{"title":"为什么SBVH在并行平台上优于KD-tree ?","authors":"A. Breglia, A. Capozzoli, C. Curcio, A. Liseno","doi":"10.1109/ROPACES.2016.7465401","DOIUrl":null,"url":null,"abstract":"We spot on the performance of two acceleration data structures for electromagnetic ray tracing purposes on GPU using the CUDA programming language, namely the KD-tree and the SBVH. Our implementations have been based on the approach made available by NVIDIA which takes into account for the programming optimizations made possible by the latest version of CUDA and for the latest NVIDIA GPU architectures.","PeriodicalId":101990,"journal":{"name":"2016 IEEE/ACES International Conference on Wireless Information Technology and Systems (ICWITS) and Applied Computational Electromagnetics (ACES)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Why does SBVH outperform KD-tree on parallel platforms?\",\"authors\":\"A. Breglia, A. Capozzoli, C. Curcio, A. Liseno\",\"doi\":\"10.1109/ROPACES.2016.7465401\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We spot on the performance of two acceleration data structures for electromagnetic ray tracing purposes on GPU using the CUDA programming language, namely the KD-tree and the SBVH. Our implementations have been based on the approach made available by NVIDIA which takes into account for the programming optimizations made possible by the latest version of CUDA and for the latest NVIDIA GPU architectures.\",\"PeriodicalId\":101990,\"journal\":{\"name\":\"2016 IEEE/ACES International Conference on Wireless Information Technology and Systems (ICWITS) and Applied Computational Electromagnetics (ACES)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE/ACES International Conference on Wireless Information Technology and Systems (ICWITS) and Applied Computational Electromagnetics (ACES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROPACES.2016.7465401\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE/ACES International Conference on Wireless Information Technology and Systems (ICWITS) and Applied Computational Electromagnetics (ACES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROPACES.2016.7465401","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Why does SBVH outperform KD-tree on parallel platforms?
We spot on the performance of two acceleration data structures for electromagnetic ray tracing purposes on GPU using the CUDA programming language, namely the KD-tree and the SBVH. Our implementations have been based on the approach made available by NVIDIA which takes into account for the programming optimizations made possible by the latest version of CUDA and for the latest NVIDIA GPU architectures.
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