Parallel Algorithms Column 1 : Models of Computatio n Michael T . Goodrich * Department of Computer Scienc e Johns Hopkins Universit y Baltimore, MD 2121 8 goodrich@cs .jhu .edu 1 Introductio n If you are the type that always orders vanilla in the ice cream shop, then you should probably sta y away from parallel algorithm design, for you may never get past the fact that any given paralle l algorithm may be designed for any one of several different computational models . If, on the other hand, you are the type who thinks there are still not enough channels on cable TV, then you migh t think the number of parallel models is woefully small . If you fall into neither camp, which, accordin g to the Chernoff bounds [22], you probably should, then you may find this first SIGACT News column on parallel algorithm design of some use . For here I review a "classic" model for parallel computatio n and I survey some interesting work on alternative models for parallel computation . 2 A Classic Model for Parallel Computation : The PRA M The shared-memory model, or parallel random access machine (PRAM), is classic
End of preview. The entire article is 6 pages. To view the full-text, please rent this article to continue.
/lp/association-for-computing-machinery/parallel-algorithms-column-1-models-of-computation-YZcF6ETUHX
Welcome to DeepDyve! Rent Premier Research Articles and Save Up to 90%
Parallel algorithms column 1: models of computation
Goodrich, Michael T.
Follow ACM SIGACT News
, Volume 24 (4)
Association for Computing Machinery – Dec 1, 1993
More Info
More Like This Article
View All dataSource[]=actageo&dataSource[]=aspet&dataSource[]=aaos&dataSource[]=aacc&dataSource[]=aacr&dataSource[]=aea&dataSource[]=aip&dataSource[]=ajnr&dataSource[]=ams&dataSource[]=aps_physical&dataSource[]=appi_book&dataSource[]=appi_journal&dataSource[]=apha&dataSource[]=asip&dataSource[]=asm&dataSource[]=asn&dataSource[]=aspb&dataSource[]=avs&dataSource[]=annual_reviews&dataSource[]=arxiv&dataSource[]=acm&dataSource[]=berghahn&dataSource[]=cabi&dataSource[]=clinical_trials&dataSource[]=dailymed&dataSource[]=degruyter&dataSource[]=du_press&dataSource[]=esa&dataSource[]=eu_press&dataSource[]=elsevier&dataSource[]=emerald&dataSource[]=ejtr&dataSource[]=emea&dataSource[]=epo&dataSource[]=faseb&dataSource[]=gsa&dataSource[]=health_affairs&dataSource[]=hindawi&dataSource[]=imanager&dataSource[]=imedpub&dataSource[]=informa_healthcare&dataSource[]=informs&dataSource[]=iop&dataSource[]=iucr&dataSource[]=iospress&dataSource[]=jbjs&dataSource[]=leftcoast&dataSource[]=lu_press&dataSource[]=mesharpe&dataSource[]=mary_ann_liebert&dataSource[]=medline&dataSource[]=mit_press&dataSource[]=nature&dataSource[]=oxford&dataSource[]=pier_professional&dataSource[]=pnas&dataSource[]=portlandpress&dataSource[]=psyc_articles&dataSource[]=psyc_books&dataSource[]=psyc_critiques&dataSource[]=plos_journal&dataSource[]=pubmed_central&dataSource[]=rsna&dataSource[]=rockefeller&dataSource[]=rcn&dataSource[]=ria&dataSource[]=rsc&dataSource[]=sage&dataSource[]=spie&dataSource[]=springer_journal&dataSource[]=springer&dataSource[]=taylor_francis&dataSource[]=aps&dataSource[]=the_scientist&dataSource[]=uc_press&dataSource[]=uspto_abstract&dataSource[]=wiley&dataSource[]=pct
© 2012 DeepDyve, Inc. All rights reserved.
Terms of Service | Privacy Policy
