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Scientific codes are all subject to variation in performance depending on the runtime platform and/or configuration, the output writing API employed, and the file system for output. Since changing the IO routines to match the optimal or desired configuration for a given system can be costly in terms of human time and machine resources, the Adaptable IO(More)
Computational Grids are distributed systems that provide access to computational resources in a transparent fashion. Collecting and providing information about the status of the Grid itself is called Grid monitoring. We describe R-GMA (Relational Grid Monitoring Architecture) as a solution to the Grid monitoring problem. It uses a local as view approach to(More)
We have developed and implemented the Relational Grid Monitoring Architecture (R-GMA) as part of the DataGrid project, to provide a flexible information and monitoring service for use by other middleware components and applications. R-GMA presents users with a virtual database and mediates queries posed at this database: users pose queries against a global(More)
We describe R-GMA (Relational Grid Monitoring Architecture) which is being developed within the European DataGrid Project as an Grid Information and Monitoring System. Is is based on the GMA from GGF, which is a simple Consumer-Producer model. The special strength of this implementation comes from the power of the relational model. We offer a global view of(More)
—Peta-scale scientific applications running on High End Computing (HEC) platforms can generate large volumes of data. For high performance storage and in order to be useful to science end users, such data must be organized in its layout, indexed, sorted, and otherwise manipulated for subsequent data presentation, visualization, and detailed analysis. In(More)
The Center for Plasma Edge Simulation project aims to automate the tedious tasks of monitoring the simulation, archiving and post-processing the output. This paper describes the tasks and requirements, the several components developed within the Kepler workflow system to provide the required functionality and the automated workflow solution. Besides(More)
SUMMARY Scientific workflows often benefit from or even require advanced modeling constructs, e.g., nesting of subworkflows, cycles for executing loops, data-dependent routing, and pipelined execution. In such settings, an often overlooked aspect of provenance takes center stage: A suitable model of provenance (MoP) for scientific workflows should be based(More)
In order to understand the complex physics of mother nature, physicist often use many approximations to understand one area of physics and then write a simulation to reduce these equations to ones that can be solved on a computer. Different approximations lead to different equations that model different physics, which can often lead to a completely(More)