Mirage: A framework for data-driven collaborative high-resolution simulation

Byung H. Park; Melissa R. Allen; Devin White; Eric Weber; John T. Murphy; Michael J. North; Pam Sydelko

doi:10.1007/978-3-319-22786-3_35

Mirage: A framework for data-driven collaborative high-resolution simulation

Byung H. Park
, Melissa R. Allen
, Devin White
, Eric Weber
, John T. Murphy
, Michael J. North
, Pam Sydelko

Performance Engineering

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

1 Scopus citations

Abstract

Information about how human populations shift in response to various stimuli is limited because no single model is capable of addressing these stimuli simultaneously, and integration of the best existing models has been challenging because of the vast disparity among constituent model purposes, architectures, scales, and execution environments. To demonstrate a potential model coupling for approaching this problem, three major model components are integrated into a fully coupled system that executes a worldwide infection-infected routine where a human population requires a food source for sustenance and an infected population can spread an infection when it is in contact with the remaining healthy population. To enable high-resolution data-driven model federation and an ability to capture dynamics and behaviors of billions of humans, a high-performance computing agent-based framework has been created and is demonstrated in this chapter.

Original language	English
Title of host publication	Advances in Geocomputation - Geocomputation 2015—The 13th International Conference
Editors	Daniel A. Griffith, Yongwan Chun, Denis J. Dean
Publisher	Springer Heidelberg
Pages	395-403
Number of pages	9
ISBN (Print)	9783319227856
DOIs	https://doi.org/10.1007/978-3-319-22786-3_35
State	Published - 2017
Event	13th International Conference on Advances in Geocomputation, Geocomputation 2015 - Dallas, United States Duration: May 20 2015 → May 23 2015

Publication series

Name	Advances in Geographic Information Science
Volume	0
ISSN (Print)	1867-2434
ISSN (Electronic)	1867-2442

Conference

Conference	13th International Conference on Advances in Geocomputation, Geocomputation 2015
Country/Territory	United States
City	Dallas
Period	05/20/15 → 05/23/15

Funding

This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. This manuscript also has been authored in part by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a US Department of Energy Office of Science Laboratory, is operated under contract number DE-AC02-06CH11357. The United States Government retains, and the publisher, by accepting the article for publication, acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The work used the Oak Ridge Leadership Computing Facility.

Keywords

Agent-based
High-performance computing
Model coupling

Access to Document

10.1007/978-3-319-22786-3_35

Cite this

Park, B. H., Allen, M. R., White, D., Weber, E., Murphy, J. T., North, M. J., & Sydelko, P. (2017). Mirage: A framework for data-driven collaborative high-resolution simulation. In D. A. Griffith, Y. Chun, & D. J. Dean (Eds.), Advances in Geocomputation - Geocomputation 2015—The 13th International Conference (pp. 395-403). (Advances in Geographic Information Science; Vol. 0). Springer Heidelberg. https://doi.org/10.1007/978-3-319-22786-3_35

@inproceedings{66b9b9a1481e4966ad064e67e18e71bf,

title = "Mirage: A framework for data-driven collaborative high-resolution simulation",

abstract = "Information about how human populations shift in response to various stimuli is limited because no single model is capable of addressing these stimuli simultaneously, and integration of the best existing models has been challenging because of the vast disparity among constituent model purposes, architectures, scales, and execution environments. To demonstrate a potential model coupling for approaching this problem, three major model components are integrated into a fully coupled system that executes a worldwide infection-infected routine where a human population requires a food source for sustenance and an infected population can spread an infection when it is in contact with the remaining healthy population. To enable high-resolution data-driven model federation and an ability to capture dynamics and behaviors of billions of humans, a high-performance computing agent-based framework has been created and is demonstrated in this chapter.",

keywords = "Agent-based, High-performance computing, Model coupling",

author = "Park, \{Byung H.\} and Allen, \{Melissa R.\} and Devin White and Eric Weber and Murphy, \{John T.\} and North, \{Michael J.\} and Pam Sydelko",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2017.; 13th International Conference on Advances in Geocomputation, Geocomputation 2015 ; Conference date: 20-05-2015 Through 23-05-2015",

year = "2017",

doi = "10.1007/978-3-319-22786-3\_35",

language = "English",

isbn = "9783319227856",

series = "Advances in Geographic Information Science",

publisher = "Springer Heidelberg",

pages = "395--403",

editor = "Griffith, \{Daniel A.\} and Yongwan Chun and Dean, \{Denis J.\}",

booktitle = "Advances in Geocomputation - Geocomputation 2015—The 13th International Conference",

}

Park, BH , Allen, MR, White, D, Weber, E, Murphy, JT, North, MJ & Sydelko, P 2017, Mirage: A framework for data-driven collaborative high-resolution simulation. in DA Griffith, Y Chun & DJ Dean (eds), Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. Advances in Geographic Information Science, vol. 0, Springer Heidelberg, pp. 395-403, 13th International Conference on Advances in Geocomputation, Geocomputation 2015, Dallas, United States, 05/20/15. https://doi.org/10.1007/978-3-319-22786-3_35

Mirage: A framework for data-driven collaborative high-resolution simulation. / Park, Byung H.; Allen, Melissa R.; White, Devin et al.
Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. ed. / Daniel A. Griffith; Yongwan Chun; Denis J. Dean. Springer Heidelberg, 2017. p. 395-403 (Advances in Geographic Information Science; Vol. 0).

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

TY - GEN

T1 - Mirage

T2 - 13th International Conference on Advances in Geocomputation, Geocomputation 2015

AU - Park, Byung H.

AU - Allen, Melissa R.

AU - White, Devin

AU - Weber, Eric

AU - Murphy, John T.

AU - North, Michael J.

AU - Sydelko, Pam

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2017.

PY - 2017

Y1 - 2017

N2 - Information about how human populations shift in response to various stimuli is limited because no single model is capable of addressing these stimuli simultaneously, and integration of the best existing models has been challenging because of the vast disparity among constituent model purposes, architectures, scales, and execution environments. To demonstrate a potential model coupling for approaching this problem, three major model components are integrated into a fully coupled system that executes a worldwide infection-infected routine where a human population requires a food source for sustenance and an infected population can spread an infection when it is in contact with the remaining healthy population. To enable high-resolution data-driven model federation and an ability to capture dynamics and behaviors of billions of humans, a high-performance computing agent-based framework has been created and is demonstrated in this chapter.

AB - Information about how human populations shift in response to various stimuli is limited because no single model is capable of addressing these stimuli simultaneously, and integration of the best existing models has been challenging because of the vast disparity among constituent model purposes, architectures, scales, and execution environments. To demonstrate a potential model coupling for approaching this problem, three major model components are integrated into a fully coupled system that executes a worldwide infection-infected routine where a human population requires a food source for sustenance and an infected population can spread an infection when it is in contact with the remaining healthy population. To enable high-resolution data-driven model federation and an ability to capture dynamics and behaviors of billions of humans, a high-performance computing agent-based framework has been created and is demonstrated in this chapter.

KW - Agent-based

KW - High-performance computing

KW - Model coupling

UR - https://www.scopus.com/pages/publications/85010047106

U2 - 10.1007/978-3-319-22786-3_35

DO - 10.1007/978-3-319-22786-3_35

M3 - Conference contribution

AN - SCOPUS:85010047106

SN - 9783319227856

T3 - Advances in Geographic Information Science

SP - 395

EP - 403

BT - Advances in Geocomputation - Geocomputation 2015—The 13th International Conference

A2 - Griffith, Daniel A.

A2 - Chun, Yongwan

A2 - Dean, Denis J.

PB - Springer Heidelberg

Y2 - 20 May 2015 through 23 May 2015

ER -

Park BH , Allen MR, White D, Weber E, Murphy JT, North MJ et al. Mirage: A framework for data-driven collaborative high-resolution simulation. In Griffith DA, Chun Y, Dean DJ, editors, Advances in Geocomputation - Geocomputation 2015—The 13th International Conference. Springer Heidelberg. 2017. p. 395-403. (Advances in Geographic Information Science). doi: 10.1007/978-3-319-22786-3_35

Mirage: A framework for data-driven collaborative high-resolution simulation

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this