Evaluating a formal methodology for dynamic tuning of large-scale parallel applications

Abstract

Large-scale parallel applications performance is usually far from the expected. Dynamic tuning is a powerful technique that helps to improve the performance of parallel applications. To bring this technique to large-scale computers, this work presents a model that enables decentralized dynamic tuning of large-scale parallel applications. In this model, applications are decomposed into disjoint subsets of tasks that can be tuned individually but also abstracted to obtain a global view of the parallel application. The proposed model has been designed as a hierarchical tuning network of distributed analysis modules and implemented in the form of ELASTIC, an environment for large-scale dynamic tuning. Using ELASTIC an experimental evaluation has been conducted over a synthetic large-scale parallel application and a real agent-based parallel application. The results show that the proposed model, embodied in ELASTIC, is able to scale to meet the demands of dynamic tuning over thousands of processes, while effectively improving the performance of large-scale applications.