Shucheng Huang, Li Xu, Bingqi Liu, Zhonghai Yang, Bin Li
{"title":"基于不连续伽辽金方法的统一GPU并行框架","authors":"Shucheng Huang, Li Xu, Bingqi Liu, Zhonghai Yang, Bin Li","doi":"10.1109/APS/URSI47566.2021.9703771","DOIUrl":null,"url":null,"abstract":"In this paper, we designed a unified parallel DG framework. Based on the characteristics of DG discretization, we built a preprocessing part in the framework. Then designed different kernel functions to match different numerical problems, such as Maxwell's equation and Euler's equation. A speed-up of 160 has been achieved over an equivalent CPU code. Compared with CPU code, the computing efficiency has been greatly improved.","PeriodicalId":6801,"journal":{"name":"2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI)","volume":"27 1","pages":"1891-1892"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unified GPU Parallel Framework Based on Discontinuous Galerkin Method\",\"authors\":\"Shucheng Huang, Li Xu, Bingqi Liu, Zhonghai Yang, Bin Li\",\"doi\":\"10.1109/APS/URSI47566.2021.9703771\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we designed a unified parallel DG framework. Based on the characteristics of DG discretization, we built a preprocessing part in the framework. Then designed different kernel functions to match different numerical problems, such as Maxwell's equation and Euler's equation. A speed-up of 160 has been achieved over an equivalent CPU code. Compared with CPU code, the computing efficiency has been greatly improved.\",\"PeriodicalId\":6801,\"journal\":{\"name\":\"2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI)\",\"volume\":\"27 1\",\"pages\":\"1891-1892\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APS/URSI47566.2021.9703771\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APS/URSI47566.2021.9703771","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Unified GPU Parallel Framework Based on Discontinuous Galerkin Method
In this paper, we designed a unified parallel DG framework. Based on the characteristics of DG discretization, we built a preprocessing part in the framework. Then designed different kernel functions to match different numerical problems, such as Maxwell's equation and Euler's equation. A speed-up of 160 has been achieved over an equivalent CPU code. Compared with CPU code, the computing efficiency has been greatly improved.