A full parallel Quicksort algorithm for multicore processors

The problem addressed in this paper is that we want to sort an integer array a[] of length n in parallel on a multi core machine with p cores using Quicksort. Amdahl’s law tells us that the inherent sequential part of any algorithm will in the end dominate and limit the speedup we get from parallelisation. This paper introduces ParaQuick, a full parallel quicksort algorithm for use on an ordinary shared memory multi core machine that has just a few simple statements in its sequential part. It can be seen as an improvement over traditional parallelization of the Quicksort algorithm, where one follows the sequential algorithm and substitute recursive calls with the creation of parallel threads for these calls in the top of the recursion tree. The ParaQuick algorithm, starts with k parallel threads, where k is a multiple of p (here k = 8*p) in a k way partition of the original array with the same pivot value, and hence we get 2k partitioned areas in the first pass. We then calculate where the pivot index, the division between the small and large elements if this had been ordinary sequential Quicksort partition. In full parallel we then swap all small elements to the right of this pivot index with the large elements to the left of this pivot index – these two ‘displaced’ sets are by definition of equal size. We can then recursively with half of the threads now do the left part, and with the other half of the threads the right part (more details and synchronization considerations in the paper). Finally, when there is only one thread left working on one such area, sequential Quicksort and Insertionsort are used, as in the traditional way of doing parallel Quicksort. In the last part of the paper, this new algorithm is empirically tested against two other algorithms and Arrays.sort from the Java library. Five different distributions of the numbers to be sorted end three different machines with p = 2(4 hyper threaded), 4(8) and 32(64) are tested. Finally, conclusions are presented and an explanation is given why this ParaQuick algorithm for large values of n and some distributions is so much faster than a traditional parallelisation

Improving of Quicksort Algorithm performance by sequential thread Or parallel algorithms

Quicksort is well-know algorithm used for sorting, making O(n log n) comparisons to sort a dataset of n items. Being a divide-and-conquer algorithm, it is easily modified to use parallel computing. The aim of this paper is to evaluate the performance of parallel quicksort algorithm and compare it with theoretical performance analysis. To achieve this we implement a tool to do both sequential and parallel quicksort on randomly generated arrays of different size in several runs to provide us with enough data to draw conclusions about the efficiency of using the capability of modern multicore processors together with algorithms designed to increase the speed of sorting large arrays

Porting Decision Tree Algorithms to Multicore using FastFlow

The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.Comment: 18 pages + cove

Active data structures on GPGPUs

Active data structures support operations that may affect a large number of elements of an aggregate data structure. They are well suited for extremely fine grain parallel systems, including circuit parallelism. General purpose GPUs were designed to support regular graphics algorithms, but their intermediate level of granularity makes them potentially viable also for active data structures. We consider the characteristics of active data structures and discuss the feasibility of implementing them on GPGPUs. We describe the GPU implementations of two such data structures (ESF arrays and index intervals), assess their performance, and discuss the potential of active data structures as an unconventional programming model that can exploit the capabilities of emerging fine grain architectures such as GPUs

Even faster sorting of (not only) integers

In this paper we introduce RADULS2, the fastest parallel sorter based on radix algorithm. It is optimized to process huge amounts of data making use of modern multicore CPUs. The main novelties include: extremely optimized algorithm for handling tiny arrays (up to about a hundred of records) that could appear even billions times as subproblems to handle and improved processing of larger subarrays with better use of non-temporal memory stores