Opportunities for cost savings with in-transit visualization

James Kress, Matthew Larsen, Jong Choi, Mark Kim, Matthew Wolf, Norbert Podhorszki, Scott Klasky, Hank Childs, David Pugmire

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

We analyze the opportunities for in-transit visualization to provide cost savings compared to in-line visualization. We begin by developing a cost model that includes factors related to both in-line and in-transit which allows comparisons to be made between the two methods. We then run a series of studies to create a corpus of data for our model. We run two different visualization algorithms, one that is computation heavy and one that is communication heavy with concurrencies up to 32, 768 cores. Our primary results are in exploring the cost model within the context of our corpus. Our findings show that in-transit consistently achieves significant cost efficiencies by running visualization algorithms at lower concurrency, and that in many cases these efficiencies are enough to offset other costs (transfer, blocking, and additional nodes) to be cost effective overall. Finally, this work informs future studies, which can focus on choosing ideal configurations for in-transit processing that can consistently achieve cost efficiencies.

Original languageEnglish
Title of host publicationHigh Performance Computing - 35th International Conference, ISC High Performance 2020, Proceedings
EditorsPonnuswamy Sadayappan, Bradford L. Chamberlain, Guido Juckeland, Hatem Ltaief
PublisherSpringer
Pages146-165
Number of pages20
ISBN (Print)9783030507428
DOIs
StatePublished - 2020
Event35th International Conference on High Performance Computing, ISC High Performance 2020 - Frankfurt, Germany
Duration: Jun 22 2020Jun 25 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12151 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference35th International Conference on High Performance Computing, ISC High Performance 2020
Country/TerritoryGermany
CityFrankfurt
Period06/22/2006/25/20

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/ downloads/doe-public-access-plan). This work was partially performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 (LLNL-CONF-805283).

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