On Parallelization of a Spatially-Explicit Structured Ecological Model for Integrated Ecosystem Simulation

Authors:
Dali Wang;Michael W. Berry;Louis J. Gross
Affiliations:
Department of Computer Science, University of Tennessee, Knoxville, TN 37996-3450, USA/;Department of Computer Sscience, University of Tennessee, Knoxville, TN 37996-3450, USA;Departments of Ecology and Evolutionary Biology and Mathematics, University of Tennessee, Knoxville, TN 37996, USA
Venue:
International Journal of High Performance Computing Applications
Year:
2006

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Abstract

Spatially explicit landscape population models are widely used to analyze the dynamics of an ecological species over a realistic landscape. These models may be data intensive applications when they include the age and size structure of the species in conjunction with spatial information coming from a geographic information system (GIS). We report on parallelization of a spatially explicit landscape model (PALFISH), in a component-based simulation framework, utilizing different parallel architectures. A multithreaded programming language (Pthread) is used to deliver high scalability on a symmetric multiprocessor (SMP), and a message-passing library is deployed for parallel implementation on both an SMP and a commodity cluster. The PALFISH model delivers essentially identical results as a sequential version but with high scalability: yielding a speedup factor of 12 as the runtime is reduced from 35 hours (sequential ALFISH) to 2.5 hours on a 14processor SMP. Hardware performance data were collected to better characterize the parallel execution of the model on the different architectures. This is the first documentation of a high performance application in natural resource management that uses different parallel computing libraries and platforms. Due to the diverse needs for computationally intensive multimodels in scientific applications, our conclusions arising from a practical application which brings the software component paradigm to highperformance scientific computing, can provide guidance for appropriate parallelization approaches incorporating multiple temporal and spatial scales.