Abstract
Performance is a main issue in parallel application development. Dynamic tuning is a technique that changes certain applications' parameters on-line to improve their performance adapting the execution to actual conditions. To perform that, it is necessary to collect measurements, analyze application behavior and carry out tuning actions during the application execution. Computational Grids present proclivity for dynamic changes in the environment during the application execution. Therefore, dynamic tuning tools are necessary to reach the expected performance indexes of applications on those environments. This paper addresses the dynamic tuning of parallel/distributed applications on Computational Grids. We analyze Grid environments to determine their characteristics and we present the development of dynamic tuning tool GMATE enabled for such environments. The performance analysis is based on performance models that indicate how to improve the application execution. A particular problem which provokes performance bottlenecks is the load imbalance in Master/Worker applications. A heuristic to dynamically tune granularity of work and number of workers is proposed. Finally, we describe the experimental validation of the performance model and its applicability on a set of real parallel applications.
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