计算机科学 ›› 2017, Vol. 44 ›› Issue (3): 51-54.doi: 10.11896/j.issn.1002-137X.2017.03.013
唐兵,Laurent BOBELIN,贺海武
TANG Bing, Laurent BOBELIN and HE Hai-wu
摘要： 非负矩阵分解(NMF)作为一种数据降维和特征提取的有效工具,已经在文本聚类、推荐系统等多个领域得到应用,但是其计算过程比较复杂。对此,提出一种基于MPI+OpenMP的混合层次化并行NMF方法,其充分利用基于MPI的消息传递模型和基于OpenMP的共享存储模型各自的优势,并基于多核节点集群进行测试。实验结果表明,所设计的并行NMF算法达到了较高的加速比,能有效处理高阶矩阵的非负分解,极大地提高了计算的效率。
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