TY  - CHAP
AU  - Adinets, Andrey
AU  - Kraus, Jiri
AU  - Meinke, Jan
AU  - Pleiter, Dirk
TI  - GPUMAFIA: Efficient Subspace Clustering with MAFIA on GPUs
VL  - 8097
CY  - New York
PB  - Springer New York
M1  - FZJ-2013-04288
T2  - Lecture Notes in Computer Science
SP  - 838-849
PY  - 2013
N1  - 10.1007/978-3-642-40047-6_83
AB  - Clustering, 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.
T2  - Euro-Par 2013
CY  - 26 Aug 2013 - 30 Aug 2013, Aachen (Germany)
Y2  - 26 Aug 2013 - 30 Aug 2013
M2  - Aachen, Germany
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.1007/978-3-642-40047-6_83
UR  - https://juser.fz-juelich.de/record/138003
ER  -