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Abstract Details

Activity Number: 458
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #304976
Title: GPUs in High-Dimensional Statistical Computing
Author(s): Hua Zhou*+ and Kenneth Lange and Marc A Suchard
Companies: North Carolina State University and University of California at Los Angeles and University of California at Los Angeles
Address: 109 Silverbow Ct, Cary, NC, 27519, United States

Graphics processing units (GPUs) are becoming increasingly useful in high-dimensional statistical problems. A single GPU card with hundreds of arithmetic cores can be inserted in a personal computer and dramatically accelerates many statistical algorithms. To exploit these devices fully, optimization algorithms should reduce to multiple parallel tasks, each accessing a limited amount of data. These criteria favor EM and MM algorithms that separate parameters and data. To a lesser extent block relaxation and coordinate descent and ascent also qualify. We demonstrate the utility of GPUs in nonnegative matrix factorization, PET image reconstruction, multidimensional scaling, and protein isoform deconvolution. Speedups of 100 fold can easily be attained. Over the next decade, GPUs will fundamentally alter the landscape of computational statistics. It is time for more statisticians to get on-board.

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