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.

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