Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression

Wagih Halim Boukaram, George Turkiyyah, Hatem Ltaief, David E. Keyes

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

We present high performance implementations of the QR and the singular value decomposition of a batch of small matrices hosted on the GPU with applications in the compression of hierarchical matrices. The one-sided Jacobi algorithm is used for its simplicity and inherent parallelism as a building block for the SVD of low rank blocks using randomized methods. We implement multiple kernels based on the level of the GPU memory hierarchy in which the matrices can reside and show substantial speedups against streamed cuSOLVER SVDs. The resulting batched routine is a key component of hierarchical matrix compression, opening up opportunities to perform H-matrix arithmetic efficiently on GPUs.
Original languageEnglish (US)
Pages (from-to)19-33
Number of pages15
JournalParallel Computing
Volume74
DOIs
StatePublished - Sep 14 2017

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