{"title":"使用多维转换的数据并行可视化","authors":"G. Vezina, P. K. Robertson","doi":"10.1109/FMPC.1992.234954","DOIUrl":null,"url":null,"abstract":"The authors show how a flexible resampling approach can be embedded within massively parallel implementations of multidimensional transformation algorithms based on one-dimensional resampling operations. They provide a consistent solution to the resampling requirements across visualization applications. Based on this framework, two applications are outlined: a surface perspective viewing algorithm with hidden-surface removal and a volume rendering algorithm. These algorithms include regular and irregular resampling requirements. The algorithm considered here is well-suited to data-parallel SIMD (single-instruction multiple-data) processing, and performance on surface and volume visualization is sufficient to achieve interactive manipulation on large SIMD arrays.<<ETX>>","PeriodicalId":117789,"journal":{"name":"[Proceedings 1992] The Fourth Symposium on the Frontiers of Massively Parallel Computation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1992-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Data-parallel visualisation using multi-dimensional transformations\",\"authors\":\"G. Vezina, P. K. Robertson\",\"doi\":\"10.1109/FMPC.1992.234954\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors show how a flexible resampling approach can be embedded within massively parallel implementations of multidimensional transformation algorithms based on one-dimensional resampling operations. They provide a consistent solution to the resampling requirements across visualization applications. Based on this framework, two applications are outlined: a surface perspective viewing algorithm with hidden-surface removal and a volume rendering algorithm. These algorithms include regular and irregular resampling requirements. The algorithm considered here is well-suited to data-parallel SIMD (single-instruction multiple-data) processing, and performance on surface and volume visualization is sufficient to achieve interactive manipulation on large SIMD arrays.<<ETX>>\",\"PeriodicalId\":117789,\"journal\":{\"name\":\"[Proceedings 1992] The Fourth Symposium on the Frontiers of Massively Parallel Computation\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1992-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[Proceedings 1992] The Fourth Symposium on the Frontiers of Massively Parallel Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FMPC.1992.234954\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings 1992] The Fourth Symposium on the Frontiers of Massively Parallel Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FMPC.1992.234954","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Data-parallel visualisation using multi-dimensional transformations
The authors show how a flexible resampling approach can be embedded within massively parallel implementations of multidimensional transformation algorithms based on one-dimensional resampling operations. They provide a consistent solution to the resampling requirements across visualization applications. Based on this framework, two applications are outlined: a surface perspective viewing algorithm with hidden-surface removal and a volume rendering algorithm. These algorithms include regular and irregular resampling requirements. The algorithm considered here is well-suited to data-parallel SIMD (single-instruction multiple-data) processing, and performance on surface and volume visualization is sufficient to achieve interactive manipulation on large SIMD arrays.<>