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000811415 0247_ $$2doi$$a10.1007/978-3-319-41321-1_19
000811415 0247_ $$2ISSN$$a0302-9743
000811415 0247_ $$2ISSN$$a1611-3349
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000811415 020__ $$a978-3-319-41320-4 (print)
000811415 020__ $$a978-3-319-41321-1 (electronic)
000811415 037__ $$aFZJ-2016-03899
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000811415 1001_ $$0P:(DE-HGF)0$$aKumbhar, Pramod$$b0$$eCorresponding author
000811415 1112_ $$a31st International Conference High Performance Computing$$cFrankfurt$$d2016-06-19 - 2016-06-23$$gISC16$$wGermany
000811415 245__ $$aLeveraging a Cluster-Booster Architecture for Brain-Scale Simulations
000811415 260__ $$aCham$$bSpringer International Publishing$$c2016
000811415 29510 $$aProceedings of the 31st International Conference High Performance Computing
000811415 300__ $$a363 - 380
000811415 3367_ $$2ORCID$$aCONFERENCE_PAPER
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000811415 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1468831926_23340
000811415 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
000811415 4900_ $$aLecture Notes in Computer Science$$v9697
000811415 520__ $$aThe European Dynamical Exascale Entry Platform (DEEP) is an example of a new type of heterogeneous supercomputing architecture that include both a standard multicore-based “Cluster” used to run less scalable parts of an application, and an Intel MIC-based “Booster” used to run highly scalable compute kernels. In this paper we describe how the compute engine of the widely used NEURON scientific application has been ported on both the DEEP and the Intel MIC platform. We discuss the design and implementation of the core simulator with an emphasis on the development workflow and implementation details that enable the efficient use of the new “Cluster-Booster” type of architectures. We describe optimizations of the data structures and algorithms tailored to the Intel Xeon Phi coprocessor which contributed to improve the overall performance of NEURON by a factor 5. Validation of our implementation has first been done on STAMPEDE supercomputer in order to emulate the DEEP architecture performance. Building on these results, we then explored opportunities offered by the DEEP platform to efficiently support complex scientific workflow.
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000811415 536__ $$0G:(EU-Grant)287530$$aDEEP - Dynamical Exascale Entry Platform (287530)$$c287530$$fFP7-ICT-2011-7$$x2
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000811415 7001_ $$0P:(DE-HGF)0$$aHines, Michael$$b1
000811415 7001_ $$0P:(DE-HGF)0$$aOvcharenko, Aleksandr$$b2
000811415 7001_ $$0P:(DE-Juel1)144660$$aAlvarez, Damian$$b3
000811415 7001_ $$0P:(DE-HGF)0$$aKing, James$$b4
000811415 7001_ $$0P:(DE-HGF)0$$aSainz, Florentino$$b5
000811415 7001_ $$0P:(DE-HGF)0$$aSchürmann, Felix$$b6
000811415 7001_ $$0P:(DE-HGF)0$$aDelalondre, Fabien$$b7
000811415 773__ $$a10.1007/978-3-319-41321-1_19
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