Sampled Dense Matrix Multiplication for High-Performance Machine Learning

Many machine learning methods involve iterative optimization and are amenable to a variety of alternate formulations. Many currently popular formulations for some machine learning methods based on core operations that essentially correspond to sparse matrix-vector products. A reformulation using sparse matrix-matrix products primitives can potentially enable significant performance enhancement. Sampled Dense-Dense Matrix Multiplication (SDDMM) is a primitive that has been shown to be usable as a core component in reformulations of many machine learning factor analysis algorithms such as Alternating Least Squares (ALS), Latent Dirichlet Allocation (LDA), Sparse Factor Analysis (SFA), and Gamma Poisson (GaP). It requires the computation of the product of two input dense matrices but only at locations of the result matrix corresponding to nonzero entries in a sparse third input matrix. In this paper, we address the development of cuSDDMM, a multi-node GPU-accelerated implementation for SDDMM. We analyze the data reuse characteristics of SDDMM and develop a model-driven strategy for choice of tiling permutation and tile-size choice. cuSDDMM improves significantly (up to 4.6x) over the best currently available GPU implementation of SDDMM (in the BIDMach Machine Learning library).