My Parallel Sort Library that is more efficient version 4.03 is here..

[amin...@gmail.com]

Hello,



My Parallel Sort Library that is more efficient version 4.03 is here..

You can download it from:

https://sites.google.com/site/scalable68/parallel-sort-library-that-is-more-efficient

I have come with a "powerful" Parallel Sort library that is very efficient, and it comes with the source code, please read about it below:

Author: Amine Moulay Ramdane

Description:

Parallel Sort Library that supports Parallel Quicksort, Parallel HeapSort and Parallel MergeSort on Multicores systems.

Parallel Sort Library uses my Thread Pool Engine and sort many array parts - of your array - in parallel using Quicksort or HeapSort or MergeSort and after that it finally merge them - with the merge() procedure -

In the previous parallelsort version i have parallelized only the sort part, but in this new parallelsort version i have parallelized also the merge procedure part and it gives better performance.

My new parallel sort algorithm has become more cache-aware, and i have done some benchmarks with my new parallel algorithm and it has given up to 5X scalability on a Quadcore when sorting strings, other than that i have cleaned more the code and i think my parallel Sort library has become a more professional and industrial parallel Sort library , you can be confident cause i have tested it thoroughly and no bugs have showed , so i hope you will be happy with my new Parallel Sort library.

I have also included a "test.pas" example, just compile first the "gendata.pas" inside the zip file and run it first, after that compile the "test.pas" example and run it and do your benchmarks.

I have implemented a Parallel hybrid divide-and-conquer merge algorithm that performs 0.9-5.8 times better than sequential merge, on a quad-core processor, with larger arrays outperforming by over 5 times. Parallel processing combined with a hybrid algorithm approach provides a powerful high performance result.

My algorithm of finding the median of Parallel merge of my Parallel Sort Library that you will find here in my website:

https://sites.google.com/site/scalable68/parallel-sort-library

Is O(log(min(|A|,|B|))), where |A| is the size of A, since the binary search is performed within the smaller array and is O(lgN). But this new algorithm of finding the median of parallel merge of my Parallel Sort Library is O(log(|A|+|B|)), which is slightly worse. With further optimizations the order was reduced to O(log(2*min(|A|,|B|))), which is better, but is 2X more work, since both arrays may have to be searched. All algorithms are logarithmic. Two binary searches were necessary to find an even split that produced two equal or nearly equal halves. Luckily, this part of the merge algorithm is not performance critical. So, more effort can be spent looking for a better split. This new algorithm in the parallel merge balances the recursive binary tree of the divide-and-conquer and improve the worst-case performance of parallel merge sort.

Why are we finding the median in the parallel algorithm ?

Here is my previous idea of finding the median that is O(log(min(|A|,|B|))) to understand better:

Let's assume we want to merge sorted arrays X and Y. Select X[m] median element in X. Elements in X[ .. m-1] are less than or equal to X[m]. Using binary search find index k of the first element in Y greater than X[m]. Thus Y[ .. k-1] are less than or equal to X[m] as well. Elements in X[m+1..] are greater than or equal to X[m] and Y[k .. ] are greater. So merge(X, Y) can be defined as concat(merge(X[ .. m-1], Y[ .. k-1]), X[m], merge(X[m+1.. ], Y[k .. ])) now we can recursively in parallel do merge(X[ .. m-1], Y[ .. k-1]) and merge(X[m+1 .. ], Y[k .. ]) and then concat results.


Thank you,
Amine Moulay Ramdane.