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000153430 0247_ $$2doi$$a10.1007/978-3-642-54420-0_21
000153430 0247_ $$2ISSN$$a1611-3349
000153430 0247_ $$2ISSN$$a0302-9743
000153430 037__ $$aFZJ-2014-03047
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000153430 1001_ $$0P:(DE-Juel1)157723$$aAdinets, Andrey$$b0$$eCorresponding Author$$ufzj
000153430 1112_ $$aEuro-Par 2013: Parallel Processing Workshops$$cAachen$$d2013-08-25 - 2013-08-30$$wGermany
000153430 245__ $$aComputation of Mutual Information Metric for Image Registration on Multiple GPUs
000153430 260__ $$aBerlin, Heidelberg$$bSpringer Berlin Heidelberg$$c2014
000153430 29510 $$aEuro-Par 2013: Parallel Processing Workshops
000153430 300__ $$a208 - 217
000153430 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1399528593_3032
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000153430 3367_ $$2BibTeX$$aINPROCEEDINGS
000153430 4900_ $$aLecture Notes in Computer Science$$v8374
000153430 520__ $$aBecause of their computational power, GPUs are widely used in the field of image processing. Registration of brain images has already been successfully accelerated with GPUs, but registration of high-resolution human brain images presents new challenges due to large amounts of data and images not fitting in the memory of a single device.In this paper, we address this issue with two approaches. The first approach replicates image data in system memory of each node and distributes only a part of the data over multiple GPUs. The second approach splits image data between multiple GPUs, and overlaps computation and communication to hide latency. For both approaches, we present a performance analysis and comparison.
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000153430 7001_ $$0P:(DE-HGF)0$$aKraus, Jiri$$b1
000153430 7001_ $$0P:(DE-Juel1)131632$$aAxer, Markus$$b2$$ufzj
000153430 7001_ $$0P:(DE-Juel1)138708$$aHuysegoms, Marcel$$b3$$ufzj
000153430 7001_ $$0P:(DE-Juel1)157789$$aKöhnen, Stefan$$b4$$ufzj
000153430 7001_ $$0P:(DE-Juel1)144441$$aPleiter, Dirk$$b5$$ufzj
000153430 773__ $$a10.1007/978-3-642-54420-0_21
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000153430 9141_ $$y2014
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