Driving scientific applications by data in distributed environments

Joel Saltz; Umit Catalyurek; Tahsin Kurc; Mike Gray; Shannon Hastings; Steve Langella; Sivaramakrishnan Narayanan; Ryan Martino; Steven Bryant; Malgorzata Peszynska; Mary Wheeler; Alan Sussman; Michael Beynon; Christian Hansen; Don Stredney; Dennis Sessanna

doi:10.1007/3-540-44864-0_37

Driving scientific applications by data in distributed environments

Joel Saltz
, Umit Catalyurek
, Tahsin Kurc
, Mike Gray
, Shannon Hastings
, Steve Langella
, Sivaramakrishnan Narayanan
, Ryan Martino
, Steven Bryant
, Malgorzata Peszynska
, Mary Wheeler
, Alan Sussman
, Michael Beynon
, Christian Hansen
, Don Stredney
, Dennis Sessanna

Biomedical Informatics

Ohio State University
University of Texas at Austin
University of Maryland, College Park
Ohio Supercomputer Center

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

Abstract

Traditional simulation-based applications for exploring a parameter space to understand a physical phenomenon or to optimize a design are rapidly overwhelmed by data volume when large numbers of simulations of different parameters are carried out. Optimizing reservoir management through simulation-based studies, in which large numbers of realizations are sought using detailed geologic descriptions, is an example of such applications. In this paper, we describe a software architecture to facilitate large scale simulation studies, involving ensembles of long-running simulations and analysis of vast volumes of output data. This architecture is built on top of two frameworks we have developed: IPARS and DataCutter. These frameworks make it possible to implement tools and applications to run large-scale simulations, and generate and investigate terabyte-scale datasets efficiently.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Peter M. A. Sloot, David Abramson, Alexander V. Bogdanov, Yuriy E. Gorbachev, Jack J. Dongarra, Albert Y. Zomaya
Publisher	Springer Verlag
Pages	355-364
Number of pages	10
ISBN (Print)	3540401970, 9783540401971
DOIs	https://doi.org/10.1007/3-540-44864-0_37
State	Published - 2003

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2660
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/3-540-44864-0_37

Cite this

Saltz, J., Catalyurek, U., Kurc, T., Gray, M., Hastings, S., Langella, S., Narayanan, S., Martino, R., Bryant, S., Peszynska, M., Wheeler, M., Sussman, A., Beynon, M., Hansen, C., Stredney, D., & Sessanna, D. (2003). Driving scientific applications by data in distributed environments. In P. M. A. Sloot, D. Abramson, A. V. Bogdanov, Y. E. Gorbachev, J. J. Dongarra, & A. Y. Zomaya (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 355-364). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2660). Springer Verlag. https://doi.org/10.1007/3-540-44864-0_37

Saltz, Joel ; Catalyurek, Umit ; Kurc, Tahsin et al. / Driving scientific applications by data in distributed environments. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Peter M. A. Sloot ; David Abramson ; Alexander V. Bogdanov ; Yuriy E. Gorbachev ; Jack J. Dongarra ; Albert Y. Zomaya. Springer Verlag, 2003. pp. 355-364 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Driving scientific applications by data in distributed environments",

abstract = "Traditional simulation-based applications for exploring a parameter space to understand a physical phenomenon or to optimize a design are rapidly overwhelmed by data volume when large numbers of simulations of different parameters are carried out. Optimizing reservoir management through simulation-based studies, in which large numbers of realizations are sought using detailed geologic descriptions, is an example of such applications. In this paper, we describe a software architecture to facilitate large scale simulation studies, involving ensembles of long-running simulations and analysis of vast volumes of output data. This architecture is built on top of two frameworks we have developed: IPARS and DataCutter. These frameworks make it possible to implement tools and applications to run large-scale simulations, and generate and investigate terabyte-scale datasets efficiently.",

author = "Joel Saltz and Umit Catalyurek and Tahsin Kurc and Mike Gray and Shannon Hastings and Steve Langella and Sivaramakrishnan Narayanan and Ryan Martino and Steven Bryant and Malgorzata Peszynska and Mary Wheeler and Alan Sussman and Michael Beynon and Christian Hansen and Don Stredney and Dennis Sessanna",

year = "2003",

doi = "10.1007/3-540-44864-0\_37",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Saltz, J, Catalyurek, U, Kurc, T, Gray, M, Hastings, S, Langella, S, Narayanan, S, Martino, R, Bryant, S, Peszynska, M, Wheeler, M, Sussman, A, Beynon, M, Hansen, C, Stredney, D & Sessanna, D 2003, Driving scientific applications by data in distributed environments. in PMA Sloot, D Abramson, AV Bogdanov, YE Gorbachev, JJ Dongarra & AY Zomaya (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2660, Springer Verlag, pp. 355-364. https://doi.org/10.1007/3-540-44864-0_37

Driving scientific applications by data in distributed environments. / Saltz, Joel; Catalyurek, Umit; Kurc, Tahsin et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Peter M. A. Sloot; David Abramson; Alexander V. Bogdanov; Yuriy E. Gorbachev; Jack J. Dongarra; Albert Y. Zomaya. Springer Verlag, 2003. p. 355-364 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2660).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Driving scientific applications by data in distributed environments

AU - Saltz, Joel

AU - Catalyurek, Umit

AU - Kurc, Tahsin

AU - Gray, Mike

AU - Hastings, Shannon

AU - Langella, Steve

AU - Narayanan, Sivaramakrishnan

AU - Martino, Ryan

AU - Bryant, Steven

AU - Peszynska, Malgorzata

AU - Wheeler, Mary

AU - Sussman, Alan

AU - Beynon, Michael

AU - Hansen, Christian

AU - Stredney, Don

AU - Sessanna, Dennis

PY - 2003

Y1 - 2003

N2 - Traditional simulation-based applications for exploring a parameter space to understand a physical phenomenon or to optimize a design are rapidly overwhelmed by data volume when large numbers of simulations of different parameters are carried out. Optimizing reservoir management through simulation-based studies, in which large numbers of realizations are sought using detailed geologic descriptions, is an example of such applications. In this paper, we describe a software architecture to facilitate large scale simulation studies, involving ensembles of long-running simulations and analysis of vast volumes of output data. This architecture is built on top of two frameworks we have developed: IPARS and DataCutter. These frameworks make it possible to implement tools and applications to run large-scale simulations, and generate and investigate terabyte-scale datasets efficiently.

AB - Traditional simulation-based applications for exploring a parameter space to understand a physical phenomenon or to optimize a design are rapidly overwhelmed by data volume when large numbers of simulations of different parameters are carried out. Optimizing reservoir management through simulation-based studies, in which large numbers of realizations are sought using detailed geologic descriptions, is an example of such applications. In this paper, we describe a software architecture to facilitate large scale simulation studies, involving ensembles of long-running simulations and analysis of vast volumes of output data. This architecture is built on top of two frameworks we have developed: IPARS and DataCutter. These frameworks make it possible to implement tools and applications to run large-scale simulations, and generate and investigate terabyte-scale datasets efficiently.

UR - https://www.scopus.com/pages/publications/35248855982

U2 - 10.1007/3-540-44864-0_37

DO - 10.1007/3-540-44864-0_37

M3 - Chapter

AN - SCOPUS:35248855982

SN - 3540401970

SN - 9783540401971

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 355

EP - 364

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Sloot, Peter M. A.

A2 - Abramson, David

A2 - Bogdanov, Alexander V.

A2 - Gorbachev, Yuriy E.

A2 - Dongarra, Jack J.

A2 - Zomaya, Albert Y.

PB - Springer Verlag

ER -

Saltz J, Catalyurek U, Kurc T, Gray M, Hastings S, Langella S et al. Driving scientific applications by data in distributed environments. In Sloot PMA, Abramson D, Bogdanov AV, Gorbachev YE, Dongarra JJ, Zomaya AY, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2003. p. 355-364. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-44864-0_37

Driving scientific applications by data in distributed environments

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