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Quasi-opportunistic supercomputing is a computational paradigm for supercomputing on a large number of geographically disperse computers.〔 Quasi-opportunistic supercomputing aims to provide a higher quality of service than opportunistic resource sharing.〔''Computational Science - Iccs 2008: 8th International Conference'' edited by Marian Bubak 2008 ISBN 978-3-540-69383-3 pages 112-113 ()〕 The quasi-opportunistic approach coordinates computers which are often under different ownerships to achieve reliable and fault-tolerant high performance with more control than opportunistic computer grids in which computational resources are used whenever they may become available.〔''Quasi-opportunistic supercomputing in grids'' by Valentin Kravtsov, David Carmeli , Werner Dubitzky , Ariel Orda , Assaf Schuster , Benny Yoshpa, in IEEE International Symposium on High Performance Distributed Computing, 2007, pages 233-244 ()〕 While the "opportunistic match-making" approach to task scheduling on computer grids is simpler in that it merely matches tasks to whatever resources may be available at a given time, demanding supercomputer applications such as weather simulations or computational fluid dynamics have remained out of reach, partly due to the barriers in reliable sub-assignment of a large number of tasks as well as the reliable availability of resources at a given time.〔〔''Computational Science - Iccs 2009: 9th International Conference'' edited by Gabrielle Allen, Jarek Nabrzyski 2009 ISBN 3-642-01969-2 pages 387-388 ()〕 The quasi-opportunistic approach enables the execution of demanding applications within computer grids by establishing grid-wise resource allocation agreements; and fault tolerant message passing to abstractly shield against the failures of the underlying resources, thus maintaining some opportunism, while allowing a higher level of control.〔 ==Opportunistic supercomputing on grids== The general principle of grid computing is to use distributed computing resources from diverse administrative domains to solve a single task, by using resources as they become available. Traditionally, most grid systems have approached the task scheduling challenge by using an "opportunistic match-making" approach in which tasks are matched to whatever resources may be available at a given time.〔''Grid computing: experiment management, tool integration, and scientific workflows'' by Radu Prodan, Thomas Fahringer 2007 ISBN 3-540-69261-4 pages 1-4〕 BOINC, developed at the University of California, Berkeley is an example of a volunteer-based, opportunistic grid computing system.〔''Parallel and Distributed Computational Intelligence'' by Francisco Fernández de Vega 2010 ISBN 3-642-10674-9 pages 65-68〕 The applications based on the BOINC grid have reached multi-petaflop levels by using close to half a million computers connected on the internet, whenever volunteer resources become available.〔(BOIN statistics, 2011 )〕 Another system, Folding@home, which is not based on BOINC, computes protein folding, has reached 8.8 petaflops by using clients that include GPU and PlayStation 3 systems.〔(Folding@home statistics, 2011 )〕〔〔 However, these results are not applicable to the TOP500 ratings because they do not run the general purpose Linpack benchmark. A key strategy for grid computing is the use of middleware that partitions pieces of a program among the different computers on the network.〔''Languages and Compilers for Parallel Computing'' by Guang R. Gao 2010 ISBN 3-642-13373-8 pages 10-11〕 Although general grid computing has had success in parallel task execution, demanding supercomputer applications such as weather simulations or computational fluid dynamics have remained out of reach, partly due to the barriers in reliable sub-assignment of a large number of tasks as well as the reliable availability of resources at a given time.〔〔〔 The opportunistic (Internet PrimeNet Server ) supports GIMPS, one of the earliest grid computing projects since 1997, researching Mersenne prime numbers. , GIMPS's distributed research currently achieves about 60 teraflops as an volunteer-based computing project. The use of computing resources on "volunteer grids" such as GIMPS is usually purely opportunistic: geographically disperse distributively owned computers are contributing whenever they become available, with no preset commitments that any resources will be available at any given time. Hence, hypothetically, if many of the volunteers unwittingly decide to switch their computers off on a certain day, grid resources will become significantly reduced.〔〔〔''Euro-par 2010, Parallel Processing Workshop'' edited by Mario R. Guarracino 2011 ISBN 3-642-21877-6 pages 274-277〕 Furthermore, users will find it exceedingly costly to organize a very large number of opportunistic computing resources in a manner that can achieve reasonable high performance computing.〔''Grid Computing: Towards a Global Interconnected Infrastructure'' edited by Nikolaos P. Preve 2011 ISBN 0-85729-675-2 page 71〕 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Quasi-opportunistic supercomputing」の詳細全文を読む スポンサード リンク
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