摘要
分析稀疏矩阵向量乘(SpMV)程序优化的难点,介绍两个自动调优的代表性工作:基于预实现模板的SMAT和从头设计程序的AlphaSparse。详细介绍了它们的设计思路、实现细节、测试结果以及各自的优缺点。最后,对SpMV自动调优的发展趋势进行了分析和预测。
SpMV(sparse matrix-vector multiplication)is a widely used kernel in scientific computing.Since the performance of specific SpMV program is closely related to the distribution of non-zero elements in sparse matrices,there is no universal SpMV program design that can achieve high performance in all matrices.Therefore,auto-tuning has become a popular method for high SpMV performance.This paper analyzes the difficulties in optimizing SpMV and introduces two representative works of auto-tuning:SMAT,which is based on pre-implemented templates and AlphaSparse which designs SpMV programs from scratch.This paper introduces their designs,implementations,test results,advantages,and disadvantages.Finally,the trend of SpMV auto-tuning is analyzed and predicted.
作者
杜臻
谭光明
DU Zhen;TAN Guangming(School of Computer and Control Engineering,University of Chinese Academy of Sciences,Beijing 101408,China;Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)
出处
《计算物理》
CSCD
北大核心
2024年第1期33-39,共7页
Chinese Journal of Computational Physics
基金
国家自然科学基金杰出青年基金项目(T2125013)资助。
关键词
高性能科学计算
稀疏矩阵
自动调优
稀疏矩阵向量乘
high-performance scientific computing
sparse matrix
auto-tuning
sparse matrix-vector multiplication
