An efficient sparse conjugate gradient solver using a Beneš permutation network
File(s)
Accepted version
Author(s)
Chow, G
Grigoras, P
Burovskiy, PA
Luk, W
Type
Conference Paper
Abstract
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.
Date Issued
2014-01-01
Date Acceptance
2014-09-02
Citation
2014 24th International Conference on Field Programmable Logic and Applications, FPL 2014, 2014
ISBN
9781479933624
Publisher
IEEE
Journal / Book Title
2014 24th International Conference on Field Programmable Logic and Applications, FPL 2014
Copyright Statement
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
24th International Conference on Field Programmable Logic and Applications
Publication Status
Published
Start Date
2014-09-02
Finish Date
2014-09-04
Coverage Spatial
Munich