标题:Numerically Stable Recurrence Relations for the Communication Hiding Pipelined Conjugate Gradient Method
作者:Cools, Siegfried ;Cornelis, Jeffrey ;Vanroose, Wim
作者机构:[Cools, Siegfried ;Cornelis, Jeffrey ;Vanroose, Wim ] Department of Mathematics and Computer Science, Applied Mathematics Group, University of Antwerp 更多
通讯作者:Cools, Siegfried
来源:IEEE Transactions on Parallel and Distributed Systems
出版年:2019
卷:30
期:11
页码:2507-2522
DOI:10.1109/TPDS.2019.2917663
摘要:Pipelined Krylov subspace methods (also referred to as communication-hiding methods) have been proposed in the literature as a scalable alternative to classic Krylov subspace algorithms for iteratively computing the solution to a large linear system in parallel. For symmetric and positive definite system matrices the pipelined Conjugate Gradient method, p(ll)-CG, outperforms its classic Conjugate Gradient counterpart on large scale distributed memory hardware by overlapping global communication with essential computations like the matrix-vector product, thus hiding global communication. A well-known drawback of the pipelining technique is the (possibly significant) loss of numerical stability. In this work a numerically stable variant of the pipelined Conjugate Gradient algorithm is presented that avoids the propagation of local rounding errors in the finite precision recurrence relations that construct the Krylov subspace basis. The multi-term recurrence relation for the basis vector is replaced by ℓl three-term recurrences, improving stability without increasing the overall computational cost of the algorithm. The proposed modification ensures that the pipelined Conjugate Gradient method is able to attain a highly accurate solution independently of the pipeline length. Numerical experiments demonstrate a combination of excellent parallel performance and improved maximal attainable accuracy for the new pipelined Conjugate Gradient algorithm. This work thus resolves one of the major practical restrictions for the useability of pipelined Krylov subspace methods.
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收录类别:EI
资源类型:期刊论文
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