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A visual analytics system for optimizing the performance of large-scale networks in supercomputing systems

Visual Informatics 2018年第1期2卷 98-110页

作者： Takanori Fujiwara Jianping Kelvin Li Misbah Mubarak Caitlin Ross Christopher D.Carothers Robert B.Ross Kwan-Liu Ma University of California DavisUnited States Argonne National Laboratory United States Rensselaer Polytechnic Institute United States

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.

关键词： Supercomputing Parallel communication network dragonfly networks Time-series data Performance analysis Visual analytics

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