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My EasyList for Delphi and Freepascal was updated to version 1.1
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amin...@gmail.com
Feb 10, 2020, 4:05:10 PM2/10/20
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Hello,
My EasyList for Delphi and Freepascal was updated to version 1.1, read below
to know more about the changes..
You can download it from my website:
https://sites.google.com/site/scalable68/easylist-for-delphi-and-freepascal
Description:
This is EasyList, EasyList looks like a TList because and it is implemented with an array, and it also uses my powerful Parallel Sort Library to sort the array to be able to find an element with a binary search. Please take a look at the demo called test.pas to know how to use it, also you can take a look at the source code of EasyList to know how it is implemented.
The first parameter of the constructor is the method that permits the sorting algorithm and the binary search to get the value from the array of the EasyList, the second parameter of the constructor is the number of cores that you set for the powerful parallel sorting (read below about it).
You can set the method that permits the sorting algorithm and the binary search to read the value from the array of the EasyList by using SetGetValueFunc() method.
Also you can set the order of the sorting by using the Order property, you can set it to ctAscend for ascending order or ctDescend for descending order.
Read more in the following about my Powerful Parallel Library that is used by my EasyList:
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. Notice also in the source code that my Mergesort uses insertion sort like in a Timsort manner, so it is efficient.
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.
Language: FPC Pascal v3.02+ / Delphi tokyo+
http://www.freepascal.org/
Required FPC switches: -O3 -Sd
-Sd for delphi mode....
- Platform: Windows,Linux
Thank you,
Amine Moulay Ramdane.
My EasyList for Delphi and Freepascal was updated to version 1.1, read below
to know more about the changes..
You can download it from my website:
https://sites.google.com/site/scalable68/easylist-for-delphi-and-freepascal
Description:
This is EasyList, EasyList looks like a TList because and it is implemented with an array, and it also uses my powerful Parallel Sort Library to sort the array to be able to find an element with a binary search. Please take a look at the demo called test.pas to know how to use it, also you can take a look at the source code of EasyList to know how it is implemented.
The first parameter of the constructor is the method that permits the sorting algorithm and the binary search to get the value from the array of the EasyList, the second parameter of the constructor is the number of cores that you set for the powerful parallel sorting (read below about it).
You can set the method that permits the sorting algorithm and the binary search to read the value from the array of the EasyList by using SetGetValueFunc() method.
Also you can set the order of the sorting by using the Order property, you can set it to ctAscend for ascending order or ctDescend for descending order.
Read more in the following about my Powerful Parallel Library that is used by my EasyList:
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. Notice also in the source code that my Mergesort uses insertion sort like in a Timsort manner, so it is efficient.
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.
Language: FPC Pascal v3.02+ / Delphi tokyo+
http://www.freepascal.org/
Required FPC switches: -O3 -Sd
-Sd for delphi mode....
- Platform: Windows,Linux
Thank you,
Amine Moulay Ramdane.
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