Automatic Performance Analysis of MPI Applications Based on Event Traces
Euro-Par '00 Proceedings from the 6th International Euro-Par Conference on Parallel Processing
Dynamic Performance Tuning Environment
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
SCALEA: A Performance Analysis Tool for Distributed and Parallel Programs
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Automatic Performance Analysis of Master/Worker PVM Applications with Kpi
Proceedings of the 7th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Active harmony: towards automated performance tuning
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Scheduling From the Perspective of the Application
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
An API for Runtime Code Patching
International Journal of High Performance Computing Applications
Dynamic performance tuning of distributed programming libraries
ICCS'03 Proceedings of the 2003 international conference on Computational science
Modeling master/worker applications for automatic performance tuning
Parallel Computing - Algorithmic skeletons
Dynamic Pipeline Mapping (DPM)
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Load balancing in homogeneous pipeline based applications
Parallel Computing
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
A methodology for transparent knowledge specification in a dynamic tuning environment
Software—Practice & Experience
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The Master/Worker paradigm is one of the most commonly used by parallel/distributed application developers. This paradigm is easy to understand and is fairly close to the abstract concept of a wide range of applications. However, to obtain adequate performance indexes, such a paradigm must be managed in a very precise way. There are certain features, such as data distribution or the number of workers, that must be tuned properly in order to obtain such performance indexes, and in most cases they cannot be tuned statically since they depend on the particular conditions of each execution. In this context, dynamic tuning seems to be a highly promising approach since it provides the capability to change the parameters during the execution of the application to improve performance. In this paper, we demonstrate the usage of a dynamic tuning environment that allows for adaptation of the number of workers based on a theoretical model of Master/Worker behavior. The results show that such an approach significantly improves the execution time when the application modifies its behavior during execution.