{"title":"用于消息传递体系结构的并行多边形呈现","authors":"T. Crockett, T. Orloff","doi":"10.1109/88.311569","DOIUrl":null,"url":null,"abstract":"Applications such as real-time animation and scientific visualization demand high performance for rendering complex 3D abstract data models into 2D images. As large applications migrate to highly parallel supercomputers, how can we exploit the available parallelism to keep the rendering on the supercomputer? To answer this question, we developed a parallel polygon renderer for general-purpose MIMD distributed-memory message-passing systems. It exploits object-level and image-level parallelism, and can run on systems containing from one processor to a number bounded by the number of scan lines in the resulting image. Unlike earlier approaches, ours multiplexes the transformation and rasterization phases on the same machine. This reduces memory usage and network contention, and overlaps computation and communication.<<ETX>>","PeriodicalId":325213,"journal":{"name":"IEEE Parallel & Distributed Technology: Systems & Applications","volume":"303 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":"{\"title\":\"Parallel polygon rendering for message-passing architectures\",\"authors\":\"T. Crockett, T. Orloff\",\"doi\":\"10.1109/88.311569\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Applications such as real-time animation and scientific visualization demand high performance for rendering complex 3D abstract data models into 2D images. As large applications migrate to highly parallel supercomputers, how can we exploit the available parallelism to keep the rendering on the supercomputer? To answer this question, we developed a parallel polygon renderer for general-purpose MIMD distributed-memory message-passing systems. It exploits object-level and image-level parallelism, and can run on systems containing from one processor to a number bounded by the number of scan lines in the resulting image. Unlike earlier approaches, ours multiplexes the transformation and rasterization phases on the same machine. This reduces memory usage and network contention, and overlaps computation and communication.<<ETX>>\",\"PeriodicalId\":325213,\"journal\":{\"name\":\"IEEE Parallel & Distributed Technology: Systems & Applications\",\"volume\":\"303 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"24\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Parallel & Distributed Technology: Systems & Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/88.311569\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Parallel & Distributed Technology: Systems & Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/88.311569","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Parallel polygon rendering for message-passing architectures
Applications such as real-time animation and scientific visualization demand high performance for rendering complex 3D abstract data models into 2D images. As large applications migrate to highly parallel supercomputers, how can we exploit the available parallelism to keep the rendering on the supercomputer? To answer this question, we developed a parallel polygon renderer for general-purpose MIMD distributed-memory message-passing systems. It exploits object-level and image-level parallelism, and can run on systems containing from one processor to a number bounded by the number of scan lines in the resulting image. Unlike earlier approaches, ours multiplexes the transformation and rasterization phases on the same machine. This reduces memory usage and network contention, and overlaps computation and communication.<>