{"title":"Using Domain Decomposition to Solve Positive-Definite Systems on the Hypercube Computer","authors":"G.L. Hennigan, S. Castillo, E. Hensel","doi":"10.1109/DMCC.1991.633214","DOIUrl":"https://doi.org/10.1109/DMCC.1991.633214","url":null,"abstract":"A distributed method of solving sparse, positive-definite systems of equations on a hypercube computer, like those arising fiom many finite-element problems, is studied. A domain decomposition method is introduced wherein the domain of the problem to be solved is physically split into several sub-domains. This physical split is based on an ordering known as one-way dissection [ I ] . The one-way dissection ordering generates a block-diagonal system of equations which is well suited to a parallel implementation. Once the ordering has been accomplished each of the subdomains is then distributed to a processor in the hypercube computer as necessary. The method is applied to two-dimensional electrostatic problems which are governed by Laplace’s equation. Since the finite-element method is used to discretize the problem the method is developed to take full advantage of the inherent sparsity. The algorithm is applied to several geometries.","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115594556","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}
{"title":"Simulating Adaptive Load Sharing Policies on an iPSC/2 Multicomputer","authors":"Y. Hu, B. Gillett","doi":"10.1109/DMCC.1991.633135","DOIUrl":"https://doi.org/10.1109/DMCC.1991.633135","url":null,"abstract":"Unlike most other adaptive load sharing (U) policy studies, each node in the distributed system is modeled as a central server model represented by a closed queueing network (QN). The primary objective of this study is to use a simulation model to find the improvement for an adaptive LS policy in a distributed system. In homogeneous distributed systems, the simulation results in this study show that the performance improvements between no LS, LS with task placement, and LS with task migration are very small. These results are quite different from other studies, which show a significant improvement of mean response time by executing the LS policy. The performance improvement for the heterogeneous system i s very consistent. That is, the benefit is given to every congested node in the system.","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129855728","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}
{"title":"Choosing Processor Array Configuration by Performance Modeling for a Highly Parallel Linear Algebra Algorithm","authors":"R. Littlefield, K. Maschoff","doi":"10.1109/DMCC.1991.633343","DOIUrl":"https://doi.org/10.1109/DMCC.1991.633343","url":null,"abstract":"Many linear algebra algorithms utilize an array of processors across which matrices are distributed. Given a particular matrix size and a maximum number of processors, what configuration of processors, i.e., what size and shape array, will execute the fastest The answer to this question depends on tradeoffs between load balancing, communication startup and transfer costs, and computational overhead. In this paper we analyze in detail one algorithm: the blocked factored Jacobi method for solving dense eigensystems. A performance model is developed to predict execution time as a function of the processor array and matrix sizes, plus the basic computation and communication speeds of the underlying computer system. In experiments on a large hypercube (up to 512 processors), this model has been found to be highly accurate (mean error {approximately} 2%) over a wide range of matrix sizes (10 {times} 10 through 200 {times} 200) and processor counts (1 to 512). The model reveals, and direct experiment confirms, that the tradeoffs mentioned above can be surprisingly complex and counterintuitive. We propose decision procedures based directly on the performance model to choose configurations for fastest execution. The model-based decision procedures are compared to a heuristic strategy and shown to be significantly better.more » 7 refs., 8 figs., 1 tab.« less","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114889462","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}
{"title":"Hyperswitch Communication Network","authors":"J. Peterson, M. Pniel, E. Upchurch","doi":"10.1109/DMCC.1991.633350","DOIUrl":"https://doi.org/10.1109/DMCC.1991.633350","url":null,"abstract":"The Hyperswitch Communication Network (HCN) is a large scale parallel computer prototype being developed at JPL. Commercial versions of the HCN computer are planned. The HCN computer being designed is a message passing multiple instruction multiple data (MIMD) computer, and offers many advantages in price-performance ratio, reliability and availability, and manufacturing over traditional uniprocessors and bus based multiprocessors. The design of the HCN operating system is a uniquely flexible environment that combines both parallel processing and distributed processing. This programming paradigm can achieve a balance among the following competing factors: performance in processing and communications, user friendliness, and fault tolerance. The prototype is being designed to accommodate a maximum of 64 state of the art microprocessors. The HCN is classified as a distributed supercomputer. The HCN system is described, and the performance/cost analysis and other competing factors within the system design are reviewed.","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130279396","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}
{"title":"The PVM System: Supercomputer Level Concurrent Computation on a Heterogeneous Network of Workstations","authors":"G. Geist, V. Sunderam","doi":"10.1109/DMCC.1991.633139","DOIUrl":"https://doi.org/10.1109/DMCC.1991.633139","url":null,"abstract":"The PVM (Parallel Virtual Machine) system enables supercomputer level concurrent computations to be performed on interconnected networks of heterogeneous computer systems. Specifically, a network of 13 IBM RS/6000 powerstations has been used to run superconductor modeling codes at more than 250 Mflops. This paper describes the PVM system and two example applications running on it. 3 refs., 1 fig., 2 tabs.","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132147897","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}
{"title":"A Software Tool for Load Balanced Adaptive Multiple Grids on Distributed Memory Computers","authors":"J. De Keyser, D. Roose","doi":"10.1109/DMCC.1991.633102","DOIUrl":"https://doi.org/10.1109/DMCC.1991.633102","url":null,"abstract":"T h e solut ion of partial dif ferential equations on twodimensional domains can benefit f r o m the use of irregular grids. In t he case of multigrid solut ion techniques, a hierarchy of nested grids is used. W e imp lemen ted a n adaptive mult iple grid generat ion method and relaxat ion schemes on the iPSC/2 hypercube, based on a general software tool f o r t he man ipu la t ion of dataparallel applications characterized by a n arbitrary and varying topology. T h i s tool takes care of t he data exchanges needed t o ensure t h e consistency of t h e distributed data s tructure and controls t he load balanced execution of t he application by redistributing the data among t h e processors of t h e distributed m e m o r y parallel computer . Remapp ing is done af ter each grid ref inement . In a mult igrid context, a n appropriate choice of data s tructures allows t o coordinate t h e migrat ion of pieces of t h e grids on diflerent levels, so as t o l im i t inter-grid communicat ion.","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134523211","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}
{"title":"A Structured Representation for Parallel Algorithm Design on Multicomputers","authors":"Xian-He Sun, L.M. Ni","doi":"10.1109/DMCC.1991.633319","DOIUrl":"https://doi.org/10.1109/DMCC.1991.633319","url":null,"abstract":"Traditionally, parallel algorithms have been designed by brute force methods and fine-tuned on each architecture to achieve high performance. Rather than studying the design case by case, a systematic approach is proposed. A notation is first developed. Using this notation, most of the frequently used scientific and engineering applications can be presented by simple formulas. The formulas constitute the structured representation of the corresponding applications. The structured representation is simple, adequate and easy to understand. They also contain sufficient information about uneven allocation and communication latency degradations. With the structured representation, applications can be compared, classified and partitioned. Some of the basic building blocks, called computation models, of frequently used applications are identified and studied. Most applications are combinations of some computation models. The structured representation relates general applications to computation models. Studying computation models leads to a guideline for efficient parallel algorithm design for general applications. 6 refs., 7 figs.","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128690319","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}
{"title":"Searching for Consensus Patterns on a Hypercube","authors":"X. Guan, R. Mann, R. Mural, E. Uberbacher","doi":"10.1109/DMCC.1991.633217","DOIUrl":"https://doi.org/10.1109/DMCC.1991.633217","url":null,"abstract":"In DNA sequence analysis, consensus patterns (those that are not precisely conserved in location or with the same sequence of letters) are frequently sought among a number of sequences to find important biological features. Sequential algorithms for finding consensus patterns are time-consuming due to the nature of the inexact occurrences of the patterns. Here we show that consensus pattern search can be done efficiently on the hypercube. We describe our implementation of two algorithms to find consensus patterns on an Intel iPSC/860 Hypercube and various techniques to speed up the computation. 4 refs., 1 fig., 2 tabs.","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126458769","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}
{"title":"Cellular Automaton Models for Reaction Diffusion Equations","authors":"J. R. Weimar, L. Watson, J. Tyson","doi":"10.1109/DMCC.1991.633207","DOIUrl":"https://doi.org/10.1109/DMCC.1991.633207","url":null,"abstract":"T h i s paper introduces a n e w cellular a u t o m a t o n mode l for react ion d i f i s i o n equations with improved t r e a t m e n t s of (1) diffusio,n and wave propagation, and (2) slow d y n a m i c s of t h e recovery variable. T h e aut o m a t o n i s both computat ional ly e f l c i e n t ain a distributed m e m o r y mult iprocessor and fa i th fu l t o t h e underlying part ial dif ferential equations.","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129500622","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}
J. Saltz, R. Das, R. Ponnusamy, D. Mavriplis, H. Berryman, Janet Wu
{"title":"Parti Procedures for Realistic Loops","authors":"J. Saltz, R. Das, R. Ponnusamy, D. Mavriplis, H. Berryman, Janet Wu","doi":"10.1109/DMCC.1991.633089","DOIUrl":"https://doi.org/10.1109/DMCC.1991.633089","url":null,"abstract":"","PeriodicalId":313314,"journal":{"name":"The Sixth Distributed Memory Computing Conference, 1991. Proceedings","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126191813","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}