NDLI: Biclustering and classification analysis in gene expression using Nonnegative Matrix Factorization on multi-GPU systems

Content Provider	IEEE Xplore Digital Library
Author	Mejia-Roa, E. Garcia, C. Gomez, J.I. Prieto, M. Tirado, F. Nogales, R. Pascual-Montano, A.
Copyright Year	2011
Description	Author affiliation: Functional Bioinformatics Group, Biocomputing Unit, National Center for Biotechnology-CSIC, UAM, Madrid 28049, Spain (Nogales, R.; Pascual-Montano, A.) \|\| ArTeCS Group, Department of Computer Architecture, Complutense University of Madrid (UCM), Madrid 28040, Spain (Mejia-Roa, E.; Garcia, C.; Gomez, J.I.; Prieto, M.; Tirado, F.)
Abstract	A great interest has been given to the Nonnegative Matrix Factorization (NMF) technique due to its ability of extracting highly-interpretable parts from data sets. Gene expression analysis is one of the most popular applications of NMF in Bioinformatics. Nonetheless, its usage is hindered by the computational complexity when processing large data sets. In this paper, we present two parallel implementations of NMF. The first version uses CUDA on a Graphics Processing Unit (GPU). Large input matrices are iteratively blockwise transferred and processed. The second implementation distributes data among multiple GPUs synchronized through MPI (Message Passing Interface). When analyzing large data sets with two and four GPUs, it performs respectively, 2.3 and 4.13 times faster than the single-GPU version. This represents about 120 times faster than a conventional CPU. These super linear speedups are achieved when data portions assigned to each GPU are small enough to be transferred only once.
Starting Page	882
Ending Page	887
File Size	844929
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121769
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Sample Classification Instruction sets Bi-clustering multi-GPU implementation Gene expression Matrix decomposition Graphics processing unit CUDA/CUBLAS Message Passing Interface (MPI) Nonnegative Matrix Factorization (NMF) Clustering algorithms Kernel
Content Type	Text
Resource Type	Article

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