国防科技大学学报2024，Vol.46Issue(1)：93-102,10.DOI:10.11887/j.cn.202401010
面向众核处理器的阴阳K-means算法优化
Optimizing Yinyang K-means algorithm on many-core CPUs
摘要
Abstract
Traditional Yinyang K-means algorithm is computationally expensive when dealing with large-scale clustering problems.An efficient parallel acceleration implementation of Yinyang K-means algorithm was proposed on the basis of the architectural characteristics of typical many-core CPUs.This implementation was based on a new memory data layout,used vector units in many-core CPUs to accelerate distance calculation in Yinyang K-means,and targeted memory access optimization for NUMA(non-uniform memory access)characteristics.Compared with the open source multi-threaded version of Yinyang K-means algorithm,this implementation can achieve the speedup of up to5.6 and8.7 approximately on ARMv8 and x86 many-core CPUs,respectively.Experiments show that the optimization successfully accelerate Yinyang K-means algorithm in many-core CPUs.关键词
K-means/非一致内存访问/向量化/众核处理器/性能优化Key words
K-means/NUMA/vectorization/many-core CPU/performance optimization分类
计算机与自动化引用本文复制引用
周天阳,王庆林,李荣春,梅松竹,尹尚飞,郝若晨,刘杰..面向众核处理器的阴阳K-means算法优化[J].国防科技大学学报,2024,46(1):93-102,10.基金项目
国家自然科学基金资助项目(62002365) （62002365）
