This project investigates an observation-based execution time estimation approach for resource planning and usage estimation in the grid environment for application and resource scheduling. More specifically, the proposed approaches will collect/manage/utilize application characteristics and performance results, and equally transfer such information across disjoint applications and hardware platforms. With these approaches, performance data from one application's executions on one platform helps predict the performance of another application on another platform. The expected outcome of this research is a meta-predictor, an effective, efficient and sufficiently accurate cross-platform performance prediction tool that can provide performance predictions as a general service to assist grid users in both their long-term research planning and their everyday job execution. These approaches will be validated and evaluated on production platforms with applications representative for nationally relevant high-end applications, such as National Lab production codes.
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