计算机工程与科学2018，Vol.40Issue(5)：798-804,7.DOI:10.3969/j.issn.1007-130X.2018.05.006
一种基于遗传算法的BLAS库优化方法
A BLAS library optimization method based on genetic algorithm
摘要
Abstract
Based on OpenBLAS and BLIS,the two open source linear algebra libraries,the performance optimization of dense matrix multiplication (GEMM) operation is studied.Aiming at how to select the key block parameters of GEMM,a performance optimization model is established.An improved genetic algorithm is used to solve the above performance optimization model.The performance value of the GEMM corresponding to a certain parameter combination (individual) is taken as the fitness of the individual.The optimal combination of block parameters is found through continuous iterative selection,crossover and mutation operations in order to make the performance of GEMM optimal.Numerical experiments show that the performance of GEMM based on genetic algorithm is better than the performance under the initial block parameters,and hence the optimization is achieved.关键词
BLAS/GEMM/遗传算法/自动调优Key words
BLAS/GEMM/genetic algorithm/autotuning分类
信息技术与安全科学引用本文复制引用
孙成国,兰静,姜浩..一种基于遗传算法的BLAS库优化方法[J].计算机工程与科学,2018,40(5):798-804,7.基金项目
国家863项目(2012AA01A301) （2012AA01A301）
国家自然科学基金(61402495,61303189,61602166,61170049,61402496) （61402495,61303189,61602166,61170049,61402496）
重庆市教育科学规划课题重点项目(2015-GX-036) （2015-GX-036）
