Dali Wang

Toward ecosystem modeling on computing grids

Grid-based ecosystem modeling holds great promise for aiding the investigation of complex environmental systems. A prototype framework and generic software architecture provide increased interoperability and productivity for spatially explicit ecosystem modeling on heterogeneous grids.

A Parallel Fish Landscape Model for Ecosystem Modeling

Parallelization of a landscape fish population model (ALFISH) is an important effort towards high performance Across Tropic Level System Simulation (ATLSS) on a computing grid. ALFISH models the impacts of different water management strategies in the South Florida region on the freshwater fish population, providing estimates of the food resource available to wading birds. The parallel ALFISH model delivers similar results to those from a sequential implementation. Compared with the average simulation time of the sequential model, which is about 35 hours, the speed improvement of the parallel model on a symmetric multiprocessor (SMP) is substantial. Using 14 processors, the runtime of the parallel model with static partitioning is less than 4 hours, and that of the parallel model with dynamic load balancing is less than 3 hours.

A parallel implementation of ALFISH: simulating hydrological compartmentalization effects on fish dynamics in the Florida Everglades☆

Abstract A landscape modeling system called the Across Trophic-Level System Simulation (or ATLSS) has been developed in an effort to project the consequences of proposed water regulation plans for restoration of the South Florida Everglades. The ATLSS Landscape Fish Model (ALFISH) is a component of the ATLSS package (written in C++), which is used to provide dynamic measures of the spatially-explicit food resources available to wading birds, namely fish. The original (serial) ALFISH model requires as much as 30xa0h for 31-year simulations of specified scenarios. The model’s execution time has been successfully improved (by a factor of 4.5) by partitioning its data input and executing the model simultaneously (in parallel) on those partitions. This paper demonstrates how the model’s communications between partitioned data can be blocked to simulate compartmentalization effects on the input data. Minimal effects (below 1%) on the output of the original (serial) version are demonstrated. Regarding portability, both models (serial and parallel) have been successfully executed on two different computing environments: an SMP (Symmetric Multi-Processor) with 14 processors and a 14-processor network cluster.

A framework of integrating GIS and parallel computing for spatial control problems – a case study of wildfire control

Complex spatial control problems can be computationally intensive. Timely response in urgent spatial control situations such as wildfire control poses great challenges for the efficient solving of spatial control problems. Web-based and service-oriented architectures of integrating geographic information system (GIS) clients and parallel computing resources have been suggested as an effective paradigm to solve computationally intensive spatial problems. Such real-time coupling framework is highly dependent upon interactivity and on-demand availability of dedicated parallel computing resources appropriate for the problem. We present an approach to enhancing the efficiency of solving spatial control problems while offering another coupling framework of integrating computing resources from desktop GIS and parallel computing environments to alleviate such dependency. Specifically, a model knowledge database is developed to bridge the gap between desktop GIS models and parallel computing resources. Desktop GIS models can iteratively improve themselves by steering rules retrieved from the model knowledge database. To examine its effectiveness, we applied the framework to a wildfire control case. Simulation results show dramatic reduction in computation time of the improved desktop GIS model, and indicate that desktop GIS models enhanced by model knowledge databases can be useful in providing timely assistance on computationally intensive spatial control problems.

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

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.

Design and implementation of a parallel fish model for South Florida

A parallel, spatially explicit landscape fish population model (ALFISH) is presented hereby to model the impacts of different water management strategies in the South Florida region on the fresh water fish population, which in turn provides the information on the food resource available to wading birds. Adopting a static domain partitioning scheme and using message-passing, the parallel ALFISH model mimics the basic behaviors of fresh water fish based on the interaction of four components - landscape, hydrology, lower trophic biomass, and fish, over a time span up to several decades. The parallel ALFISH model delivers accurate results in simulations. Compared to the average simulation time of the sequential model, which is about 35 hours, the parallel model yields substantial speed improvement. On a symmetric multiprocessor (SMP), the execution time of the parallel ALFISH model on 13 processors is less than 4 hours - a speedup factor of near 9.

A grid service module for natural-resource managers

To facilitate transparent use of the high-performance Across Trophic-Level System Simulation (ATLSS) ecosystem-modeling package for natural-resource management, the authors developed a grid service module. The module exploits grid middleware functionality to process complex computation without requiring users to handle underlying issues. It represents the first application of grid computing to this discipline and provides a potential template for researchers in other disciplines to exploit scientific computation without extensive training in high-performance computing.

A parallel structured ecological model for high end shared memory computers

This paper presents a new approach to parallelize spatially-explicit structured ecological models. Previous investigations have mainly focused on the use of spatial decomposition for parallelization of these models. Here, we exploit the partitioning of species age structures (or layers) as part of an integrated ecosystem simulation on a high-end shared memory computer using OpenMP. As an example, we use a parallel spatially-explicit structured fish model (ALFISH) for regional ecosystem restoration to demonstrate the parallelization procedure and associated model performance evaluation. Identical simulation results, validated by a comparison with a sequential implementation, and impressive parallel model performance demonstrate that layer-wised partitioning offers advantages in parallelizing structured ecological models on high-end shared memory computers. The average execution time of the parallel ALFISH model, using 1 computational thread, is about 11 hours, while the execution of the parallel ALFISH model using 25 computational threads is about 39 minutes (the speedup factor being about 16).

Computational Science for Natural Resource Management

Recent advances in miniaturization, computing power, remote sensing, and modeling are revolutionizing the science of natural resource management, but these advances also bring many challenges. This article highlights some key problems in resource management that represent opportunities for computer scientists and engineers in search of challenging practical problems.

ON DESIGN OF A COUPLING COMPONENT FOR PARALLEL MULTIMODELING

A component-based simulation framework is a favorable choice for parallel multiscale simulations, which require a dedicated coupling component to support flexible data communication/exchange, computational parallelism, and on-demand dynamic load balancing. In this paper, a coupling component (the Coupler) was designed to support integrated parallel multimodeling. An integrated ecological multimodeling, part of the across trophic level system simulation (ATLSS) for Everglades ecosystem restoration, has been used to demonstrate the advantage of the Coupler for natural resource management applications.