NDLI: Iteration-fusing conjugate gradient

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Iteration-fusing conjugate gradient

Content Provider	ACM Digital Library
Author	Zhuang, Sicong Casas, Marc
Abstract	This paper presents the Iteration-Fusing Conjugate Gradient (IFCG) approach which is an evolution of the Conjugate Gradient method that consists in i) letting computations from different iterations to overlap between them and ii) splitting linear algebra kernels into subkernels to increase concurrency and relax data-dependencies. The paper presents two ways of applying the IFCG approach: The IFCG1 algorithm, which aims at hiding the cost of parallel reductions, and the IFCG2 algorithm, which aims at reducing idle time by starting computations as soon as possible. Both IFCG1 and IFCG2 algorithms are two complementary approaches aiming at increasing parallel performance. Extensive numerical experiments are conducted to compare the IFCG1 and IFCG2 numerical stability and performance against four state-of-the-art techniques. By considering a set of representative input matrices, the paper demonstrates that IFCG1 and IFCG2 provide parallel performance improvements up to 42.9% and 41.5% respectively and average improvements of 11.8% and 7.1% with respect to the best state-of-the-art techniques while keeping similar numerical stability properties. Also, this paper provides an evaluation of the IFCG algorithms' sensitivity to system noise and it demonstrates that they run 18.0% faster on average than the best state-of-the-art technique under realistic degrees of system noise.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450350204
DOI	10.1145/3079079.3079091
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-06-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Task parallelism Mitigation of synchronization costs Sparse linear algebra Overlap between iterations
Content Type	Text
Resource Type	Article

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