A dynamic parameter tuning method for SpMM parallel execution

Sparse matrix-matrix multiplication (SpMM) is a basic kernel that is used by many algorithms. Several researches focus on various optimizations for SpMM parallel execution. However, a division of a task for parallelization is not well considered yet. Generally, a matrix is equally divided into blocks for processes even though the sparsities of input matrices are different. The parameter that divides a task into multiple processes for parallelization is fixed. As a result, load imbalance among the processes occurs. To balance the loads among the processes, this article proposes a dynamic parameter tuning method by analyzing the sparsities of input matrices. The experimental results show that the proposed method improves the performance of SpMM for examined matrices by up to 39.5% on a single vector engine and 3.49 x on a single CPU.

Automated summary

Sparse matrix-matrix multiplication is a fundamental kernel used in many algorithms. This article proposes a dynamic parameter tuning method to balance the load among processes in order to improve the performance of SpMM.