Visualizing the Performance of Parallel Programs
IEEE Software
Automatic Detection of Parallel Program Performance Problems
VECPAR '98 Selected Papers and Invited Talks from the Third International Conference on Vector and Parallel Processing
Autopilot: Adaptive Control of Distributed Applications
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
An API for Runtime Code Patching
International Journal of High Performance Computing Applications
Modeling master/worker applications for automatic performance tuning
Parallel Computing - Algorithmic skeletons
Concurrency and Computation: Practice & Experience - European–American Working Group on Automatic Performance Analysis (APART)
Patterns for parallel programming
Patterns for parallel programming
Scalable dynamic Monitoring, Analysis and Tuning Environment for parallel applications
Journal of Parallel and Distributed Computing
Automatic tuning of master/worker applications
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
Automatic generation of dynamic tuning techniques
Euro-Par'07 Proceedings of the 13th international Euro-Par conference on Parallel Processing
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The increasing use of parallel/distributed applications demands a continuous support to take significant advantages from parallel power. This includes the evolution of performance analysis and tuning tools which automatically allows for obtaining a better behavior of the applications. Different approaches and tools have been proposed and they are continuously evolving to cover the requirements and expectations of users. One such tool is MATE (Monitoring Analysis and Tuning Environment), which provides automatic and dynamic tuning for parallel/distributed applications. The knowledge used by MATE to analyze and take decisions is based on performance models which include a set of performance parameters and a set of mathematical expressions modeling the solution of the performance problem. These elements are used by the tuning environment to conduct the monitoring and analysis steps, respectively. The tuning phase depends on the results of the performance analysis. This paper presents a methodology to specify performance models. Each performance model specification can be automatically and transparently translated into a piece of software code encapsulating the knowledge to be straightforwardly included in MATE. Applying this methodology, the user does not have to be involved in the implementation details of MATE, which makes the usage of the tool more transparent. Copyright © 2011 John Wiley & Sons, Ltd.