Scalable dynamic Monitoring, Analysis and Tuning Environment for parallel applications

Parallel/distributed systems are continuously growing. This allows and enables the scalability of the applications, either by considering bigger problems in the same period of time or by solving the problem in a shorter time. In consequence, the methodologies, approaches and tools related to parallel paradigm should be brought up to date to support the increasing requirements of the applications and the users. MATE (Monitoring, Analysis and Tuning Environment) provides automatic and dynamic tuning for parallel/distributed applications. The tuning decisions are made according to performance models, which provide a fast means to decide what to improve in the execution. However, MATE presents some bottlenecks as the application grows, due to the fact that the analysis process is made in a full centralized manner. In this work, we propose a new approach to make MATE scalable. In addition, we present the experimental results and the analysis to validate the proposed approach against the original one.