Treffer: High-performance computing tools for the integrated assessment and modelling of social―ecological systems

Title:
High-performance computing tools for the integrated assessment and modelling of social―ecological systems
Authors:
BRYAN, Brett A
Source:
Thematic Issue on the Future of Integrated Modeling Science and TechnologyEnvironmental modelling & software. 39:295-303
Publisher Information:
Oxford: Elsevier, 2013.
Publication Year:
2013
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Ecology, Ecologie, Environment, Environnement, Computer science, Informatique, Pollution, Sciences biologiques et medicales, Biological and medical sciences, Sciences biologiques fondamentales et appliquees. Psychologie, Fundamental and applied biological sciences. Psychology, Ecologie animale, vegetale et microbienne, Animal, plant and microbial ecology, Généralités. Techniques, General aspects. Techniques, Méthodes et techniques (échantillonnage, marquage, piégeage, modélisation...), Methods and techniques (sampling, tagging, trapping, modelling...), Modélisation, Modeling, Modelización, Performance, Rendimiento, Représentation graphique, Graphics, Grafo (curva), Système information géographique, Geographic information system, Sistema información geográfica, Cluster, Concurrency, Environmental, GIS, Global challenges, Graphics processing unit (GPU), Grid, Multi-core, Parallel programming
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
CSIRO Ecosystem Sciences, Waite Campus, Urrbrae, SA 5064, Australia
ISSN:
1364-8152
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=27188440
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Animal, vegetal and microbial ecology
Accession Number:
edscal.27188440
Database:
PASCAL Archive

Weitere Informationen

Integrated spatio-temporal assessment and modelling of complex social-ecological systems is required to address global environmental challenges. However, the computational demands of this modelling are unlikely to be met by traditional Geographic Information System (GIS) tools anytime soon. I evaluated the potential of a range of high-performance computing (HPC) hardware and software tools to overcome these computational barriers. Performance advantages were quantified using a synthetic model. Four tests were compared, using: a) an Arc Macro Language (AML) GIS script on a single central processing unit (CPU); b) Python/NumPy on 1―256 CPU cores; c) Python/NumPy on 1―64 graphics processing units (GPUs) with high-level PyCUDA abstraction (GPUArray); and d) Python/NumPy on 1―64 GPUs with low-level PyCUDA abstraction (ElementwiseKernel). The GIS implementation effectively took 15.5 weeks to run. Python/NumPy on a single CPU core led to a speed-up of 59 × compared to the GIS. On a single GPU, speed-ups of 1473× were achieved using GPUArray and 4881 x using ElementwiseKernel. Parallel processing led to further performance enhancements. At best, the ElementwiseKernel module in parallel over 64 GPUs achieved a speed-up of 63,643×. Open source tools such as Python applied across a spectrum of HPC resources offer transformational and accessible performance improvements for integrated assessment and modelling. By reducing the computational barrier, HPC can lead to a step change in modelling sophistication, including the better representation of uncertainty, and perhaps even new modelling paradigms. However, migration to new hardware and software environments also has significant costs. Costs and benefits of HPC are discussed and code tools are provided to help others migrate to HPC and transform our ability to address global challenges through integrated assessment and modelling.