{"title":"平行分治光线追踪","authors":"S. Ravichandran, P J Narayanan","doi":"10.1145/2542355.2542393","DOIUrl":null,"url":null,"abstract":"Divide and Conquer Ray Tracing (DACRT) is a recent technique which constructs no explicit acceleration structure. It creates and traverses an implicit hierarchy in a depth-first fashion recursively and is suited for dynamic scenes that change constantly. In this paper, we present a parallel version of DACRT that runs entirely on the GPU, which exploits efficient primitives like sort and reduce. Our approach suits the GPU well, with a low memory footprint. Our implementation outperforms the serial CPU algorithm for both primary and secondary ray passes. We show good performance on primary pass and on advanced effects.","PeriodicalId":232593,"journal":{"name":"SIGGRAPH Asia 2013 Technical Briefs","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Parallel divide and conquer ray tracing\",\"authors\":\"S. Ravichandran, P J Narayanan\",\"doi\":\"10.1145/2542355.2542393\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Divide and Conquer Ray Tracing (DACRT) is a recent technique which constructs no explicit acceleration structure. It creates and traverses an implicit hierarchy in a depth-first fashion recursively and is suited for dynamic scenes that change constantly. In this paper, we present a parallel version of DACRT that runs entirely on the GPU, which exploits efficient primitives like sort and reduce. Our approach suits the GPU well, with a low memory footprint. Our implementation outperforms the serial CPU algorithm for both primary and secondary ray passes. We show good performance on primary pass and on advanced effects.\",\"PeriodicalId\":232593,\"journal\":{\"name\":\"SIGGRAPH Asia 2013 Technical Briefs\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-11-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SIGGRAPH Asia 2013 Technical Briefs\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2542355.2542393\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIGGRAPH Asia 2013 Technical Briefs","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2542355.2542393","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Divide and Conquer Ray Tracing (DACRT) is a recent technique which constructs no explicit acceleration structure. It creates and traverses an implicit hierarchy in a depth-first fashion recursively and is suited for dynamic scenes that change constantly. In this paper, we present a parallel version of DACRT that runs entirely on the GPU, which exploits efficient primitives like sort and reduce. Our approach suits the GPU well, with a low memory footprint. Our implementation outperforms the serial CPU algorithm for both primary and secondary ray passes. We show good performance on primary pass and on advanced effects.