{"title":"消息传递与工作站网络上的分布式共享内存","authors":"Honghui Lu, S. Dwarkadas, A. Cox, W. Zwaenepoel","doi":"10.1145/224170.224285","DOIUrl":null,"url":null,"abstract":"The message passing programs are executed with the Parallel Virtual Machine (PVM) library and the shared memory programs are executed using TreadMarks. The programs are Water and Barnes-Hut from the SPLASH benchmark suite; 3-D FFT, Integer Sort (IS) and Embarrassingly Parallel (EP) from the NAS benchmarks; ILINK, a widely used genetic linkage analysis program; and Successive Over-Relaxation (SOR), Traveling Salesman (TSP), and Quicksort (QSORT). Two different input data sets were used for Water (Water-288 and Water-1728), IS (IS-Small and IS-Large), and SOR (SOR-Zero and SOR-NonZero). Our execution environment is a set of eight HP735 workstations connected by a 100Mbits per second FDDI network. For Water-1728, EP, ILINK, SOR-Zero, and SOR-NonZero, the performance of TreadMarks is within 10%of PVM. For IS-Small, Water-288, Barnes-Hut, 3-D FFT, TSP, and QSORT, differences are on the order of 10%to 30%. Finally, for IS-Large, PVM performs two times better than TreadMarks. More messages and more data are sent in TreadMarks, explaining the performance differences. This extra communication is caused by 1) the separation of synchronization and data transfer, 2) extra messages to request updates for data by the invalidate protocol used in TreadMarks, 3) false sharing, and 4) diff accumulation for migratory data in TreadMarks.","PeriodicalId":269909,"journal":{"name":"Proceedings of the IEEE/ACM SC95 Conference","volume":"105 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"112","resultStr":"{\"title\":\"Message Passing Versus Distributed Shared Memory on Networks of Workstations\",\"authors\":\"Honghui Lu, S. Dwarkadas, A. Cox, W. Zwaenepoel\",\"doi\":\"10.1145/224170.224285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The message passing programs are executed with the Parallel Virtual Machine (PVM) library and the shared memory programs are executed using TreadMarks. The programs are Water and Barnes-Hut from the SPLASH benchmark suite; 3-D FFT, Integer Sort (IS) and Embarrassingly Parallel (EP) from the NAS benchmarks; ILINK, a widely used genetic linkage analysis program; and Successive Over-Relaxation (SOR), Traveling Salesman (TSP), and Quicksort (QSORT). Two different input data sets were used for Water (Water-288 and Water-1728), IS (IS-Small and IS-Large), and SOR (SOR-Zero and SOR-NonZero). Our execution environment is a set of eight HP735 workstations connected by a 100Mbits per second FDDI network. For Water-1728, EP, ILINK, SOR-Zero, and SOR-NonZero, the performance of TreadMarks is within 10%of PVM. For IS-Small, Water-288, Barnes-Hut, 3-D FFT, TSP, and QSORT, differences are on the order of 10%to 30%. Finally, for IS-Large, PVM performs two times better than TreadMarks. More messages and more data are sent in TreadMarks, explaining the performance differences. This extra communication is caused by 1) the separation of synchronization and data transfer, 2) extra messages to request updates for data by the invalidate protocol used in TreadMarks, 3) false sharing, and 4) diff accumulation for migratory data in TreadMarks.\",\"PeriodicalId\":269909,\"journal\":{\"name\":\"Proceedings of the IEEE/ACM SC95 Conference\",\"volume\":\"105 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"112\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the IEEE/ACM SC95 Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/224170.224285\",\"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 of the IEEE/ACM SC95 Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/224170.224285","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Message Passing Versus Distributed Shared Memory on Networks of Workstations
The message passing programs are executed with the Parallel Virtual Machine (PVM) library and the shared memory programs are executed using TreadMarks. The programs are Water and Barnes-Hut from the SPLASH benchmark suite; 3-D FFT, Integer Sort (IS) and Embarrassingly Parallel (EP) from the NAS benchmarks; ILINK, a widely used genetic linkage analysis program; and Successive Over-Relaxation (SOR), Traveling Salesman (TSP), and Quicksort (QSORT). Two different input data sets were used for Water (Water-288 and Water-1728), IS (IS-Small and IS-Large), and SOR (SOR-Zero and SOR-NonZero). Our execution environment is a set of eight HP735 workstations connected by a 100Mbits per second FDDI network. For Water-1728, EP, ILINK, SOR-Zero, and SOR-NonZero, the performance of TreadMarks is within 10%of PVM. For IS-Small, Water-288, Barnes-Hut, 3-D FFT, TSP, and QSORT, differences are on the order of 10%to 30%. Finally, for IS-Large, PVM performs two times better than TreadMarks. More messages and more data are sent in TreadMarks, explaining the performance differences. This extra communication is caused by 1) the separation of synchronization and data transfer, 2) extra messages to request updates for data by the invalidate protocol used in TreadMarks, 3) false sharing, and 4) diff accumulation for migratory data in TreadMarks.