A mixed precision LOBPCG algorithm

Fuente: arXiv
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Auteurs principaux: Kressner, Daniel, Ma, Yuxin, Shao, Meiyue
Format: Preprint
Publié: 2023
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author Kressner, Daniel
Ma, Yuxin
Shao, Meiyue
author_facet Kressner, Daniel
Ma, Yuxin
Shao, Meiyue
contents The locally optimal block preconditioned conjugate gradient (LOBPCG) algorithm is a popular approach for computing a few smallest eigenvalues and the corresponding eigenvectors of a large Hermitian positive definite matrix A. In this work, we propose a mixed precision variant of LOBPCG that uses a (sparse) Cholesky factorization of A computed in reduced precision as the preconditioner. To further enhance performance, a mixed precision orthogonalization strategy is proposed. To analyze the impact of reducing precision in the preconditioner on performance, we carry out a rounding error and convergence analysis of PINVIT, a simplified variant of LOBPCG. Our theoretical results predict and our numerical experiments confirm that the impact on convergence remains marginal. In practice, our mixed precision LOBPCG algorithm typically reduces the computation time by a factor of 1.4--2.0 on both CPUs and GPUs.
format Preprint
id arxiv_https___arxiv_org_abs_2302_12528
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle A mixed precision LOBPCG algorithm
Kressner, Daniel
Ma, Yuxin
Shao, Meiyue
Numerical Analysis
65F15, 65F50
The locally optimal block preconditioned conjugate gradient (LOBPCG) algorithm is a popular approach for computing a few smallest eigenvalues and the corresponding eigenvectors of a large Hermitian positive definite matrix A. In this work, we propose a mixed precision variant of LOBPCG that uses a (sparse) Cholesky factorization of A computed in reduced precision as the preconditioner. To further enhance performance, a mixed precision orthogonalization strategy is proposed. To analyze the impact of reducing precision in the preconditioner on performance, we carry out a rounding error and convergence analysis of PINVIT, a simplified variant of LOBPCG. Our theoretical results predict and our numerical experiments confirm that the impact on convergence remains marginal. In practice, our mixed precision LOBPCG algorithm typically reduces the computation time by a factor of 1.4--2.0 on both CPUs and GPUs.
title A mixed precision LOBPCG algorithm
topic Numerical Analysis
65F15, 65F50
url https://arxiv.org/abs/2302.12528