{"title":"Cirrus 超级计算机上的矩阵乘法算法性能分析","authors":"Temitayo Adefemi","doi":"arxiv-2408.15384","DOIUrl":null,"url":null,"abstract":"Matrix multiplication is integral to various scientific and engineering\ndisciplines, including machine learning, image processing, and gaming. With the\nincreasing data volumes in areas like machine learning, the demand for\nefficient parallel processing of large matrices has grown significantly.This\nstudy explores the performance of both serial and parallel matrix\nmultiplication on the Cirrus supercomputer at the University of Edinburgh. The\nresults demonstrate the scalability and efficiency of these methods, providing\ninsights for optimizing matrixmultiplication in real-world applications.","PeriodicalId":501422,"journal":{"name":"arXiv - CS - Distributed, Parallel, and Cluster Computing","volume":"11 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analysis of the Performance of the Matrix Multiplication Algorithm on the Cirrus Supercomputer\",\"authors\":\"Temitayo Adefemi\",\"doi\":\"arxiv-2408.15384\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Matrix multiplication is integral to various scientific and engineering\\ndisciplines, including machine learning, image processing, and gaming. With the\\nincreasing data volumes in areas like machine learning, the demand for\\nefficient parallel processing of large matrices has grown significantly.This\\nstudy explores the performance of both serial and parallel matrix\\nmultiplication on the Cirrus supercomputer at the University of Edinburgh. The\\nresults demonstrate the scalability and efficiency of these methods, providing\\ninsights for optimizing matrixmultiplication in real-world applications.\",\"PeriodicalId\":501422,\"journal\":{\"name\":\"arXiv - CS - Distributed, Parallel, and Cluster Computing\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Distributed, Parallel, and Cluster Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.15384\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Distributed, Parallel, and Cluster Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.15384","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analysis of the Performance of the Matrix Multiplication Algorithm on the Cirrus Supercomputer
Matrix multiplication is integral to various scientific and engineering
disciplines, including machine learning, image processing, and gaming. With the
increasing data volumes in areas like machine learning, the demand for
efficient parallel processing of large matrices has grown significantly.This
study explores the performance of both serial and parallel matrix
multiplication on the Cirrus supercomputer at the University of Edinburgh. The
results demonstrate the scalability and efficiency of these methods, providing
insights for optimizing matrixmultiplication in real-world applications.