电子科技大学学报2026，Vol.55Issue(2)：244-251,8.DOI:10.12178/1001-0548.2024358
GPU上Tensor Core加速的共轭梯度解法器
Tensor Core accelerated conjugate gradient solver on GPUs
摘要
Abstract
Conjugate gradient(CG)and biconjugate gradient stabilized(BiCGSTAB)are two classical and efficient iterative methods for solving sparse linear systems,widely used in scientific computing and engineering applications.Although GPUs and other parallel processors have enhanced the parallelism of these methods,the latest hardware unit,Tensor Core,and its computing power have not yet been fully exploited for these two methods.This work proposes a Tensor Core-accelerated CG solver that leverages Tensor Cores for the key components in the CG and BiCGSTAB methods,such as sparse matrix-vector multiplication(SpMV)and dot product computation,thereby exploiting the computational capability of Tensor Cores to improve the overall performance of both methods.Experimental results on NVIDIA A100 and H100 GPUs demonstrate that both of these methods accelerated by Tensor Core achieve significant speedups over the baseline version that uses the CUDA official library on various sparse matrices.关键词
稀疏矩阵-向量乘法/点积/共轭梯度法/稳定双共轭梯度法/张量核心/图形处理单元Key words
sparse matrix-vector multiplication/dot product/conjugate gradient/biconjugate gradient stabilized/Tensor Core/GPU分类
信息技术与安全科学引用本文复制引用
卢玥辰,袁雨萧,杨德闯,刘伟峰..GPU上Tensor Core加速的共轭梯度解法器[J].电子科技大学学报,2026,55(2):244-251,8.基金项目
国家自然科学基金(U23A20301,62372467) （U23A20301,62372467）
