A visual analytics system for optimizing the performance of large-scale networks in supercomputing systems
作者机构：University of CaliforniaDavisUnited States Argonne National LaboratoryUnited States Rensselaer Polytechnic InstituteUnited States
出 版 物：《Visual Informatics》 (可视信息学（英文）)
年 卷 期：2018年第2卷第1期
页 面：98-110页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This research was sponsored by the Advanced Scientific Computing Research Program,the Office of Science,U.S Department of Energy through grants DE-SC0014917,DE-SC0012610,and DE-AC02-06CH11357
主 题：Supercomputing Parallel communication network Dragonfly networks Time-series data Performance analysis Visual analytics
摘 要：The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network *** of the state of the art supercomputers use networks based on the increasingly popular Dragonfly *** is crucial to study the behavior and performance of different parallel applications running on Dragonfly networks in order to make optimal system configurations and design choices,such as job scheduling and routing ***,in order to study these temporal network behavior,we would need a tool to analyze and correlate numerous sets of multivariate time-series data collected from the Dragonfly s multi-level *** paper presents such a tool-a visual analytics system-that uses the Dragonfly network to investigate the temporal behavior and optimize the communication performance of a *** coupled interactive visualization with time-series analysis methods to help reveal hidden patterns in the network behavior with respect to different parallel applications and system *** system also provides multiple coordinated views for connecting behaviors observed at different levels of the network hierarchies,which effectively helps visual analysis *** demonstrate the effectiveness of the system with a set of case *** system and findings can not only help improve the communication performance of supercomputing applications,but also the network performance of next-generation supercomputers.
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