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Computational science is paramount to the understanding of underlying processes in internal combustion engines of the future that will utilize non-petroleum-based alternative fuels, including carbon-neutral biofuels, and burn in new combustion regimes that will attain high efficiency while minimizing emissions of particulates and nitrogen oxides.(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)
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)
This white paper is aimed at creating a directory of existing performance monitoring and evaluation tools. The detailed categorization enables finding relevant properties, similarities and differences, and comparing the tools. The paper is neutral: there are no comments or assessment. The catalogue helps grid users, developers, and administrators in finding(More)
SUMMARY The first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a Functional Magnetic Resonance Imaging workflow was defined, which participants had to either simulate or run in order to(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)
P-GRADE provides a high-level graphical environment to develop parallel applications transparently both for parallel systems and the Grid. P-GRADE supports the interactive execution of parallel programs as well as the creation of a Condor,Condor-G or Globus job to execute parallel programs in the Grid. In P-GRADE, the user can generate either PVM or MPI(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)
We introduce and describe scientific workflows, i.e., executable descriptions of automatable scientific processes such as computational science simulations and data analyses. Scientific workflows are often expressed in terms of tasks and their (dataflow) dependencies. This chapter first provides an overview of the characteristic features of scientific(More)