Fusion Plasma simulation in the Interactive Grid

Within the framework of the int.eu.grid project, IVISDEP, which stands for Interactive Visualizer of an Integrator of Stochastic Differential Equations for Plasmas, has been successfully ported and executed. It is an example of integration of many of the innovative features that this grid infrastructure offers. The application itself simulates and visualizes the evolution of the plasma inside a stellarator fusion device. The followed approach implies the simulation of a great number of trajectories of particles, whose positions are calculated among a large number of nodes in the Grid. The graphical interface allows the user to see and modify the physical and computational parameters of the simulation interactively, making this application a valuable tool for researchers in plasma physics

Graphical Framework for Grid Interactive and Parallel Applications

Considering how complex Grid technology is, it is essential to provide adequate support on users' level in order to simplify interaction and attract users. Intuitive and easy-to-use graphical user interfaces could be critical for the usefulness of the whole infrastructure and may play a significant role within successful stories. We would like to present the int.eu.grid approach to a Migrating Desktop product providing a unified and intuitive graphical work environment that allows users to control sophisticated interactive services, access Grid resources, run sequential and parallel jobs -- using both batch and interactive paradigms. The Migrating Desktop provides a front-end for embedding some of the application mechanisms and interfaces, and it allows the user to have virtual access to Grid resources. Generic API for applications based on the OSGi specification provides mechanisms for the job submission phase, interaction with an application and remote visualization of the results which assemble the powerful platform for the Grid environment

MPI Support on the Grid

Grids as infrastructures offer access to computing, storage and other resources in a transparent way. The user does not have to be aware where and how the job is being executed. Grid clusters in particular are an interesting target for running computation-intensive calculations. Running MPI-parallel applications on such clusters is a logical approach that is of interest to both computer scientists and to engineers. This paper gives an overview of the issues connected to running MPI applications on a heterogenous Grid consisting of different clusters located at different sites within the Int.EU.Grid project. The role of a workload management system (WMS) for such a scenario, as well as important modifications that need to be made to a WMS oriented towards sequential batch jobs for better support of MPI applications and tools are discussed. In order to facilitate the adoption of MPI-parallel applications on heterogeneous Grids, the application developer should be made aware of performance problems, as well as MPI-standard issues within its code. Therefore tools for these issues are also supported within Int.EU.Grid. Also, the special case of running MPI applications on different clusters simultaneously as a more Grid-oriented computational approach is described

Managing MPI Applications in Grid Environments

Abstract. One of the goals of the EU CrossGrid project is to provide a basis for supporting the efficient execution of parallel and interactive applications on Grid environments. CrossGrid jobs typically consist of computationally intensive simulations that are often programmed using a parallel programming model and a parallel programming library (MPI). This paper describes the key components that we have included in our resource management system in order to provide effective and reliable execution of parallel applications on a Grid environment. The general architecture of our resource management system is briefly introduced first and we focus afterwards on the description of the main components of our system. We provide support for executing parallel applications written in MPI either in a single cluster or over multiple clusters. 1