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000138003 0247_ $$2DOI$$a10.1007/978-3-642-40047-6_83
000138003 037__ $$aFZJ-2013-04288
000138003 1001_ $$0P:(DE-Juel1)157723$$aAdinets, Andrey$$b0$$eCorresponding author$$ufzj
000138003 1112_ $$aEuro-Par 2013$$cAachen$$d2013-08-26 - 2013-08-30$$wGermany
000138003 245__ $$aGPUMAFIA: Efficient Subspace Clustering with MAFIA on GPUs
000138003 260__ $$aNew York$$bSpringer New York$$c2013
000138003 29510 $$aEuro-Par 2013 Parallel Processing
000138003 300__ $$a838-849
000138003 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$mcontrib
000138003 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$bcontb$$mcontb$$s1407160007_24848
000138003 3367_ $$2DRIVER$$abookPart
000138003 3367_ $$2ORCID$$aBOOK_CHAPTER
000138003 3367_ $$07$$2EndNote$$aBook Section
000138003 3367_ $$2BibTeX$$aINBOOK
000138003 3367_ $$2DataCite$$aOutput Types/Book chapter
000138003 4900_ $$0PERI:(DE-600)2018930-8$$aLecture Notes in Computer Science$$v8097
000138003 500__ $$a10.1007/978-3-642-40047-6_83
000138003 520__ $$aClustering, i.e., the identification of regions of similar objects in a multi-dimensional data set, is a standard method of data analytics with a large variety of applications. For high-dimensional data, subspace clustering can be used to find clusters among a certain subset of data point dimensions and alleviate the curse of dimensionality.In this paper we focus on the MAFIA subspace clustering algorithm and on using GPUs to accelerate the algorithm. We first present a number of algorithmic changes and estimate their effect on computational complexity of the algorithm. These changes improve the computational complexity of the algorithm and accelerate the sequential version by 1–2 orders of magnitude on practical datasets while providing exactly the same output. We then present the GPU version of the algorithm, which for typical datasets provides a further 1–2 orders of magnitude speedup over a single CPU core or about an order of magnitude over a typical multi-core CPU. We believe that our faster implementation widens the applicability of MAFIA and subspace clustering.
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000138003 536__ $$0G:(DE-HGF)POF2-41G21$$a41G - Supercomputer Facility (POF2-41G21)$$cPOF2-41G21$$fPOF II$$x1
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000138003 7001_ $$0P:(DE-HGF)0$$aKraus, Jiri$$b1
000138003 7001_ $$0P:(DE-Juel1)132189$$aMeinke, Jan$$b2$$ufzj
000138003 7001_ $$0P:(DE-Juel1)144441$$aPleiter, Dirk$$b3$$ufzj
000138003 773__ $$a10.1007/978-3-642-40047-6_83$$y2013
000138003 8564_ $$uhttp://link.springer.com/book/10.1007/978-3-642-40047-6/page/1
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000138003 9141_ $$y2013
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000138003 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144441$$aForschungszentrum Jülich GmbH$$b3$$kFZJ
000138003 9132_ $$0G:(DE-HGF)POF3-513$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data $$vSupercomputer Facility$$x0
000138003 9131_ $$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
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