Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression
KAUST DepartmentApplied Mathematics and Computational Science Program
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extreme Computing Research Center
Permanent link to this recordhttp://hdl.handle.net/10754/625473
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AbstractWe 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.
CitationHalim Boukaram W, Turkiyyah G, Ltaief H, Keyes DE (2017) Batched QR and SVD Algorithms on GPUs with Applications in Hierarchical Matrix Compression. Parallel Computing. Available: http://dx.doi.org/10.1016/j.parco.2017.09.001.
SponsorsThe work of all four authors was supported by the Extreme Computing Research Center at the King Abdullah University of Science and Technology. We thank the NVIDIA Corporation for providing access to the P100 GPU used in this work.