Abstract
The current availability of a variety of computing infrastructures including HPC, Grid and Cloud resources provides great computer power for many fields of science, but their common profit to accomplish large scientific experiments is still a challenge. In this work, we use the paradigm of climate modeling to present the key problems found by standard applications to be run in hybrid distributed computing infrastructures and propose a framework to allow a climate model to take advantage of these resources in a transparent and user-friendly way. Furthermore, an implementation of this framework, using the Weather Research and Forecasting system, is presented as a working example. In order to illustrate the usefulness of this framework, a realistic climate experiment leveraging Cluster, Grid and Cloud resources simultaneously has been performed. This test experiment saved more than 75% of the execution time, compared to local resources. The framework and tools introduced in this work can be easily ported to other models and are probably useful in other scientific areas employing data- and CPU-intensive applications.
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