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research-article

PANORAMA

Authors:

Christopher Carothers,

Anirban Mandal,

Jeffrey S Vetter,

Claris Castillo,

Jeremy Meredith,

Thomas Proffen,

Rafael Ferreira da SilvaAuthors Info & Claims

International Journal of High Performance Computing Applications, Volume 31, Issue 1

Pages 4 - 18

https://doi.org/10.1177/1094342015594515

Published: 01 January 2017 Publication History

Abstract

Computational science is well established as the third pillar of scientific discovery and is on par with experimentation and theory. However, as we move closer toward the ability to execute exascale calculations and process the ensuing extreme-scale amounts of data produced by both experiments and computations alike, the complexity of managing the compute and data analysis tasks has grown beyond the capabilities of domain scientists. Thus, workflow management systems are absolutely necessary to ensure current and future scientific discoveries. A key research question for these workflow management systems concerns the performance optimization of complex calculation and data analysis tasks. The central contribution of this article is a description of the PANORAMA approach for modeling and diagnosing the run-time performance of complex scientific workflows. This approach integrates extreme-scale systems testbed experimentation, structured analytical modeling, and parallel systems simulation into a comprehensive workflow framework called Pegasus for understanding and improving the overall performance of complex scientific workflows.

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Cited By

Dongarra JTourancheau BJin HRaghavan KPapadimitriou GWang CMandal AKiran MDeelman EBalaprakash P(2023)Graph neural networks for detecting anomalies in scientific workflowsInternational Journal of High Performance Computing Applications10.1177/1094342023117214037:3-4(394-411)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1177/10943420231172140
Liu ZKettimuthu RChung JAnanthakrishnan RLink MFoster I(2021)Design and Evaluation of a Simple Data Interface for Efficient Data Transfer across Diverse StorageACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/34520076:1(1-25)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3452007
Roy RPatel TTiwari DMartínez JDuato JJohn L(2021)SatoriProceedings of the 48th Annual International Symposium on Computer Architecture10.1109/ISCA52012.2021.00031(292-305)Online publication date: 14-Jun-2021
https://dl.acm.org/doi/10.1109/ISCA52012.2021.00031
Show More Cited By

PANORAMA

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cover image International Journal of High Performance Computing Applications

International Journal of High Performance Computing Applications Volume 31, Issue 1

1 2017

111 pages

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Copyright © © The Authors 2015.

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Sage Publications, Inc.

United States

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Published: 01 January 2017

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Dongarra JTourancheau BJin HRaghavan KPapadimitriou GWang CMandal AKiran MDeelman EBalaprakash P(2023)Graph neural networks for detecting anomalies in scientific workflowsInternational Journal of High Performance Computing Applications10.1177/1094342023117214037:3-4(394-411)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1177/10943420231172140
Liu ZKettimuthu RChung JAnanthakrishnan RLink MFoster I(2021)Design and Evaluation of a Simple Data Interface for Efficient Data Transfer across Diverse StorageACM Transactions on Modeling and Performance Evaluation of Computing Systems10.1145/34520076:1(1-25)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3452007
Roy RPatel TTiwari DMartínez JDuato JJohn L(2021)SatoriProceedings of the 48th Annual International Symposium on Computer Architecture10.1109/ISCA52012.2021.00031(292-305)Online publication date: 14-Jun-2021
https://dl.acm.org/doi/10.1109/ISCA52012.2021.00031
Dongarra JTourancheau BDeelman EMandal AJiang MSakellariou R(2019)The role of machine learning in scientific workflowsInternational Journal of High Performance Computing Applications10.1177/109434201985212733:6(1128-1139)Online publication date: 1-Nov-2019
https://dl.acm.org/doi/10.1177/1094342019852127
Buyya RSrirama SCasale GCalheiros RSimmhan YVarghese BGelenbe EJavadi BVaquero LNetto MToosi ARodriguez MLlorente IVimercati SSamarati PMilojicic DVarela CBahsoon RAssuncao MRana OZhou WJin HGentzsch WZomaya AShen H(2018)A Manifesto for Future Generation Cloud ComputingACM Computing Surveys10.1145/324173751:5(1-38)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3241737

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