Parallel MCGLS and ICGLS Methods for Least Squares Problems on Distributed Memory Architectures

Abstract

In this paper we mainly study the parallelization of the CGLS method, a basic iterative method for large and sparse least squares problems in which the conjugate gradient method is applied to solve normal equations. On modern parallel architectures its parallel performance is always limited because of the global communication required for inner products, the main bottleneck of parallel performance. In this paper, we describe a modified CGLS (MCGLS) method which improve parallel performance by assembling the results of a number of inner products collectively and by creating situations where communication can be overlapped with computation. More importantly, we also propose an improved CGLS (ICGLS) method to reduce inner product’s global synchronization points to half, then significantly improve the parallel performance accordingly compared with the standard CGLS method and the MCGLS method.KeywordsConjugate Gradient MethodParallel PerformanceSparse MatriceExact ArithmeticDistribute Memory ArchitectureThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.