Porting a process-based crop model to a high-performance computing environment for plant simulation

Zhao, G; Song, X; Yan, C; Yu, Q

Porting a process-based crop model to a high-performance computing environment for plant simulation

Zhao, G Song, X Yan, C Yu, Q

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, PMA 2012, 2012, pp. 462 - 465
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Zhao, G	en_US
dc.contributor.author	Song, X	en_US
dc.contributor.author	Yan, C	en_US
dc.contributor.author	Yu, Q https://orcid.org/0000-0001-6950-1821	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings - 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, PMA 2012, 2012, pp. 462 - 465	en_US
dc.identifier.isbn	9781467300681	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120906
dc.description.abstract	Increasing concerns about food security have stimulated integrated assessment of the sustainability of agricultural systems at regional, national and global scales with high-resolution. Traditionally, the process-based agricultural models are designed for field scale studies that obtain inputs, run the simulations and provide outputs through the graphic interface. The graphic interface based model dose not suit for modelling practices requiring a large number of simulations. Here, we developed a high performance approach which concurrently executed the Agricultural Production Systems sIMulator (APSIM) simulations using parallel programming techniques. In this approach, an APSIM simulation template with replaceable parameters was firstly designed, and new simulations based on the template was then constructed by dynamically replacing parameters of climate, soil and management options. We parallelized the batched running method in a shared-memory multiprocessor system using Python's Multiprocessing module. We tested the approach with a case study that simulated the productivity of continuous wheat cropping system during 20 years period along the Australian cereal-growing regions under management practices of 5 levels nitrogen application and 3 stubble management practices. More than 170 K runs were finished in 43h by using 64 workers, achieved a speedup ratio of 60. The parallelized method proposed in this study makes large-scale and high-resolution agricultural systems assessment possible. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications, PMA 2012	en_US
dc.relation.isbasedon	10.1109/PMA.2012.6524873	en_US
dc.title	Porting a process-based crop model to a high-performance computing environment for plant simulation	en_US
dc.type	Conference Proceeding
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0701 Agriculture, Land and Farm Management	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 Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Increasing concerns about food security have stimulated integrated assessment of the sustainability of agricultural systems at regional, national and global scales with high-resolution. Traditionally, the process-based agricultural models are designed for field scale studies that obtain inputs, run the simulations and provide outputs through the graphic interface. The graphic interface based model dose not suit for modelling practices requiring a large number of simulations. Here, we developed a high performance approach which concurrently executed the Agricultural Production Systems sIMulator (APSIM) simulations using parallel programming techniques. In this approach, an APSIM simulation template with replaceable parameters was firstly designed, and new simulations based on the template was then constructed by dynamically replacing parameters of climate, soil and management options. We parallelized the batched running method in a shared-memory multiprocessor system using Python's Multiprocessing module. We tested the approach with a case study that simulated the productivity of continuous wheat cropping system during 20 years period along the Australian cereal-growing regions under management practices of 5 levels nitrogen application and 3 stubble management practices. More than 170 K runs were finished in 43h by using 64 workers, achieved a speedup ratio of 60. The parallelized method proposed in this study makes large-scale and high-resolution agricultural systems assessment possible. © 2012 IEEE.

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http://hdl.handle.net/10453/120906