Fast truncated SVD of sparse and dense matrices on graphics processors.
Int. J. High Perform. Comput. Appl.(2023)
摘要
We investigate the solution of low-rank matrix approximation problems using
the truncated SVD. For this purpose, we develop and optimize GPU
implementations for the randomized SVD and a blocked variant of the Lanczos
approach. Our work takes advantage of the fact that the two methods are
composed of very similar linear algebra building blocks, which can be assembled
using numerical kernels from existing high-performance linear algebra
libraries. Furthermore, the experiments with several sparse matrices arising in
representative real-world applications and synthetic dense test matrices reveal
a performance advantage of the block Lanczos algorithm when targeting the same
approximation accuracy.
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