Rgs-SpMM: Accelerate Sparse Matrix-Matrix Multiplication by Row Group Splitting Strategy on the GPU

Abstract

The Sparse Matrix-Matrix Multiplication (SpMM) operation is widely used in different fields, especially the recently popular GNN framework. Researchers have designed many kernels on the GPU to accelerate the SpMM operation. Existing methods mostly adopt a row splitting strategy to obtain better parallelism and memory access efficiency. However, due to irregularities of sparse matrices such as short rows with few non-zero elements, current methods suffer from the underutilization of thread resources in GPU. In this paper, We rearrange the distribution of non-zero elements in the sparse matrix and design the SpMM kernel based on the row group splitting strategy. In contrast to previous methods which assign a “row” task unit to a warp for processing, we combine short rows in a sparse matrix into “row groups” as a task unit, which allocate more appropriate non-zero elements tasks to the GPU resources. This method reduces the thread divergence in a warp and improves load balancing among warps. Our experimental data comes from the SNAP Matrix Collection. The results show that our kernel is faster than cuSPARSE and GE-SpMM, with an average speedup of 1.61 and 1.42 respectively.