krandomsequence.h 46949b4
>
> Diff: http://git.reviewboard.kde.org/r/110262/diff/
>
>
> Testing
> -------
>
> I wrote a small benchmark: http://paste.kde.org/735914/
>
> I got the following results with the existing algorithm:
>
> RESULT : Benchmark::randomSequenceBenchmark():"n=0":
> 0.000015 msecs per iteration (total: 66, iterations: 4194304)
> RESULT : Benchmark::randomSequenceBenchmark():"n=1":
> 0.000192 msecs per iteration (total: 101, iterations: 524288)
> RESULT : Benchmark::randomSequenceBenchmark():"n=3":
> 0.00070 msecs per iteration (total: 93, iterations: 131072)
> RESULT : Benchmark::randomSequenceBenchmark():"n=10":
> 0.0025 msecs per iteration (total: 83, iterations: 32768)
> RESULT : Benchmark::randomSequenceBenchmark():"n=30":
> 0.0070 msecs per iteration (total: 58, iterations: 8192)
> RESULT : Benchmark::randomSequenceBenchmark():"n=100":
> 0.023 msecs per iteration (total: 97, iterations: 4096)
> RESULT : Benchmark::randomSequenceBenchmark():"n=300":
> 0.077 msecs per iteration (total: 79, iterations: 1024)
> RESULT : Benchmark::randomSequenceBenchmark():"n=1000":
> 0.35 msecs per iteration (total: 90, iterations: 256)
> RESULT : Benchmark::randomSequenceBenchmark():"n=3000":
> 1.8 msecs per iteration (total: 58, iterations: 32)
> RESULT : Benchmark::randomSequenceBenchmark():"n=10000":
> 18 msecs per iteration (total: 72, iterations: 4)
> RESULT : Benchmark::randomSequenceBenchmark():"n=30000":
> 283 msecs per iteration (total: 283, iterations: 1)
> RESULT : Benchmark::randomSequenceBenchmark():"n=100000":
> 3,823 msecs per iteration (total: 3,823, iterations: 1)
>
> Here are the numbers for the proposed new algorithm:
>
> RESULT : Benchmark::randomSequenceBenchmark():"n=0":
> 0.000015 msecs per iteration (total: 65, iterations: 4194304)
> RESULT : Benchmark::randomSequenceBenchmark():"n=1":
> 0.000015 msecs per iteration (total: 65, iterations: 4194304)
> RESULT : Benchmark::randomSequenceBenchmark():"n=3":
> 0.00018 msecs per iteration (total: 98, iterations: 524288)
> RESULT : Benchmark::randomSequenceBenchmark():"n=10":
> 0.00079 msecs per iteration (total: 52, iterations: 65536)
> RESULT : Benchmark::randomSequenceBenchmark():"n=30":
> 0.0025 msecs per iteration (total: 83, iterations: 32768)
> RESULT : Benchmark::randomSequenceBenchmark():"n=100":
> 0.0084 msecs per iteration (total: 69, iterations: 8192)
> RESULT : Benchmark::randomSequenceBenchmark():"n=300":
> 0.025 msecs per iteration (total: 52, iterations: 2048)
> RESULT : Benchmark::randomSequenceBenchmark():"n=1000":
> 0.085 msecs per iteration (total: 88, iterations: 1024)
> RESULT : Benchmark::randomSequenceBenchmark():"n=3000":
> 0.25 msecs per iteration (total: 66, iterations: 256)
> RESULT : Benchmark::randomSequenceBenchmark():"n=10000":
> 0.85 msecs per iteration (total: 55, iterations: 64)
> RESULT : Benchmark::randomSequenceBenchmark():"n=30000":
> 2.6 msecs per iteration (total: 86, iterations: 32)
> RESULT : Benchmark::randomSequenceBenchmark():"n=100000":
> 10 msecs per iteration (total: 81, iterations: 8)
>
>
> Thanks,
>
> Frank Reininghaus
>
>
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