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research
An efficient sparse conjugate gradient solver using a Beneš permutation network
Authors
PA Burovskiy
G Chow
P Grigoras
W Luk
Publication date
2 September 2014
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
Abstract
© 2014 Technical University of Munich (TUM).The conjugate gradient (CG) is one of the most widely used iterative methods for solving systems of linear equations. However, parallelizing CG for large sparse systems is difficult due to the inherent irregularity in memory access pattern. We propose a novel processor architecture for the sparse conjugate gradient method. The architecture consists of multiple processing elements and memory banks, and is able to compute efficiently both sparse matrix-vector multiplication, and other dense vector operations. A Beneš permutation network with an optimised control scheme is introduced to reduce memory bank conflicts without expensive logic. We describe a heuristics for offline scheduling, the effect of which is captured in a parametric model for estimating the performance of designs generated from our approach
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Spiral - Imperial College Digital Repository
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oai:spiral.imperial.ac.uk:1004...
Last time updated on 17/02/2017
Crossref
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info:doi/10.1109%2Ffpl.2014.69...
Last time updated on 02/01/2020