Abstract: TBC

Abstract: Environmental computing often involves diverse data sets, large and complex simulations, user interaction, optimisation, high performance computing, scientific visualisation and complex orchestration. Scientific Workflows are an ideal platform for incorporating all these aspects of environmental computing. They provide a common framework that both specifies and documents complex applications, but also also provides an execution platform. In this talk I will describe how Nimrod/OK achieves this goal. Nomrod/OK is based on the long running Nimrod tool set and the Kepler scientific workflow engine. It incorporates a novel user interaction tool called WorkWays, which combines Kepler and Science Gateways. It also includes non-linear optimisation algorithms that allow complex environmental problems to be solved. I will demonstrate Nimrod/OK and WorkWays with a number of environmental applications involving wild fire simulations and ecological planning.

Abstract: In order to support the computational and data needs of today’s science, new knowledge must be gained on how to deliver the growing capabilities of the national cyberinfrastructures and more recently commercial clouds to the scientist’s desktop in an accessible, reliable, and scalable way. In over a decade of working with domain scientists, the Pegasus workflow management system has being used by researchers to model seismic wave propagation, to discover new celestial objects, to study RNA critical to human brain development, and to investigate other important research questions. Recently, the Pegasus and the dispel4py teams have collaborated to enable automated processing of real-time seismic interferometry and earthquake “repeater” analysis using data collected from the IRIS database. The proposed integrated solution empowers real-time stream-based workflows to seamlessly run on different distributed infrastructures (or in the wide area), where data is automatically managed by a task-oriented workflow system, which orchestrates the distributed execution. We have demonstrated the feasibility of this approach by using docker containers to deploy the workflow management systems and two different computing infrastructures: an Apache Storm cluster for real-time processing, and an MPI-based cluster for shared memory computing. Stream-based executions is managed by dispel4py, while the data movement between the clusters and the workflow engine (submit host) is managed by Pegasus.

Abstract: Computationally ecient sensitivity analysis of a large-scale air pol- lution model is an important issue we focus on in this paper. Sensitivity studies play an important role for reliability analysis of the results of com- plex nonlinear models as those used in the air pollution modelling. There is a number of uncertainties in the input data sets, as well as in some internal coecients, which determine the speed of the main chemical re- actions in the chemical part of the model. These uncertainties are subject to our quantitative sensitivity study. Monte Carlo and quasi-Monte Carlo algorithms are used in this study. A large number of numerical experiments with some special modica- tions of the model must be carried out in order to collect the necessary input data for the particular sensitivity study. For this purpose we cre- ated an ecient high performance implementation SA-DEM, based on the MPI version of the package UNI-DEM. A large number of numerical ex- periments were carried out with SA-DEM on the IBM MareNostrum III at BSC - Barcelona, helped us to identify a severe performance problem with an earlier version of the code and to resolve it successfuly. The im- proved implementation appears to be quite ecient for that challenging computational problem, as our experiments show. Some numerical results with performance and scalability analysis of these results are presented in the paper.

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