Active harmony: towards automated performance tuning
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Autopilot: Adaptive Control of Distributed Applications
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
MRNet: A Software-Based Multicast/Reduction Network for Scalable Tools
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
An API for Runtime Code Patching
International Journal of High Performance Computing Applications
Concurrency and Computation: Practice & Experience - European–American Working Group on Automatic Performance Analysis (APART)
Automatic generation of dynamic tuning techniques
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
Advances in Engineering Software
Load balancing in homogeneous pipeline based applications
Parallel Computing
A methodology for transparent knowledge specification in a dynamic tuning environment
Software—Practice & Experience
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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.