Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores

TL;DR

This paper introduces tSparse, a novel GPU algorithm that leverages Tensor Cores for efficient sparse matrix-matrix multiplication by partitioning matrices into tiles and using mixed precision dense multiplication.

Contribution

First to utilize Tensor Cores for sparse matrix multiplication, proposing a tiling approach that significantly enhances performance over existing methods.

Findings

tSparse outperforms cuSPARSE, CUSP, and other libraries in speed.

The tiling approach effectively exploits Tensor Cores for sparsity.

Performance gains are demonstrated on various sparse matrices.

Abstract

Sparse general matrix-matrix multiplication (spGEMM) is an essential component in many scientific and data analytics applications. However, the sparsity pattern of the input matrices and the interaction of their patterns make spGEMM challenging. Modern GPUs include Tensor Core Units (TCUs), which specialize in dense matrix multiplication. Our aim is to re-purpose TCUs for sparse matrices. The key idea of our spGEMM algorithm, tSparse, is to multiply sparse rectangular blocks using the mixed precision mode of TCUs. tSparse partitions the input matrices into tiles and operates only on tiles which contain one or more elements. It creates a task list of the tiles, and performs matrix multiplication of these tiles using TCUs. To the best of our knowledge, this is the first time that TCUs are used in the context of spGEMM. We show that spGEMM, with our tiling approach, benefits from TCUs. Our approach significantly improves the performance of spGEMM in comparison to cuSPARSE, CUSP, RMerge2, Nsparse, AC-SpGEMM and spECK.