{"title":"基于CUDA架构GPU的数值并行处理","authors":"Chengming Zou, Chunfen Xia, Guanghui Zhao","doi":"10.1109/WNIS.2009.46","DOIUrl":null,"url":null,"abstract":"The characteristics of modern graphics processing unit (GPU) is programmable, high price / performance ratio and high speed . It has a strong ability to adapt the parallel calculation, Based on this, the article study the general method of GPU calculating and use compute unified device architecture (CUDA) to design new parallel algorithm to accelerate the matrix inversion and Binarization algorithm. The results show that with the increase of matrix dimension, GPU performs much better than CPU in increase multiple.","PeriodicalId":280001,"journal":{"name":"2009 International Conference on Wireless Networks and Information Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Numerical Parallel Processing Based on GPU with CUDA Architecture\",\"authors\":\"Chengming Zou, Chunfen Xia, Guanghui Zhao\",\"doi\":\"10.1109/WNIS.2009.46\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The characteristics of modern graphics processing unit (GPU) is programmable, high price / performance ratio and high speed . It has a strong ability to adapt the parallel calculation, Based on this, the article study the general method of GPU calculating and use compute unified device architecture (CUDA) to design new parallel algorithm to accelerate the matrix inversion and Binarization algorithm. The results show that with the increase of matrix dimension, GPU performs much better than CPU in increase multiple.\",\"PeriodicalId\":280001,\"journal\":{\"name\":\"2009 International Conference on Wireless Networks and Information Systems\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Wireless Networks and Information Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WNIS.2009.46\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Wireless Networks and Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WNIS.2009.46","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Numerical Parallel Processing Based on GPU with CUDA Architecture
The characteristics of modern graphics processing unit (GPU) is programmable, high price / performance ratio and high speed . It has a strong ability to adapt the parallel calculation, Based on this, the article study the general method of GPU calculating and use compute unified device architecture (CUDA) to design new parallel algorithm to accelerate the matrix inversion and Binarization algorithm. The results show that with the increase of matrix dimension, GPU performs much better than CPU in increase multiple.
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