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Case study for running HPC applications in public clouds

Authors:

Tom McGlynnAuthors Info & Claims

HPDC '10: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

Pages 395 - 401

https://doi.org/10.1145/1851476.1851535

Published: 21 June 2010 Publication History

Abstract

Cloud computing is emerging as an alternative computing platform to bridge the gap between scientists' growing computational demands and their computing capabilities. A scientist who wants to run HPC applications can obtain massive computing resources 'in the cloud' quickly (in minutes), as opposed to days or weeks it normally takes under traditional business processes. Due to the popularity of Amazon EC2, most HPC-in-the-cloud research has been conducted using EC2 as a target platform. Previous work has not investigated how results might depend upon the cloud platform used. In this paper, we extend previous research to three public cloud computing platforms. In addition to running classical benchmarks, we also port a 'full-size' NASA climate prediction application into the cloud, and compare our results with that from dedicated HPC systems. Our results show that 1) virtualization technology, which is widely used by cloud computing, adds little performance overhead; 2) most current public clouds are not designed for running scientific applications primarily due to their poor networking capabilities. However, a cloud with moderately better network (vs. EC2) will deliver a significant performance improvement. Our observations will help to quantify the improvement of using fast networks for running HPC-in-the-cloud, and indicate a promising trend of HPC capability in future private science clouds. We also discuss techniques that will help scientists to best utilize public cloud platforms despite current deficiencies.

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Cited By

Logan LKougkas ASun X(2024)MegaMmap: Blurring the Boundary Between Memory and Storage for Data-Intensive WorkloadsSC24: International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC41406.2024.00114(1-18)Online publication date: 17-Nov-2024
https://doi.org/10.1109/SC41406.2024.00114
Dancheva TAlonso UBarton M(2023)Cloud benchmarking and performance analysis of an HPC application in Amazon EC2Cluster Computing10.1007/s10586-023-04060-427:2(2273-2290)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04060-4
Whitcomb TArevalo DMoskaitis JKomaromi WDoyle JHuang AWodka JNoon FMay MWilliamson FReinecke P(2022)Real-Time Probabilistic Tropical Cyclone Forecasting in the CloudBulletin of the American Meteorological Society10.1175/BAMS-D-21-0164.1103:8(E1930-E1946)Online publication date: Aug-2022
https://doi.org/10.1175/BAMS-D-21-0164.1
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cover image ACM Conferences

HPDC '10: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing

June 2010

911 pages

ISBN:9781605589428

DOI:10.1145/1851476

General Chairs:
Salim Hariri
University of Arizona
,
Kate Keahey
University of Chicago

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Arizona: University of Arizona
SIGARCH: ACM Special Interest Group on Computer Architecture

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 June 2010

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National Aeronautics and Space Administration

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HPDC '10

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University of Arizona
SIGARCH

HPDC '10: The 19th International Symposium on High Performance Distributed Computing

June 21 - 25, 2010

Illinois, Chicago

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Overall Acceptance Rate 166 of 966 submissions, 17%

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Cited By

Logan LKougkas ASun X(2024)MegaMmap: Blurring the Boundary Between Memory and Storage for Data-Intensive WorkloadsSC24: International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC41406.2024.00114(1-18)Online publication date: 17-Nov-2024
https://doi.org/10.1109/SC41406.2024.00114
Dancheva TAlonso UBarton M(2023)Cloud benchmarking and performance analysis of an HPC application in Amazon EC2Cluster Computing10.1007/s10586-023-04060-427:2(2273-2290)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04060-4
Whitcomb TArevalo DMoskaitis JKomaromi WDoyle JHuang AWodka JNoon FMay MWilliamson FReinecke P(2022)Real-Time Probabilistic Tropical Cyclone Forecasting in the CloudBulletin of the American Meteorological Society10.1175/BAMS-D-21-0164.1103:8(E1930-E1946)Online publication date: Aug-2022
https://doi.org/10.1175/BAMS-D-21-0164.1
Fan YLan ZRich PAllcock WPapka M(2022)Hybrid Workload Scheduling on HPC Systems2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS53621.2022.00052(470-480)Online publication date: May-2022
https://doi.org/10.1109/IPDPS53621.2022.00052
Kutzner CKniep CCherian ANordstrom LGrubmüller Hde Groot BGapsys V(2022)GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug DesignJournal of Chemical Information and Modeling10.1021/acs.jcim.2c0004462:7(1691-1711)Online publication date: 30-Mar-2022
https://doi.org/10.1021/acs.jcim.2c00044
Vivas ACastro H(2022)Quantitative Characterization of Scientific Computing ClustersHigh Performance Computing10.1007/978-3-031-23821-5_4(47-62)Online publication date: 21-Dec-2022
https://doi.org/10.1007/978-3-031-23821-5_4
Guidi GEllis MBuluç AYelick KCuller DBourcier JJiang ZBezemer CCortellessa V(2021)10 Years Later: Cloud Computing is Closing the Performance GapCompanion of the ACM/SPEC International Conference on Performance Engineering10.1145/3447545.3451183(41-48)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3447545.3451183
Hsu KChou J(2021)Performance benchmarking and auto-tuning for scientific applications on virtual clusterThe Journal of Supercomputing10.1007/s11227-021-04103-w78:5(6174-6206)Online publication date: 11-Oct-2021
https://doi.org/10.1007/s11227-021-04103-w
Chitre VShah D(2021)Novel Approach for Task Scheduling of HPC Applications Using SDNProceeding of First Doctoral Symposium on Natural Computing Research10.1007/978-981-33-4073-2_33(355-365)Online publication date: 19-Mar-2021
https://doi.org/10.1007/978-981-33-4073-2_33
Nowicki MGórski ŁBała P(2021)Performance Evaluation of Java/PCJ Implementation of Parallel Algorithms on the CloudEuro-Par 2020: Parallel Processing Workshops10.1007/978-3-030-71593-9_17(213-224)Online publication date: 14-Mar-2021
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