Most data-parallel rendering algorithms (on multiprocessot systems with distributed memory) spend a substantial amount of time composing (merging or assembling) the partial images of all the processors. This paper shows how "intelligent communication controllers" (ICCs) help to reduce the immense communication overhead and accumulated latencies to an absolute minimum. Three examples of "intelligent" communication schemes are presented: the fully automatic redistribution of multi-dimensional data sets, depthmerge and bucket-sort. We show that these (and other) "intelligent communication schemes" can be implemented in hardware with a reasonable effort and that the communication bandwidth is used most efficiently. This results in a good speed-up, good scalability and the maximum utilizable performance for parallel rendering and many other data-parallel algorithms running on our multiprocessor system "MUSIC". As an example, we present a simple objectparallel renderer running at interactive frame rates.
{"title":"Interactive parallel rendering on a multiprocessor system with intelligent communication controllers","authors":"B. Bäumle, P. Kohler, A. Gunzinger","doi":"10.1145/218327.218342","DOIUrl":"https://doi.org/10.1145/218327.218342","url":null,"abstract":"Most data-parallel rendering algorithms (on multiprocessot systems with distributed memory) spend a substantial amount of time composing (merging or assembling) the partial images of all the processors. This paper shows how \"intelligent communication controllers\" (ICCs) help to reduce the immense communication overhead and accumulated latencies to an absolute minimum. Three examples of \"intelligent\" communication schemes are presented: the fully automatic redistribution of multi-dimensional data sets, depthmerge and bucket-sort. We show that these (and other) \"intelligent communication schemes\" can be implemented in hardware with a reasonable effort and that the communication bandwidth is used most efficiently. This results in a good speed-up, good scalability and the maximum utilizable performance for parallel rendering and many other data-parallel algorithms running on our multiprocessor system \"MUSIC\". As an example, we present a simple objectparallel renderer running at interactive frame rates.","PeriodicalId":101947,"journal":{"name":"Proceedings of the IEEE symposium on Parallel rendering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128991118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract : As computing technology continues to advance, computational modeling of scientific and engineering problems produces data of increasing complexity: large in size and unstructured in shape. Volume visualization of such data is a challenging problem. This paper proposes a distributed parallel solution that makes ray-casting volume rendering of unstructured-grid data practical. Both the data and the rendering process are distributed among processors. At each processor, ray-casting of local data is performed independent of the other processors. The global image compositing processes, which require inter-processor communication, are overlapped with the local ray-casting processes to achieve maximum parallel efficiency. This algorithm differs from previous ones in four ways: it is completely distributed, less view-dependent, reasonably scalable, and flexible. Without using dynamic load balancing, test results on the Intel Paragon using from two to 128 processors show, on average, about 60% parallel efficiency.
{"title":"Parallel volume ray-casting for unstructured-grid data on distributed-memory architectures","authors":"K. Ma","doi":"10.1145/218327.218333","DOIUrl":"https://doi.org/10.1145/218327.218333","url":null,"abstract":"Abstract : As computing technology continues to advance, computational modeling of scientific and engineering problems produces data of increasing complexity: large in size and unstructured in shape. Volume visualization of such data is a challenging problem. This paper proposes a distributed parallel solution that makes ray-casting volume rendering of unstructured-grid data practical. Both the data and the rendering process are distributed among processors. At each processor, ray-casting of local data is performed independent of the other processors. The global image compositing processes, which require inter-processor communication, are overlapped with the local ray-casting processes to achieve maximum parallel efficiency. This algorithm differs from previous ones in four ways: it is completely distributed, less view-dependent, reasonably scalable, and flexible. Without using dynamic load balancing, test results on the Intel Paragon using from two to 128 processors show, on average, about 60% parallel efficiency.","PeriodicalId":101947,"journal":{"name":"Proceedings of the IEEE symposium on Parallel rendering","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116536944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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