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
On-line automated performance diagnosis on thousands of processes
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Modeling master/worker applications for automatic performance tuning
Parallel Computing - Algorithmic skeletons
Design and implementation of a dynamic tuning environment
Journal of Parallel and Distributed Computing
Concurrency and Computation: Practice & Experience - European–American Working Group on Automatic Performance Analysis (APART)
Dynamic Pipeline Mapping (DPM)
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Performance measurement and analysis tools for extremely scalable systems
Concurrency and Computation: Practice & Experience - International Supercomputing Conference
Tree-based overlay networks for scalable applications
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Automatic tuning of data distribution using factoring in master/worker applications
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
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 use of parallel/distributed programming increases as it enables high performance computing. There are many tools that help a user in the performance analysis of the application, and that allow to improve the application execution. As there is a high demand of computational power, new systems, such as large scale computer clusters, have become more common and accessible to everyone to solve complex problems. However, these systems generate a new set of problems related to the scalability of current analysis and tuning tools. Our automatic and dynamic tuning environment MATE does not scale well because it has a set of common bottlenecks in its architecture, and hence we have decided to improve the tool for providing dynamic tuning on large scale systems too. For this purpose, we are designing a new tool that introduces a tree-based overlay network infrastructure for scalable metrics collection, and to substitutes the current centralized performance analysis by a distributed one, in order to provide better scalability.