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000150587 0247_ $$2doi$$a10.1109/HPCC.2012.88
000150587 0247_ $$2WOS$$aWOS:000310377500079
000150587 037__ $$aFZJ-2014-00636
000150587 1001_ $$0P:(DE-Juel1)132079$$aDachsel, Holger$$b0$$eCorresponding author$$ufzj
000150587 1112_ $$a2012 IEEE 14th Int'l Conf. on High Performance Computing and Communication (HPCC) & 2012 IEEE 9th Int'l Conf. on Embedded Software and Systems (ICESS)$$cLiverpool$$d2012-06-25 - 2012-06-27$$wUnited Kingdom
000150587 245__ $$aAutomatic Tuning of the Fast Multipole Method Based on Integrated Performance Prediction
000150587 260__ $$bIEEE$$c2012
000150587 29510 $$a2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems
000150587 300__ $$a617-624
000150587 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1391441314_8059
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000150587 3367_ $$033$$2EndNote$$aConference Paper
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000150587 520__ $$aThe Fast Multipole Method (FMM) is an efficient, widely used method for the solution of N-body problems. One of the main data structures is a hierarchical tree data structure describing the separation into near-field and far-field particle interactions. This article presents a method for automatic tuning of the FMM by selecting the optimal FMM tree depth based on an integrated performance prediction of the FMM computations. The prediction method exploits benchmarking of significant parts of the FMM implementation to adapt the tuning to the specific hardware system being used. Furthermore, a separate analysis phase at runtime is used to predict the computational load caused by the specific particle system to be computed. The tuning method was integrated into an FMM implementation. Performance results show that a reliable determination of the tree depth is achieved, thus leading to minimal execution times of the FMM algorithm.
000150587 536__ $$0G:(DE-HGF)POF2-411$$a411 - Computational Science and Mathematical Methods (POF2-411)$$cPOF2-411$$fPOF II$$x0
000150587 588__ $$aDataset connected to CrossRef Conference
000150587 7001_ $$0P:(DE-HGF)0$$aHofmann, Michael$$b1
000150587 7001_ $$0P:(DE-HGF)0$$aLang, Jens$$b2
000150587 7001_ $$0P:(DE-HGF)0$$aRunger, Gudula$$b3
000150587 773__ $$a10.1109/HPCC.2012.88
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000150587 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)132079$$aForschungszentrum Jülich GmbH$$b0$$kFZJ
000150587 9132_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data $$vComputational Science and Mathematical Methods$$x0
000150587 9131_ $$0G:(DE-HGF)POF2-411$$1G:(DE-HGF)POF2-410$$2G:(DE-HGF)POF2-400$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bSchlüsseltechnologien$$lSupercomputing$$vComputational Science and Mathematical Methods$$x0
000150587 9141_ $$y2013
000150587 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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