Min Shih, S. Rizzi, J. Insley, T. Uram, V. Vishwanath, M. Hereld, M. Papka, K. Ma
{"title":"并行分布式,gpu加速,先进的照明计算大规模体积可视化","authors":"Min Shih, S. Rizzi, J. Insley, T. Uram, V. Vishwanath, M. Hereld, M. Papka, K. Ma","doi":"10.1109/LDAV.2016.7874309","DOIUrl":null,"url":null,"abstract":"The benefits of applying advanced illumination models to volume visualization have been demonstrated by many researchers. For a parallel distributed, GPU computing environment, however, there is no efficient algorithm for scalable global illumination calculations. This paper presents a parallel, data-distributed and GPU-accelerated algorithm for volume rendering with advanced lighting. Our approach features tunable soft shadows for enhancing perception of complex spatial structures and relationships. For lighting calculations, our design effectively avoids data exchange among GPUs. Performance evaluation on a GPU cluster using up to 128 GPUs shows scalable rendering performance, with both the number of GPUs and volume data size.","PeriodicalId":148570,"journal":{"name":"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Parallel distributed, GPU-accelerated, advanced lighting calculations for large-scale volume visualization\",\"authors\":\"Min Shih, S. Rizzi, J. Insley, T. Uram, V. Vishwanath, M. Hereld, M. Papka, K. Ma\",\"doi\":\"10.1109/LDAV.2016.7874309\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The benefits of applying advanced illumination models to volume visualization have been demonstrated by many researchers. For a parallel distributed, GPU computing environment, however, there is no efficient algorithm for scalable global illumination calculations. This paper presents a parallel, data-distributed and GPU-accelerated algorithm for volume rendering with advanced lighting. Our approach features tunable soft shadows for enhancing perception of complex spatial structures and relationships. For lighting calculations, our design effectively avoids data exchange among GPUs. Performance evaluation on a GPU cluster using up to 128 GPUs shows scalable rendering performance, with both the number of GPUs and volume data size.\",\"PeriodicalId\":148570,\"journal\":{\"name\":\"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/LDAV.2016.7874309\",\"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 6th Symposium on Large Data Analysis and Visualization (LDAV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LDAV.2016.7874309","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallel distributed, GPU-accelerated, advanced lighting calculations for large-scale volume visualization
The benefits of applying advanced illumination models to volume visualization have been demonstrated by many researchers. For a parallel distributed, GPU computing environment, however, there is no efficient algorithm for scalable global illumination calculations. This paper presents a parallel, data-distributed and GPU-accelerated algorithm for volume rendering with advanced lighting. Our approach features tunable soft shadows for enhancing perception of complex spatial structures and relationships. For lighting calculations, our design effectively avoids data exchange among GPUs. Performance evaluation on a GPU cluster using up to 128 GPUs shows scalable rendering performance, with both the number of GPUs and volume data size.