Matrix factorization (MF) is used by many popular algorithms such as collaborative filtering. GPU with massive cores and high memory bandwidth sheds light on accelerating MF much further when appropriately exploiting its architectural characteristics. This paper presents cuMF, a CUDA-based matrix factorization library that optimizes alternate least square (ALS) method to solve very large-scale MF. CuMF uses a set of techniques to maximize the performance on single and multiple GPUs. These techniques include smart access of sparse data leveraging GPU memory hierarchy, using data parallelism in conjunction with model parallelism, minimizing the communication overhead among GPUs, and a novel topologyaware parallel reduction scheme. With only a single machine with four Nvidia GPU cards, cuMF can be 6-10 times as fast, and 33-100 times as costefficient, compared with the state-of-art distributed CPU solutions. Moreover, cuMF can solve the largest matrix factorization problem ever reported in current literature, with impressively good performance.
These techniques include smart access of sparse data leveraging GPU memory hierarchy, using data parallelism in conjunction with model parallelism, minimizing the communication overhead between computing units, and utilizing a novel topology-aware parallel reduction scheme REF .
Faster and Cheaper: Parallelizing Large-Scale Matrix Factorization on GPUs
HPDC '16
Computer Science