Efficient GPU implementation of randomized SVD and its applications

arXiv:2110.03423v2 Announce Type: replace
Abstract: Matrix decompositions are ubiquitous in machine learning, including applications in dimensionality reduction, data compression and deep learning algorithms. Typical solutions for matrix decompositions have polynomial complexity which significantly increases their computational cost and time. In this work, we leverage efficient processing operations that can be run in parallel on modern Graphical Processing Units (GPUs), predominant computing architecture used e.g. in deep learning, to reduce the computational burden of computing matrix decompositions. More specifically, we reformulate …

applications arxiv cs.lg cs.pf gpu implementation svd type