Comparative analysis of the effectiveness of using fine-grained and nested parallelism to increase the speedup of parallel computing in multicore computer systems

System research and information technologies Pub Date : 2022-08-30 DOI:10.20535/srit.2308-8893.2022.2.03

Valerii Martell, Aleksandr Korochkin, O. Rusanova

{"title":"Comparative analysis of the effectiveness of using fine-grained and nested parallelism to increase the speedup of parallel computing in multicore computer systems","authors":"Valerii Martell, Aleksandr Korochkin, O. Rusanova","doi":"10.20535/srit.2308-8893.2022.2.03","DOIUrl":null,"url":null,"abstract":"The article presents a comparative analysis of the effectiveness of using parallelism of varying granularity degrees in modern multicore computer systems using the most popular programming languages and libraries (such as C#, Java, C++, and OpenMP). Based on the performed comparison, the possibilities of increasing the efficiency of computations in multicore computer systems by using combinations of medium- and fine-grained parallelism were also investigated. The results demonstrate the high potential efficiency of fine-grained parallelism when organizing intensive parallel computations. Based on these results, it can be argued that, in comparison with more traditional parallelization methods that use medium-grain parallelism, the use of separately fine-grained parallelism can reduce the computation time of a large mathematical problem by an average of 4%. The use of combined parallelism can reduce the computation time of such a problem to 5,5%. This reduction in execution time can be significant when performing very large computations.","PeriodicalId":330635,"journal":{"name":"System research and information technologies","volume":"303 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"System research and information technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20535/srit.2308-8893.2022.2.03","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

The article presents a comparative analysis of the effectiveness of using parallelism of varying granularity degrees in modern multicore computer systems using the most popular programming languages and libraries (such as C#, Java, C++, and OpenMP). Based on the performed comparison, the possibilities of increasing the efficiency of computations in multicore computer systems by using combinations of medium- and fine-grained parallelism were also investigated. The results demonstrate the high potential efficiency of fine-grained parallelism when organizing intensive parallel computations. Based on these results, it can be argued that, in comparison with more traditional parallelization methods that use medium-grain parallelism, the use of separately fine-grained parallelism can reduce the computation time of a large mathematical problem by an average of 4%. The use of combined parallelism can reduce the computation time of such a problem to 5,5%. This reduction in execution time can be significant when performing very large computations.

查看原文本刊更多论文

在多核计算机系统中，使用细粒度并行和嵌套并行提高并行计算速度的有效性对比分析

本文对使用最流行的编程语言和库(如c#、Java、c++和OpenMP)的现代多核计算机系统中使用不同粒度程度的并行性的有效性进行了比较分析。在此基础上，研究了中粒度并行和细粒度并行相结合提高多核计算机系统计算效率的可能性。结果表明，细粒度并行在组织密集并行计算时具有很高的潜在效率。基于这些结果，可以认为，与使用中粒度并行性的更传统的并行化方法相比，使用单独的细粒度并行性可以将大型数学问题的计算时间平均减少4%。使用组合并行可以将这类问题的计算时间减少到5.5%。在执行非常大的计算时，执行时间的减少是非常显著的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

System research and information technologies

自引率

0.00%

发文量