HiperVR: A Virtual Reality model for Visualizing Multidimensional Health Status of High Performance Computing Systems

Ngan Nguyen, Lino Virgen, Tommy Dang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper proposes a virtual reality model for situational awareness and health monitoring of high-performance computing systems. The virtual reality model takes into account the following design requirements: 1) Mimicking data center spatial layout, 2) Tracking temporal changes, and 3) Monitoring multidimensional health statuses such as temperature, CPU load, memory usage, fan speed, and power consumption. To show the effectiveness of our design, we demonstrate the developed virtual reality model on a medium-scale high-performance computing system of 10 racks and 467 hosts at a university. The work was developed using weekly feedback from both industrial and academic domain experts.

Original languageEnglish
Title of host publicationProceedings of the Practice and Experience in Advanced Research Computing
Subtitle of host publicationRise of the Machines (Learning), PEARC 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450372275
DOIs
StatePublished - Jul 28 2019
Event2019 Conference on Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019 - Chicago, United States
Duration: Jul 28 2019Aug 1 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2019 Conference on Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019
CountryUnited States
CityChicago
Period07/28/1908/1/19

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    Nguyen, N., Virgen, L., & Dang, T. (2019). HiperVR: A Virtual Reality model for Visualizing Multidimensional Health Status of High Performance Computing Systems. In Proceedings of the Practice and Experience in Advanced Research Computing: Rise of the Machines (Learning), PEARC 2019 [3337958] (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3332186.3337958