Towards Dense Linear Algebra for Hybrid GPU Accelerated Manycore Systems

Tomov, Stanimire and Dongarra, Jack and Baboulin, Marc (2009) Towards Dense Linear Algebra for Hybrid GPU Accelerated Manycore Systems. [MIMS Preprint]

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Abstract

If multicore is a disruptive technology, try to imagine hybrid multicore systems enhanced with accelerators! This is happening today as accelerators, in particular Graphics Processing Units (GPUs), are steadily making their way into the high performance computing (HPC) world. We highlight the trends leading to the idea of hybrid manycore/GPU systems, and we present a set of techniques that can be used to eciently program them. The presentation is in the context of Dense Linear Algebra (DLA), a major building block for many scientic computing applications.We motivate the need for new algorithms that would split the computation in a way that would fully exploit the power that each of the hybrid components oers. As the area of hybrid multicore/GPU computing is still in its infancy, we also argue for its importance in view of what future architectures may look like. We therefore envision the need for a DLA library similar to LAPACK but for hybrid manycore/GPU systems. We illustrate the main ideas with an LU-factorization algorithm where particular techniques are used to reduce the amount of pivoting, resulting in an algorithm achieving up to 388 GFlop/s for single and up to 99:4 GFlop/s for double precision factorization on a hybrid Intel Xeon (2x4 cores @ 2.33 GHz) { NVIDIA GeForce GTX 280 5 (240 cores @ 1.30 GHz) system.

Item Type:	MIMS Preprint
Additional Information:	Appears also as Technical Report UT-CS-08-632, Department of Computer Science, University of Tennessee, Knoxville, TN, USA, October 2008 and as LAPACK Working Note 210"
Uncontrolled Keywords:	hybrid computing, dense linear algebra, parallel algorithms, LU factorization, multicore processors, graphics processing units.
Subjects:	MSC 2010, the AMS's Mathematics Subject Classification > 65 Numerical analysis MSC 2010, the AMS's Mathematics Subject Classification > 68 Computer science
Depositing User:	Ms Lucy van Russelt
Date Deposited:	13 Jan 2009
Last Modified:	20 Oct 2017 14:12
URI:	https://eprints.maths.manchester.ac.uk/id/eprint/1213

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