Exploiting Online Locality and Reduction Parallelism for Sampled Dense Matrix Multiplication on GPUs

Abstract

Sampled Dense-Dense Matrix Multiplication (SDDMM) is a core component of many machine learning systems. SDDMM exposes a substantial amount of parallelism that favors throughput-oriented architectures like the GPU. However, accelerating it on GPUs is challenging in two aspects: the poor memory access locality caused by the sparse sampling matrix with the poor parallelism caused by the dot-product reduction of vectors in two dense matrices. To address both challenges, we present PRedS to boost SDDMM efficiency with a suite of Parallel Reduction Scheduling optimizations. PRedS uses Vectorized Coarsen 1-Dimensional Tiling (VCT) to benefit the online locality of loading the dense matrix. PRedS uses Integrated Interleaving Reduction (IIR) to increase thread occupancy in the parallel reduction. PRedS also leverages Warp-Merged Tiling (WMT) to preserve occupancy and parallelism when reducing very long arrays. Enhanced with GPU-intrinsic vectorized memory loading, PRedS achieves a geometric speedup of 29.20× compared to the vendor library. PRedS achieves up to 8.31× speedup over state-of-the-art implementations on the SuiteSparse benchmark.