The scalability and efficiency of graph applications are significantly constrained by conventional systems and their supporting programming models. Technology trends like multicore, manycore, and heterogeneous system architectures are introducing further challenges and possibilities for emerging application domains such as graph applications. This paper explores the space of effective parallel execution of ephemeral graphs that are dynamically generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The workloads are expressed using the semantics of an Exascale computing execution model called ParalleX. For comparison, results using conventional execution model semantics are also presented. We find improved load balancing during runtime and automatic parallelism discovery improving efficiency using the advanced semantics for Exascale computing. I. INTRODUCTIONA large class of problems in physics and molecular biology can be represented using a particle interaction method commonly known as N-Body and computational techniques based on these discretization methods. The science domains utilizing the particle interaction discretization model are limited by the number of particles that can be simulated and the time it takes to execute the computational techniques. Conventional practices have significantly advanced particle interaction based methodologies. However, the combined ecosystem of emerging multicore based system architectures and conventional programming models are imposing grave challenges to the continued effectiveness of these methods. This research identifies and addresses these challenges through the hypothesis that emerging system architectures and extreme scale oriented runtime systems can dramatically improve the end science.Applications based on graphs and tree data structures rely on more dynamic, adaptive, and irregular computations. This work explores an exemplar dynamic tree based application embodied by an N-Body simulation. Systems comprising many particles (N-Body problem) interacting through long-range forces have considerable computational science interest. N-Body systems comprising three or more particles do not have a closed form solution; consequently, iterative methods are used to approximate solutions for the N-Body problem. The N-Body problem simulates the evolution of n particles under the influence of mutual pairwise interactions through forces such as gravitational pull or electrostatic forces. This work focuses on gravitational forces operating on the N-Body system and the Barnes-Hut approximation of the N-Body solution.While several approaches to simulating N-Body systems exist, the Barnes-Hut algorithm [5] is widely used in astrophysical simulations mainly due to its logarithmic computational complexity while generating results that are within acceptable bounds of accuracy. In the Barnes-Hut algorithm the particles are grouped by a hierarchy of cube structures using a recursive algorithm which subdivides the cubes until there is one particle per sub-cube. It then u...

SUMMARYWe describe the background, architecture and implementation of a user portal for the SCOOP coastal ocean observing and modeling community. SCOOP is engaged in the real-time prediction of severe weather events, including tropical storms and hurricanes, and provides operational information including wind, storm surge and resulting inundation, which are important for emergency management. The SCOOP portal, built with the GridSphere Framework, currently integrates customized Grid portlet components for data access, job submission, resource management and notification.

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SUMMARYobservational data need to be merged and visualized in a geospatial context for a variety of analyses and applications. A data archive at LSU aggregates the model outputs from multiple sources, and a data-driven workflow triggers remotely performed conversion of a subset of model predictions to georeferenced data sets, which are then delivered to a Web Map Service located at Texas A&M University. Other nodes in the distributed system aggregate the observational data. This paper describes the use of GIS within the SCOOP program for the 2005 hurricane season, along with details of the data-driven distributed dataflow and workflow, which results in geospatial products. We also focus on future plans related to the complimentary use of GIS and Grid technologies in the SCOOP program, through which we hope to provide a wider range of tools that can enhance the tools and capabilities of earth science research and hazard planning.

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