Swift: High-Performance Sparse-Dense Matrix Multiplication on GPUs

Sparse-Dense Matrix Multiplication (SpMM) on GPUs has gained significant attention because of its importance in modern applications and the increasing computing power of GPUs in the last decade. Previous SpMM studies have focused on the importance of storage format and load balance for the overall performance of SpMM on GPUs. However, very little attention has been paid to the efficacy of coalesced memory access in improving the efficiency of data loading, which incurs a notable overhead that amounts to an average of more than 32% of the overall performance, according to our experimental observation. Existing state-of-the-art (SOTA) solutions fail to adequately support coalesced memory access of both sparse and dense matrices between the global memory and threads on GPUs. In this paper, we propose an efficient algorithm called Swift.11Swift is available at https://github.com/MinttHu/Swift.git that speeds up the loading of both sparse and dense matrices of SpMM on modern GPUs. Leveraging coalesced memory access, Swift achieves high loading efficiency by sorting both the columns of the sparse matrix and elements of the dense matrix based on the number of non-zero elements and balancing the load by handling the regular and irregular parts differently and judiciously. Swift takes the Compressed Sparse Column format as an implementation case study to prove the concept and gain insights. We conduct a comprehensive comparison of Swift with four SOTA solutions: ASpT, cuSPARSE, RoDe, and Sputnik, using the full SuiteSparse Matrix Collection as the workload. The experimental results on RTX 4080s, RTX 3090Ti, A100, and V100 demonstrate that our method outperforms the baselines significantly.