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

Abstract

The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network interconnects. Several of the state of the art supercomputers use networks based on the increasingly popular Dragonfly topology. It 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 strategies. However, 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 hierarchies. This 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 supercomputer. We 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 configurations. Our system also provides multiple coordinated views for connecting behaviors observed at different levels of the network hierarchies, which effectively helps visual analysis tasks. We demonstrate the effectiveness of the system with a set of case studies. Our system and findings can not only help improve the communication performance of supercomputingmore » applications, but also the network performance of next-generation supercomputers.« less

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Science Foundation (NSF); USDOE Office of Science - Office of Advanced Scientific Computing Research
OSTI Identifier:
1494291
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Conference: 11th IEEE Pacific Visualization Symposium, 04/10/18 - 04/13/18, Kobe, JP
Country of Publication:
United States
Language:
English
Subject:
Dragonfly networks; Parallel communication network; Performance analysis; Supercomputing; Time-series data; Visual analytics

Citation Formats

Fujiwara, Takanori, Li, Jianping Kelvin, Mubarak, Misbah, Ross, Caitlin, Carothers, Christopher, Ross, Robert B., and Ma, Kwan-Liu. A visual analytics system for optimizing the performance of large-scale networks in supercomputing systems. United States: N. p., 2018. Web. doi:10.1016/j.visinf.2018.04.010.
Fujiwara, Takanori, Li, Jianping Kelvin, Mubarak, Misbah, Ross, Caitlin, Carothers, Christopher, Ross, Robert B., & Ma, Kwan-Liu. A visual analytics system for optimizing the performance of large-scale networks in supercomputing systems. United States. doi:10.1016/j.visinf.2018.04.010.
Fujiwara, Takanori, Li, Jianping Kelvin, Mubarak, Misbah, Ross, Caitlin, Carothers, Christopher, Ross, Robert B., and Ma, Kwan-Liu. Thu . "A visual analytics system for optimizing the performance of large-scale networks in supercomputing systems". United States. doi:10.1016/j.visinf.2018.04.010. https://www.osti.gov/servlets/purl/1494291.
@article{osti_1494291,
title = {A visual analytics system for optimizing the performance of large-scale networks in supercomputing systems},
author = {Fujiwara, Takanori and Li, Jianping Kelvin and Mubarak, Misbah and Ross, Caitlin and Carothers, Christopher and Ross, Robert B. and Ma, Kwan-Liu},
abstractNote = {The overall efficiency of an extreme-scale supercomputer largely relies on the performance of its network interconnects. Several of the state of the art supercomputers use networks based on the increasingly popular Dragonfly topology. It 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 strategies. However, 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 hierarchies. This 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 supercomputer. We 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 configurations. Our system also provides multiple coordinated views for connecting behaviors observed at different levels of the network hierarchies, which effectively helps visual analysis tasks. We demonstrate the effectiveness of the system with a set of case studies. Our system and findings can not only help improve the communication performance of supercomputing applications, but also the network performance of next-generation supercomputers.},
doi = {10.1016/j.visinf.2018.04.010},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {3}
}

Conference:
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