paraSNF:An Parallel Approach for Large-Scale Similarity Network Fusion

摘要：

　　With the rapid accumulation of multi-dimensional disease data,the integration of multiple similarity networks is essential for understanding the development of diseases and identifying subtypes of diseases.The recent computational efficient method named SNF is suitable for the integration of similarity networks and has been extensively applied to the bioinformatics analysis.However,the computational complexity and space complexity of the SNF method increases with the increase of the sample numbers.In this research,we develop a parallel SNF algorithm named paraSNF to improve the speed and scalability of the SNF.The experimental results on two large-scale simulation datasets reveal that the paraSNF algorithm is 30x–100x faster than the serial SNF.And the speedup of the paraSNF over the SNF which running on multi-cores with multi-threads is 8x–15x.Furthermore,more than 60%memory space are saved using paraSNF,which can greatly improve the scalability of the SNF.

关键词： Similarity network fusion Data integration Parallel SNF Multi-core CPU Compressed storage

作者: Xiaolong Shen Song He Minquan Fang Yuqi Wen Xiaochen Bo Yong Dou

作者单位: College of Computer,Nationality University of Defense Technology,Changsha,China;Science and Technolo Department of Biotechnology,Beijing Institute of Radiation Medicine,Beijing 100850,China Huawei Technologies Co.,Ltd.,Hangzhou,China College of Computer,Nationality University of Defense Technology,Changsha,China

会议类型: 国际会议

会议名称: the 12th Conference on Advanced Computer Architecture?(ACA 2018)(2018年全国计算机体系结构学术年会)

会议地点: 辽宁营口

会议语种:英文

页码: 155-167

在线出版日期: 2018-08-10（万方平台首次上网日期，不代表论文的发表时间）