{"title":"ANALYSIS OF DIFFERENT PARTITIONING SCHEMES FOR PARALLEL GRAM-SCHMIDT ALGORITHMS","authors":"S. Oliveira, L. Borges, M. Holzrichter, T. Soma","doi":"10.1080/10637199808947392","DOIUrl":null,"url":null,"abstract":"In this paper we analyze implementations of parallel Gram-Schmidt orthogonalization algorithms. One of the first parallel orthogonalization of Gram-Schmidt was the row-wise partitioning of O'Leary and Whitman. In this paper we describe a pipelined implementation which uses column-wise partitioning schemes. Timing models for the column-wise parallel algorithms are derived. We compare our column-wise partitionings against the row-wise partitioning and validate our study with computational results. The pipelined orthogonalization algorithm is important because the timing analysis is independent of the architecture model. Threshold values of m max, which is the number of rows where row partitioning becomes better than column partitioning are found theoretically and verified with our experiments","PeriodicalId":406098,"journal":{"name":"Parallel Algorithms and Applications","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Parallel Algorithms and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/10637199808947392","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
In this paper we analyze implementations of parallel Gram-Schmidt orthogonalization algorithms. One of the first parallel orthogonalization of Gram-Schmidt was the row-wise partitioning of O'Leary and Whitman. In this paper we describe a pipelined implementation which uses column-wise partitioning schemes. Timing models for the column-wise parallel algorithms are derived. We compare our column-wise partitionings against the row-wise partitioning and validate our study with computational results. The pipelined orthogonalization algorithm is important because the timing analysis is independent of the architecture model. Threshold values of m max, which is the number of rows where row partitioning becomes better than column partitioning are found theoretically and verified with our experiments