LU Factorization of Small Matrices: Accelerating Batched DGETRF on the GPU

TitleLU Factorization of Small Matrices: Accelerating Batched DGETRF on the GPU
Publication TypeConference Paper
Year of Publication2014
AuthorsDong, T., A. Haidar, P. Luszczek, J. Harris, S. Tomov, and J. Dongarra
Conference Name16th IEEE International Conference on High Performance Computing and Communications (HPCC)
Date Published08-2014
PublisherIEEE
Conference LocationParis, France
Abstract

Gaussian Elimination is commonly used to solve dense linear systems in scientific models. In a large number of applications, a need arises to solve many small size problems, instead of few large linear systems. The size of each of these small linear systems depends, for example, on the number of the ordinary differential equations (ODEs) used in the model, and can be on the order of hundreds of unknowns. To efficiently exploit the computing power of modern accelerator hardware, these linear systems are processed in batches. To improve the numerical stability of the Gaussian Elimination, at least partial pivoting is required, most often accomplished with row pivoting. However, row pivoting can result in a severe performance penalty on GPUs because it brings in thread divergence and non-coalesced memory accesses. The state-of-the-art libraries for linear algebra that target GPUs, such as MAGMA, focus on large matrix sizes. They change the data layout by transposing the matrix to avoid these divergence and non-coalescing penalties. However, the data movement associated with transposition is very expensive for small matrices. In this paper, we propose a batched LU factorization for GPUs by using a multi-level blocked right looking algorithm that preserves the data layout but minimizes the penalty of partial pivoting. Our batched LU achieves up to 2:5-fold speedup when compared to the alternative CUBLAS solutions on a K40c GPU and 3:6-fold speedup over MKL on a node of the Titan supercomputer at ORNL in a nuclear reaction network simulation.

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