Parallel and distributed computation in design flood estimation

Brady, PDM; Ball, JE

Parallel and distributed computation in design flood estimation

Brady, PDM

Ball, JE

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Publication Type:: Conference Proceeding
Citation:: The Art and Science of Water - 36th Hydrology and Water Resources Symposium, HWRS 2015, 2015, pp. 1538 - 1544
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Brady, PDM https://orcid.org/0000-0002-6052-8777	en_US
dc.contributor.author	Ball, JE https://orcid.org/0000-0001-8342-1916	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	The Art and Science of Water - 36th Hydrology and Water Resources Symposium, HWRS 2015, 2015, pp. 1538 - 1544	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136415
dc.description.abstract	© 2015, Engineers Australia. All rights reserved. Reliable and efficient design flood estimation remains a concern for many catchment managers. The search for reliable and efficient approaches to design flood estimation together with the increased computational capacity available to analysts has resulted in the development of computationally intensive methods for design flood estimation; for example, the application of a Genetic Algorithm for calibration of a catchment modeling system and the use of a Monte Carlo technique for generation of a POT series requires multiple executions of the catchment modeling system. As the execution of a given simulation is entirely separate from others, the computation step in the method is embarrassingly parallel. The key step to reduce computational run times, therefore, is to efficiently distribute, compute and gather the results amongst a cluster of computers or processing cores. Within this paper we present two methods of efficiently distributing the individuals such that they are computed in parallel. The example application is the use of a Genetic Algorithm for calibration of SWMM applied to an urban catchment. The first method is based on the recognition that SWMM 4.4 is a single threaded application. We can, therefore, place a wrapper that is multi-threaded in front of the SWMM execution step and compute the population in parallel on a multi-core machine. With this wrapper we achieved linear speed up to three cores, which peaked at a 5.5 times speed up on 12 core machine. The second method builds on the first and is based on the BOINC framework. Implementing a distributed methodology to deploy the multi-core programme across a cluster of shared workstations using the BOINC middleware, we were able scavenge 20-30% of the computational resources of a 100+ node cluster with over 1000 cores.	en_US
dc.relation.ispartof	The Art and Science of Water - 36th Hydrology and Water Resources Symposium, HWRS 2015	en_US
dc.title	Parallel and distributed computation in design flood estimation	en_US
dc.type	Conference Proceeding
utslib.for	040608 Surfacewater Hydrology	en_US
utslib.for	090509 Water Resources Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2015, Engineers Australia. All rights reserved. Reliable and efficient design flood estimation remains a concern for many catchment managers. The search for reliable and efficient approaches to design flood estimation together with the increased computational capacity available to analysts has resulted in the development of computationally intensive methods for design flood estimation; for example, the application of a Genetic Algorithm for calibration of a catchment modeling system and the use of a Monte Carlo technique for generation of a POT series requires multiple executions of the catchment modeling system. As the execution of a given simulation is entirely separate from others, the computation step in the method is embarrassingly parallel. The key step to reduce computational run times, therefore, is to efficiently distribute, compute and gather the results amongst a cluster of computers or processing cores. Within this paper we present two methods of efficiently distributing the individuals such that they are computed in parallel. The example application is the use of a Genetic Algorithm for calibration of SWMM applied to an urban catchment. The first method is based on the recognition that SWMM 4.4 is a single threaded application. We can, therefore, place a wrapper that is multi-threaded in front of the SWMM execution step and compute the population in parallel on a multi-core machine. With this wrapper we achieved linear speed up to three cores, which peaked at a 5.5 times speed up on 12 core machine. The second method builds on the first and is based on the BOINC framework. Implementing a distributed methodology to deploy the multi-core programme across a cluster of shared workstations using the BOINC middleware, we were able scavenge 20-30% of the computational resources of a 100+ node cluster with over 1000 cores.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/136415