农业机械学报2014，Vol.45Issue(11)：47-53,74,8.DOI:10.6041/j.issn.1000-1298.2014.11.008
基于CUDA的并行K-means聚类图像分割算法优化
CUDA-based Parallel K-means Clustering Algorithm
摘要
Abstract
K-means clustering algorithm is an excellent algorithm which has been widely used in the image processing and data mining.However,the algorithm arouses a high computational complexity.This paper made a parallel analysis of K-means algorithm in detail,and proposed a partitioning and parallel K-means algorithm based on CUDA (Compute unified device architecture).In addition,some optimization strategies,e.g.,coalesced memory access,parallel reduction,load balance and instruction optimization,were discussed to obtain the higher performance.Experimental results show that the parallel K-means algorithm achieves 560x speedup over the sequential C codes,while maintains the same effect.Hence it solves the bottleneck of the algorithm perfectly,which is an attractive alternative to the sequential K-means algorithm for image segmentation and clustering analysis.关键词
图像分割/聚类分割算法/统一计算架构/图形处理器/并行优化Key words
Image segmentation / K-means clustering algorithm / CUDA/ GPU / Parallel optimization分类
信息技术与安全科学引用本文复制引用
霍迎秋,秦仁波,邢彩燕,陈曦,方勇..基于CUDA的并行K-means聚类图像分割算法优化[J].农业机械学报,2014,45(11):47-53,74,8.基金项目
国家自然科学基金资助项目(61271280)和国家级大学生科技创新重点资助项目(201310712068) （61271280）
