Re: [boost] Integer sorting

On Tue, Jan 22, 2019 at 4:09 AM Fenil Mehta via Boost wrote:

I communicate with this E-mail account only: fenilgmehta@gmail.com I have no other E-mail account.

Please forgive me for my mistyped time complexity, I have corrected it to O(nk). I am not quite good at calculating the time complexity. Hence there may be an error in my time complexity but the sorting idea is good.

I have been working on my project for the past 6 months and it was for the first time I heard about ska_sort. When I read its implementation, it had already implemented what I was planning to do in the near future. I could not find ska_sort in the Boost library which I had recently installed. If someone could throw some light on this, then it would be greatly appreciated. I completely agree that ska_sort is highly optimized. For all arrays of primary data types(like int, float, char) or small objects(like pair) ska_sort performance is the best. However, for arrays of large objects, ir_sort is better.

The test cases are generated completely randomly. I do not know the test cases at all when I make the comparison. The source code for test case generation is there on the GitHub[1], you can scrutinize it and a snippet is as follows: // here I pass a vector for arr template<typename T> void fillRandArray(T &arr, const int64_t &low, const int64_t &high, const int64_t &randStart, const int64_t &randEnd) { std::random_device rd; std::mt19937_64 gen(rd()); std::uniform_int_distribution<ArrayIndexType> dis(randStart, randEnd);

for (int64_t i = low; i <= high; i++) arr[i] = dis(gen); }

As of my claims, let me give the clear details of the input. ir_sort is faster when I tested it for an array of objects of size = 1000*64 bits, and array length = 2000 only. I have to test it for other conditions.

I test using this random number generation approach (in addition to the worst-case analysis): https://github.com/boostorg/sort/blob/master/example/randomgen.cpp So that I can vary the range in both the top and bottom 2 bytes of a 4-byte integer. The same data can be cast to 8-byte if you wish. I use this script to run through the various combinations of distributions and different data types: https://github.com/boostorg/sort/blob/master/tune.pl tune.pl is also capable of tuning constants to the processor with the -tune option. The optimal sorting approach varies based on input array size, data distribution, processor, and data type. 2000 randomly-distributed elements should fit in L1 cache and be split evenly by a single radix pass; the optimal approach for more complicated distributions and millions of elements, sorted data, or for 256 elements may be quite different. spreadsort is optimized to provide good performance across all these cases (for 256 elements it just uses pdqsort).

Steven, at present I have only implemented the swapping optimization for integers only. ir_sort is in the beginning phase. Once it finalized for integers, I will plan a solution for other data types. I have not looked much at the worst case distribution. At present, I am testing it for random distributions. I will have a look at the worst scenario and mostly sorted data. For memory overhead, it will require two integer arrays of size "n", and probably it can be reduced to a single array of size "n".

Spreadsort uses just the input array and kilobytes of RAM overhead for the bins. For large amounts of data the RAM overhead can be important. Your swapping approach would probably need to change if you sorted in-place.

I agree that these algorithms have been designed keeping generality as the focus. However, generally, objects are indexed based on integers, keeping this in mind I decided to write ir_sort.

The reason it is way faster for large object arrays is that I only make "n" swaps to sort the array. I use indexes to reduce the overhead of swapping. I believe the swapping optimization I have written is not limited to ir_sort, it can be used along with other sorting algorithms to improve their running speed for large objects.

Francisco, thank you for your quick response. I wait for your feedback.

[1] https://github.com/fenilgmehta/Fastest-Integer-Sort/blob/b11ae9ddb3c9cc7e9d1... [2] https://github.com/fenilgmehta/Fastest-Integer-Sort/

Regards, Fenil Mehta

On Mon, Jan 21, 2019 at 8:18 PM Steven Ross via Boost wrote:

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Fenil,

Based on the description, this looks like spreadsort without the worst case analysis, and with a new swapping optimization (I know there is room for improvement in the swapping). I expect this algorithm will perform significantly worse than std::sort (or pdqsort) in the worst-case situations, without applying similar worst-case analysis. spreadsort is comparable to std::sort in the worst case distribution for spreadsort because of careful analysis and threshold optimization. If you tweaked the spreadsort constants < https://github.com/boostorg/sort/blob/master/include/boost/sort/spreadsort/d...

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I think you should be able to get comparable performance to your algorithm, minus the impact of your swapping optimization. How does your swapping optimization perform on mostly-sorted data? spreadsort does well in this common case relative to std::sort, which is somewhat tricky with in-place radix sorting. You should try seeing how your algorithm performs on the distributions in https://github.com/boostorg/sort/blob/master/example/alrbreaker.cpp and https://github.com/boostorg/sort/blob/master/example/binaryalrbreaker.cpp, tweaking those to hit your worst case. What's the memory overhead? What's the threshold size at which it starts becoming faster than std::sort and pdqsort? comparison-based sorting is surprisingly fast on small lists of integers that fit on L1 cache. How well does it handle common prefixes in string sorting? string_sort is optimized to handle that case extremely quickly. It's a serious performance issue if you don't optimize for it.

I wish more people realized the generality of these algorithms; if you really want a fast sort, you can use spreadsort (or an equivalent algorithm), you just need to define a way to index into the key. This was published a while ago in Engineering Radix Sort, but most people use comparison sorts because they're easier to use (and for most applications, sorting compute time is minor).

On Mon, Jan 21, 2019, 3:29 AM Francisco José Tapia via Boost < boost@lists.boost.org> wrote:

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Hi Fenil,

I am Francisco Tapia, one of the maintainers of the Sort Library.

I had been watching your code, and find it interesting. But give me a week to say you something. These days I am very busy, and I expect to have time the next weekend, and examine and test your code.

Yours

Francisco Tapia

El lun., 21 ene. 2019 a las 8:41, Richard Hodges via Boost (< boost@lists.boost.org>) escribió:

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On Sat, 29 Dec 2018 at 03:36, Fenil Mehta via Boost < boost@lists.boost.org

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wrote:

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I have written a sorting algorithm which is way faster than std::sort for all array size.

"Extraordinary claims require extraordinary evidence" - Carl Sagan

https://en.wikipedia.org/wiki/Sagan_standard

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The link is as follows: https://github.com/fenilgmehta/Fastest-Integer-Sort

Some guidance would be appreciated on how to improve the project structure and the steps to get it included in boost.

Regards, Fenil Mehta

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