Johannes Spazier, Steffen Christgau, Bettina Schnor
{"title":"多核集群中MATLAB模板应用的高效并行化","authors":"Johannes Spazier, Steffen Christgau, Bettina Schnor","doi":"10.1109/WOLFHPC.2016.7","DOIUrl":null,"url":null,"abstract":"This paper presents the automatic parallelization of Stencil codes written in MATLAB for distributed systems. The compiler translates MATLAB source into C code and automatically parallelizes using MPI. For clusters of multi-cores, also a hybrid approach is presented where the generated code combines MPI and OpenMP parallelization. The parallelization concepts are evaluated with two stencil computation benchmarks. The automatically generated code not only outperforms the MATLAB code, but shows also a very good scaling.","PeriodicalId":59014,"journal":{"name":"高性能计算技术","volume":"115 1","pages":"20-29"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient Parallelization of MATLAB Stencil Applications for Multi-core Clusters\",\"authors\":\"Johannes Spazier, Steffen Christgau, Bettina Schnor\",\"doi\":\"10.1109/WOLFHPC.2016.7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the automatic parallelization of Stencil codes written in MATLAB for distributed systems. The compiler translates MATLAB source into C code and automatically parallelizes using MPI. For clusters of multi-cores, also a hybrid approach is presented where the generated code combines MPI and OpenMP parallelization. The parallelization concepts are evaluated with two stencil computation benchmarks. The automatically generated code not only outperforms the MATLAB code, but shows also a very good scaling.\",\"PeriodicalId\":59014,\"journal\":{\"name\":\"高性能计算技术\",\"volume\":\"115 1\",\"pages\":\"20-29\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"高性能计算技术\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.1109/WOLFHPC.2016.7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"高性能计算技术","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.1109/WOLFHPC.2016.7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient Parallelization of MATLAB Stencil Applications for Multi-core Clusters
This paper presents the automatic parallelization of Stencil codes written in MATLAB for distributed systems. The compiler translates MATLAB source into C code and automatically parallelizes using MPI. For clusters of multi-cores, also a hybrid approach is presented where the generated code combines MPI and OpenMP parallelization. The parallelization concepts are evaluated with two stencil computation benchmarks. The automatically generated code not only outperforms the MATLAB code, but shows also a very good scaling.