{"title":"基于GPU并行计算的FFT算法优化","authors":"Zhicheng Zhao, Yaqun Zhao","doi":"10.1109/IAEAC.2018.8577843","DOIUrl":null,"url":null,"abstract":"FFTW and CUFFT are used as typical FFT computing libraries based on CPU and GPU respectively. This paper tests and analyzes the performance and total consumption time of machine floating-point operation accelerated by CPU and GPU algorithm under the same data volume. The results show that CUFFT based on GPU has a better comprehensive performance than FFTW.","PeriodicalId":6573,"journal":{"name":"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","volume":"145 1","pages":"2003-2007"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The Optimization of FFT Algorithm Based with Parallel Computing on GPU\",\"authors\":\"Zhicheng Zhao, Yaqun Zhao\",\"doi\":\"10.1109/IAEAC.2018.8577843\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"FFTW and CUFFT are used as typical FFT computing libraries based on CPU and GPU respectively. This paper tests and analyzes the performance and total consumption time of machine floating-point operation accelerated by CPU and GPU algorithm under the same data volume. The results show that CUFFT based on GPU has a better comprehensive performance than FFTW.\",\"PeriodicalId\":6573,\"journal\":{\"name\":\"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"volume\":\"145 1\",\"pages\":\"2003-2007\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IAEAC.2018.8577843\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IAEAC.2018.8577843","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Optimization of FFT Algorithm Based with Parallel Computing on GPU
FFTW and CUFFT are used as typical FFT computing libraries based on CPU and GPU respectively. This paper tests and analyzes the performance and total consumption time of machine floating-point operation accelerated by CPU and GPU algorithm under the same data volume. The results show that CUFFT based on GPU has a better comprehensive performance than FFTW.
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