Matrix Factorization on GPUs with Memory Optimization and Approximate Computing
Wei Tan, Shiyu Chang, Liana Fong, Cheng Li, Zijun Wang, Liangliang Cao · 2018
Matrix factorization (MF) discovers latent features from observations, which has shown great promises in the fields of collaborative filtering, data compression, feature extraction, word embedding, etc. While many problem-specific optimization techniques have been proposed, alternating least square (ALS) remains popular due to its general applicability (e.g. easy to handle positive-unlabeled inputs), fast convergence and parallelization capability. Current MF implementations are either optimized for a single machine or with a need of a large computer cluster but still are insufficent. This is because a single machine provides limited compute power for large-scale data while multiple machines suffer from the network communication bottleneck.