Parallel Performance of MPI Sorting Algorithms on Dual-Core Processor Windows-Based Systems

Message Passing Interface (MPI) is widely used to implement parallel programs. Although Windowsbased architectures provide the facilities of parallel execution and multi-threading, little attention has been focused on using MPI on these platforms. In this paper we use the dual core Window-based platform to study the effect of parallel processes number and also the number of cores on the performance of three MPI parallel implementations for some sorting algorithms

Applications for Multicore System

A multi-core processor is a single computing unit with two or more processors (“cores”). These cores are integrated into a single IC for enhanced performance, reduced power consumption and more efficient simultaneous processing of multiple tasks. Homogeneous multi-core systems include only identical cores, whereas heterogeneous multi-core systems have cores that are not identical. Most of the computers and workstations these days have multicore processors. However most software programs are not designed to make use of multi-core processors and hence even though we run these programs on the new machines equipped with multicore processors, we don’t see sizable improvements in application performance. The idea behind improved performance is in parallelizing the code and distributing the work amongst multiple cores, but writing programming logic to achieve this is complex. The conventional model of lock-based parallelism for writing such programs is difficult in use, error-prone and does not always lead to efficient use of the resources but with the help of OpenMP, programmers have enhanced support for parallel programming. In this work I have implemented quicksort algorithm using OpenMP library and analysed the performance in terms of execution time

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

Parallel String Sample Sort

We discuss how string sorting algorithms can be parallelized on modern multi-core shared memory machines. As a synthesis of the best sequential string sorting algorithms and successful parallel sorting algorithms for atomic objects, we propose string sample sort. The algorithm makes effective use of the memory hierarchy, uses additional word level parallelism, and largely avoids branch mispredictions. Additionally, we parallelize variants of multikey quicksort and radix sort that are also useful in certain situations.Comment: 34 pages, 7 figures and 12 table

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

Performance Debugging and Tuning using an Instruction-Set Simulator

Instruction-set simulators allow programmers a detailed level of insight into, and control over, the execution of a program, including parallel programs and operating systems. In principle, instruction set simulation can model any target computer and gather any statistic. Furthermore, such simulators are usually portable, independent of compiler tools, and deterministic-allowing bugs to be recreated or measurements repeated. Though often viewed as being too slow for use as a general programming tool, in the last several years their performance has improved considerably. We describe SIMICS, an instruction set simulator of SPARC-based multiprocessors developed at SICS, in its rôle as a general programming tool. We discuss some of the benefits of using a tool such as SIMICS to support various tasks in software engineering, including debugging, testing, analysis, and performance tuning. We present in some detail two test cases, where we've used SimICS to support analysis and performance tuning of two applications, Penny and EQNTOTT. This work resulted in improved parallelism in, and understanding of, Penny, as well as a performance improvement for EQNTOTT of over a magnitude. We also present some early work on analyzing SPARC/Linux, demonstrating the ability of tools like SimICS to analyze operating systems

Analysing the Performance of Divide-and-Conquer Algorithms on Multicore Processors

Multicore systems are widely gaining popularity because of the significant avail-ability and performance increase over the single core systems. Multicore systems have a lesser power consumption and heat generation than that of the multiple single core systems. The different compiler support provided by different vendors also make multicore programming one of the main area of research. The multicore programming utilises the power of multiple cores to parallelise a task. The widely used algorithm paradigms for multicore programming are the Divide and Conquer algorithms. The divide and conquer algorithms are candidate problem for the multicore programming because divide and conquer algorithm divides a problem into sub- problems which can be solved by distributing the sub-problems among the different cores and parallel solve them. A wide range of divide and conquer algorithm has been parallelized. In this paper, we have taken two of the widely used divide and conquer algorithms, quick sort and convex hull, parallel implemented them to analyse their performance gain in compared to the sequential version of the algorithm. The parallel implementations distribute the load onto the multiple cores, parallel work upon the loads and finally merge individual results of the each core. We have also proposed a scheme for efficient merging of the parallel sorted sub-arrays in the quick sort. We have taken the mean and standard deviation theory for efficient merging of the sorted sub-arrays. The OpenMP programming model has been used for the implementation of the programs. The processor architecture used for analysing the behaviour of the algorithm is a shared memory based processo

Engineering Parallel String Sorting

We discuss how string sorting algorithms can be parallelized on modern multi-core shared memory machines. As a synthesis of the best sequential string sorting algorithms and successful parallel sorting algorithms for atomic objects, we first propose string sample sort. The algorithm makes effective use of the memory hierarchy, uses additional word level parallelism, and largely avoids branch mispredictions. Then we focus on NUMA architectures, and develop parallel multiway LCP-merge and -mergesort to reduce the number of random memory accesses to remote nodes. Additionally, we parallelize variants of multikey quicksort and radix sort that are also useful in certain situations. Comprehensive experiments on five current multi-core platforms are then reported and discussed. The experiments show that our implementations scale very well on real-world inputs and modern machines.Comment: 46 pages, extension of "Parallel String Sample Sort" arXiv:1305.115