Pulung Waskito, Shinobu Miwa, Y. Mitsukura, H. Nakajo
{"title":"在GPU上并行化Hilbert-Huang变换","authors":"Pulung Waskito, Shinobu Miwa, Y. Mitsukura, H. Nakajo","doi":"10.1109/IC-NC.2010.44","DOIUrl":null,"url":null,"abstract":"In this paper, we show parallel implementation of Hilbert-Huang Transform on GPU. This implementation focused on the reducing the computation complexity from O(N) on a single CPU to O(N/P log (N)) on GPU, as well as the use of 'shared-global' switching method to increase performance. Evaluation results show our single GPU implementation using Tesla C1060 achieves 29.0x speedup in best case, and a total of 7.1x speedup for all results when compared to a single Intel dual core CPU.","PeriodicalId":375145,"journal":{"name":"2010 First International Conference on Networking and Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Parallelizing Hilbert-Huang Transform on a GPU\",\"authors\":\"Pulung Waskito, Shinobu Miwa, Y. Mitsukura, H. Nakajo\",\"doi\":\"10.1109/IC-NC.2010.44\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we show parallel implementation of Hilbert-Huang Transform on GPU. This implementation focused on the reducing the computation complexity from O(N) on a single CPU to O(N/P log (N)) on GPU, as well as the use of 'shared-global' switching method to increase performance. Evaluation results show our single GPU implementation using Tesla C1060 achieves 29.0x speedup in best case, and a total of 7.1x speedup for all results when compared to a single Intel dual core CPU.\",\"PeriodicalId\":375145,\"journal\":{\"name\":\"2010 First International Conference on Networking and Computing\",\"volume\":\"32 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 First International Conference on Networking and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IC-NC.2010.44\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 First International Conference on Networking and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC-NC.2010.44","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we show parallel implementation of Hilbert-Huang Transform on GPU. This implementation focused on the reducing the computation complexity from O(N) on a single CPU to O(N/P log (N)) on GPU, as well as the use of 'shared-global' switching method to increase performance. Evaluation results show our single GPU implementation using Tesla C1060 achieves 29.0x speedup in best case, and a total of 7.1x speedup for all results when compared to a single Intel dual core CPU.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
