The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Poster Type: Research Posters
Author: Ahmad Abdelfattah (University of Tennessee, Knoxville)
Supervisor:
Abstract: We consider the problem of computing the singular value decomposition (SVD) of many relatively small matrices using GPUs. This is an essential component in various scientific applications, including computational chemistry, low-rank approximations, and others. Our approach is based on the parallel one-sided Jacobi algorithm, which has a large degree of parallelism, and also heavily relies on compute-bound level-3 BLAS operations, such as matrix multiply. Our approach uses two design strategies. The first one targets very small matrices using a single GPU kernel for the entire SVD operation. The second design strategy uses a blocked version of the parallel Jacobi algorithm, which supports matrices of arbitrary dimensions. The proposed solution supports any matrix shape (square, tall-skinny, or short-wide), requires no limitations on the matrix dimensions, and delivers superior performance against state-of-the-art solutions. This work is set to be released in the MAGMA library.
Best Poster Finalist (BP): no
Poster: PDF
Poster Summary: PDF