J. Mamza, P. Makyla, A. Dziekonski, A. Lamecki, M. Mrozowski
{"title":"有限元法中数值积分的多核多处理器实现","authors":"J. Mamza, P. Makyla, A. Dziekonski, A. Lamecki, M. Mrozowski","doi":"10.1109/MIKON.2012.6233633","DOIUrl":null,"url":null,"abstract":"The paper presents techniques for accelerating a numerical integration process which appears in the Finite Element Method. The acceleration is achieved by taking advantages of multi-core and multiprocessor devices. It is shown that using multi-core implementation with OpenMP and a GPU acceleration using CUDA architecture allows one to achieve the speedups by a factor of 5 and 10 on a CPU and GPUs, respectively.","PeriodicalId":425104,"journal":{"name":"2012 19th International Conference on Microwaves, Radar & Wireless Communications","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Multi-core and multiprocessor implementation of numerical integration in Finite Element Method\",\"authors\":\"J. Mamza, P. Makyla, A. Dziekonski, A. Lamecki, M. Mrozowski\",\"doi\":\"10.1109/MIKON.2012.6233633\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents techniques for accelerating a numerical integration process which appears in the Finite Element Method. The acceleration is achieved by taking advantages of multi-core and multiprocessor devices. It is shown that using multi-core implementation with OpenMP and a GPU acceleration using CUDA architecture allows one to achieve the speedups by a factor of 5 and 10 on a CPU and GPUs, respectively.\",\"PeriodicalId\":425104,\"journal\":{\"name\":\"2012 19th International Conference on Microwaves, Radar & Wireless Communications\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 19th International Conference on Microwaves, Radar & Wireless Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MIKON.2012.6233633\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 19th International Conference on Microwaves, Radar & Wireless Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MIKON.2012.6233633","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-core and multiprocessor implementation of numerical integration in Finite Element Method
The paper presents techniques for accelerating a numerical integration process which appears in the Finite Element Method. The acceleration is achieved by taking advantages of multi-core and multiprocessor devices. It is shown that using multi-core implementation with OpenMP and a GPU acceleration using CUDA architecture allows one to achieve the speedups by a factor of 5 and 10 on a CPU and GPUs, respectively.