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<dc:title xml:lang="pl"><![CDATA[Two implementations of the preconditioned conjugate gradient method on heterogeneous computing grids]]></dc:title>
<dc:creator><![CDATA[Collignon, Tijmen P.]]></dc:creator>
<dc:creator><![CDATA[Van Gijzen, Martin B.]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[grid computing]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[large sparse linear systems]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[iterative methods]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[conjugate gradient methods]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[Chronopoulos/Gear CG]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[GridSolve middleware]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[bubbly flows]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[Efficient iterative solution of large linear systems on grid computers is a complex problem. The induced heterogeneity andvolatile nature of the aggregated computational resources present numerous algorithmic challenges. This paper describesa case study regarding iterative solution of large sparse linear systems on grid computers within the software constraintsof the grid middleware GridSolve and within the algorithmic constraints of preconditioned Conjugate Gradient (CG) typemethods.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[We identify the various bottlenecks induced by the middleware and the iterative algorithm. We consider thestandard CG algorithm of Hestenes and Stiefel, and as an alternative the Chronopoulos/Gear variant, a formulation thatis potentially better suited for grid computing since it requires only one synchronisation point per iteration, instead oftwo for standard CG. In addition, we improve the computation-to-communication ratio by maximising the work in thepreconditioner.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[In addition to these algorithmic improvements, we also try to minimise the communication overhead withinthe communication model currently used by the GridSolve middleware. We present numerical experiments on 3D bubblyflow problems using heterogeneous computing hardware that show lower computing times and better speed-up for theChronopoulos/Gear variant of conjugate gradients. Finally, we suggest extensions to both the iterative algorithm and themiddleware for improving granularity.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - red.]]></dc:contributor>
<dc:contributor><![CDATA[Uciński, Dariusz - red.]]></dc:contributor>
<dc:date><![CDATA[2010]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/46838/AMCS_2010_20_1_8.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/54931/edition/46838/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:46838]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, Volume 20, Number 1 (2010)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=paper&paper=478]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:54931]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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