M. G. Montoya, C. Gil, I. García
{"title":"Parallel thinning algorithms on multicomputers: experimental study on load balancing","authors":"M. G. Montoya, C. Gil, I. García","doi":"10.1002/1096-9128(20000425)12:5%3C327::AID-CPE493%3E3.0.CO;2-4","DOIUrl":null,"url":null,"abstract":"In this work, a practical implementation of two parallel thinning algorithms on a multicomputer system are described. The solution has been conceived for a multiprocessor using the SPMD (single program multiple data) programming model. Our main goal is intended to describe our experiences on data partition/distribution among processors for parallel thinning algorithms as a representative type of algorithm where communications take place between neighbor processors and the work load for each processor depends on the input data. It will be shown how the efficiency of the parallel implementation can be improved through the application of a preprocess. This preprocess is based on the analysis of the work load balance. An analysis of the communication cost is also made. Although the results shown here are concerned with the implementations of two parallel thinning algorithms we think that our proposal about data distribution is general and useful for a wide set of algorithms in the field of image processing. Copyright © 2000 John Wiley & Sons, Ltd.","PeriodicalId":199059,"journal":{"name":"Concurr. Pract. Exp.","volume":"284 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Concurr. Pract. Exp.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/1096-9128(20000425)12:5%3C327::AID-CPE493%3E3.0.CO;2-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
多机并行细化算法:负载平衡的实验研究
在这项工作中,描述了在多计算机系统上两个并行细化算法的实际实现。该解决方案是为使用SPMD(单程序多数据)编程模型的多处理器设计的。我们的主要目标是描述我们在并行细化算法的处理器之间的数据分区/分布方面的经验,作为一种代表性的算法,在这种算法中,通信发生在相邻处理器之间,每个处理器的工作负载取决于输入数据。它将展示如何通过应用预处理来提高并行实现的效率。该预处理是基于对工作负载平衡的分析。并对通信成本进行了分析。虽然这里显示的结果涉及两种并行细化算法的实现,但我们认为我们关于数据分布的建议对于图像处理领域的一系列算法是通用的和有用的。版权所有©2000约翰威利父子有限公司
本文章由计算机程序翻译,如有差异,请以英文原文为准。