NDLI: Scalable clustering using multiple GPUs

Content Provider	IEEE Xplore Digital Library
Author	Wasif, M.K. Narayanan, P.J.
Copyright Year	2011
Description	Author affiliation: Center for Visual Information Technology, International Institute of Information and Technology, Hyderabad, India (Wasif, M.K.; Narayanan, P.J.)
Abstract	K-Means is a popular clustering algorithm with wide applications in Computer Vision, Data mining, Data Visualization, etc. Clustering is an important step for indexing and searching of documents, images, video, etc. Clustering large numbers of high-dimensional vectors is very computation intensive. In this paper, we present the design and implementation of the K-Means clustering algorithm on the modern GPU. All steps are performed entirely on the GPU efficiently in our approach. We also present a load balanced multi-node, multi-GPU implementation which can handle up to 6 million, 128-dimensional vectors. We use efficient memory layout for all steps to get high performance. The GPU accelerators are now present on high-end workstations and low-end laptops. Scalability in the number and dimensionality of the vectors, the number of clusters, as well as in the number of cores available for processing are important for usability to different users. Our implementation scales linearly or near-linearly with different problem parameters. We achieve up to 2 times increase in speed compared to the best GPU implementation for K-Means on a single GPU. We obtain a speed up of over 170 on a single Nvidia Fermi GPU compared to a standard sequential implementation. We are able to execute one iteration of K-Means in 136 seconds on off-the-shelf GPUs to cluster 6 million vectors of 128 dimensions into 4K clusters and in 2.5 seconds to cluster 125K vectors of 128 dimensions into 2K clusters.
Starting Page	1
Ending Page	10
File Size	452684
Page Count	10
File Format	PDF
ISBN	9781457719516
e-ISBN	9781457719509
e-ISBN	9781457719493
DOI	10.1109/HiPC.2011.6152713
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-18
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Graphics processing unit Clustering algorithms Kernel Computer architecture Message systems Instruction sets
Content Type	Text
Resource Type	Article

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