Matrix Factorization on GPUs with Memory Optimization and Approximate Computing

Abstract

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 insufficient. This is because a single machine provides limited compute power for large-scale data while multiple machines suffer from the network communication bottleneck.To address the aforementioned challenge, accelerating ALS on graphics processing units (GPUs) is a promising direction. We propose the novel approach in enhancing the MF efficiency via both memory optimization and approximate computing. The former exploits GPU memory hierarchy to increase data reuse, while the later reduces unnecessary computing without hurting the convergence of learning algorithms. Extensive experiments on large-scale datasets show that our solution not only outperforms the competing CPU solutions by a large margin but also has a 2x-4x performance gain compared to the state-of-the-art GPU solutions. Our implementations are open-sourced and publicly available.A graphical illustration of our proposed approach is shown in

ALX: Large Scale Matrix Factorization on TPUs

“…To take advantage of hardware acceleration, [17] propose cuMF, a single-machine, memory-optimized multi-GPU implementation that scales to relatively large problems (up to 10 11 model parameters), further extended in [18] to allow approximate computation via a conjugate gradient solver. [17,18] exploit GPU memory hierarchy and model parallelism across GPUs in order to produce a highly performant implementation of ALS. As cuMF exploits unique properties of the GPU hardware, ALX overcomes various challenges and exploits unique properties of TPUs.…”