{"title":"优化矩阵乘法的缓存友好策略","authors":"M. Ananth, S. Vishwas, M. R. Anala","doi":"10.1109/IACC.2017.0020","DOIUrl":null,"url":null,"abstract":"Matrix multiplication is an operation used in many algorithms with a plethora of applications ranging from Image Processing, Signal Processing, to Artificial Neural Networks and Linear algebra. This work aims to showcase the effect of developing matrix multiplication strategies that are less time and processor intensive by effectively handling memory accesses. The paper also touches upon on the advantages of using OpenMP, a multiprocessing toolkit to show the effect of parallelizing matrix multiplication.","PeriodicalId":248433,"journal":{"name":"2017 IEEE 7th International Advance Computing Conference (IACC)","volume":"588 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Cache Friendly Strategies to Optimize Matrix Multiplication\",\"authors\":\"M. Ananth, S. Vishwas, M. R. Anala\",\"doi\":\"10.1109/IACC.2017.0020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Matrix multiplication is an operation used in many algorithms with a plethora of applications ranging from Image Processing, Signal Processing, to Artificial Neural Networks and Linear algebra. This work aims to showcase the effect of developing matrix multiplication strategies that are less time and processor intensive by effectively handling memory accesses. The paper also touches upon on the advantages of using OpenMP, a multiprocessing toolkit to show the effect of parallelizing matrix multiplication.\",\"PeriodicalId\":248433,\"journal\":{\"name\":\"2017 IEEE 7th International Advance Computing Conference (IACC)\",\"volume\":\"588 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 7th International Advance Computing Conference (IACC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IACC.2017.0020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 7th International Advance Computing Conference (IACC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IACC.2017.0020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Cache Friendly Strategies to Optimize Matrix Multiplication
Matrix multiplication is an operation used in many algorithms with a plethora of applications ranging from Image Processing, Signal Processing, to Artificial Neural Networks and Linear algebra. This work aims to showcase the effect of developing matrix multiplication strategies that are less time and processor intensive by effectively handling memory accesses. The paper also touches upon on the advantages of using OpenMP, a multiprocessing toolkit to show the effect of parallelizing matrix multiplication.
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