{"title":"Simulation of neural networks on a massively parallel computer (DAP-510) using sparse matrix techniques","authors":"S.N. Gupta, M. Zubair, C. Grosch","doi":"10.1109/FMPC.1990.89486","DOIUrl":null,"url":null,"abstract":"A parallel sparse matrix algorithm is proposed for the simulation of the modified Hopfield-Tank (MHT) network for solving the Traveling Salesman Problem (TSP). The MHT network using this sparse matrix algorithm has been implemented on a DAP-510, a massively parallel SIMD (single-instruction-steam, multiple-data-stream) computer consisting of 1024 processors. Problems of various sizes, ranging from eight cities up to 256 cities, have been simulated. The results show a very large speedup for the algorithm as compared with one using a standard dense matrix implementation.<<ETX>>","PeriodicalId":193332,"journal":{"name":"[1990 Proceedings] The Third Symposium on the Frontiers of Massively Parallel Computation","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1990-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1990 Proceedings] The Third Symposium on the Frontiers of Massively Parallel Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FMPC.1990.89486","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
A parallel sparse matrix algorithm is proposed for the simulation of the modified Hopfield-Tank (MHT) network for solving the Traveling Salesman Problem (TSP). The MHT network using this sparse matrix algorithm has been implemented on a DAP-510, a massively parallel SIMD (single-instruction-steam, multiple-data-stream) computer consisting of 1024 processors. Problems of various sizes, ranging from eight cities up to 256 cities, have been simulated. The results show a very large speedup for the algorithm as compared with one using a standard dense matrix implementation.<>
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