NDLI: A Class of Hybrid LAPACK Algorithms for Multicore and GPU Architectures

Content Provider	IEEE Xplore Digital Library
Author	Horton, M. Tomov, S. Dongarra, J.
Copyright Year	2011
Abstract	Three out of the top four supercomputers in the November 2010 TOP500 list of the world's most powerful supercomputers use NVIDIA GPUs to accelerate computations. Ninety-five systems from the list are using processors with six or more cores. Three-hundred-sixty-five systems use quad-core processor-based systems. Thirty-seven systems are using dual-core processors. The large-scale enabling of hybrid graphics processing unit (GPU)-based multicore platforms for computational science by developing fundamental numerical libraries (in particular, libraries in the area of dense linear algebra) for them has been underway for some time. We present a class of algorithms based largely on software infrastructures that have already been developed for homogeneous multicores and hybrid GPU-based computing. The algorithms extend what is currently available in the Matrix Algebra for GPU and Multicore Architectures (MAGMA) Library for performing Cholesky, QR, and LU factorizations using a single core or socket and a single GPU. The extensions occur in two areas. First, panels factored on the CPU using LAPACK are, instead, done in parallel using a highly optimized dynamic asynchronous scheduled algorithm on some number of CPU cores. Second, the remaining CPU cores are used to update the rightmost panels of the matrix in parallel.
Starting Page	150
Ending Page	158
File Size	308851
Page Count	9
File Format	PDF
ISBN	9781457706356
DOI	10.1109/SAAHPC.2011.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-19
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multicore QR Multicore processing Heuristic algorithms Sockets Dynamic scheduling LU Libraries Cholesky GPU Graphics processing unit
Content Type	Text
Resource Type	Article

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