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 to 5.6 and 8.7 approximately on ARMv8 and x86 many-core CPUs, respectively. Experiments show that the optimization successfully accelerate Yinyang K-means algorithm in many-core CPUs.]]> 2024/1/28 0:00:00 ZHOU Tianyang, WANG Qinglin, LI Rongchun, MEI Songzhu, YIN Shangfei, HAO Ruochen, LIU Jie 6true 2024/1/28 0:00:00 XU Jinwei, WANG Qinglin, LI Yalin, JIANG Jingfei, GAO Lei, LI Rongchun, LI Dongsheng 5true 2024/1/28 0:00:00 HE Yuanhong, JIANG Jingfei, XU Jinwei 4true 2024/1/28 0:00:00 SU Huayou, CHEN Kangkang, YANG Qianming 3true 2024/1/28 0:00:00 QIAO Peng, HE Zhouyu, LI Rongchun, JIANG Jingfei 2true 2024/1/28 0:00:00 LI Rongchun, ZHOU Xin, QIAO Peng, WANG Qinglin 1true