- Read again, i correct a little mistake.. - 1 Update
- My Parallel Sort Library was updated to version 3.6 - 1 Update
| Horizon68 <horizon@horizon.com>: Mar 24 09:31AM -0700 Hello, Read again, i correct a little mistake.. My Parallel Sort Library was updated to version 3.6 I have enhanced it more, and now it scales more and it is faster. Description: My 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. My algorithm of finding the median in Parallel merge 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). The idea: 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. You can download it from: https://sites.google.com/site/scalable68/parallel-sort-library Thank you, Amine Moulay Ramdane. |
| Horizon68 <horizon@horizon.com>: Mar 24 06:34AM -0700 Hello.. My Parallel Sort Library was updated to version 3.6 I have enhanced it more, and now it scales more and it is faster. Description: My 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. My algorithm of Parallel merge 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). The idea: 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. You can download it from: https://sites.google.com/site/scalable68/parallel-sort-library Thank you, Amine Moulay Ramdane. |
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