摘要
针对稀疏矩阵与稠密向量乘运算探讨了不同的任务分配策略对性能的影响,观察到任务分配策略的选择会显著地影响稀疏矩阵的运算性能,且不存在一种固定的任务分配策略针对所有的稀疏矩阵都能获得最佳性能。为此,提出了一种基于机器学习的最优任务分配策略选择模型,其训练过程仅使用稀疏矩阵的特征来刻画输入数据集,且能够针对给定的数据集和目标平台自动地训练模型。实验结果表明,相对于默认的块分配方法,使用该模型选择的任务分配方式能够获得平均约35%的性能提升。
In this paper,the effects of different task allocation strategies on the performance of sparse matrix and dense vector multiplication are discussed.It is observed that the selection of task allocation strategy can significantly affect the performance of sparse matrix,and there is no fixed task allocation strategy that can obtain the best performance for all sparse matrices.Therefore,this paper proposes an optimal task allocation strategy selection method based on machine learning.Its training process only uses sparse matrix features to characterize the input data set,and can automatically train the model for a given data set and target platform.Experiments show that,compared with the default block allocation method,the task allocation method selected by this model can achieve an average performance improvement of about 35%.
作者
李小玲
方建滨
马俊
谭霜
谭郁松
LI Xiao-ling;FANG Jian-bin;MA Jun;TAN Shuang;TAN Yu-song(College of Computer Science and Technology,National University of Defense Technology,Changsha 410073,China)
出处
《计算机工程与科学》
CSCD
北大核心
2023年第5期782-789,共8页
Computer Engineering & Science
基金
国家自然科学基金(61972408,U19A2060)。
关键词
稀疏矩阵向量乘
任务分配
机器学习
sparse matrix-vector multiplication
task allocation
machine learning
