PARALLEL COMPUTING ON ANY DESKTOP.
This article discusses the applications of parallel computing beyond the scientific and governmental research community. Developers using the OpenMP parallel programming model have created multithreaded applications in Beowulf clusters, a computer network technology harnessing hundreds of workstatio...
| Published in: | Communications of the ACM Vol. 50; no. 9; pp. 75 - 79 |
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| Format: | Article |
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Association for Computing Machinery
Sep2007
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=26899796&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 26899796 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Sep2007 vid: 50 iid: 9 pid: 68 pub: Association for Computing Machinery artinfo: ui: 26899796 10.1145/1284621.1284622 ppf: 75 ppct: 4 formats: tig: atl: PARALLEL COMPUTING ON ANY DESKTOP. aug: au: Marowka, Ami affil: Assistant Professor, Department of Software Engineering of Shenkar College of Engineering and Design, Ramat-Gan, Israel. su: Parallel programming Beowulf clusters (Computer systems) Parallel processing Computer networks Simultaneous multithreading processors Supercomputers High performance computing Connection machines sug: subj: Parallel programming Beowulf clusters (Computer systems) Parallel processing Computer networks Simultaneous multithreading processors Supercomputers High performance computing Connection machines ab: This article discusses the applications of parallel computing beyond the scientific and governmental research community. Developers using the OpenMP parallel programming model have created multithreaded applications in Beowulf clusters, a computer network technology harnessing hundreds of workstations banded together. Inexpensive and flexible in scale and administration, 72% of the top 500 supercomputers in 2006 were clusters. The author discusses how parallel computing differs from clusters and how it can now be adopted through the multicore processor for desktop computers and the OpenMP parallel programming model into Microsoft Visual C ++ 2005. A major technology shift is predicted. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2007 holdings: @attributes: islocal: N |
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