Factoring: a method for scheduling parallel loops
Communications of the ACM
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
MATE: toward scalable automated and dynamic performance tuning environment
PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume 2
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Parallel/Distributed programming is a complex task that requires a high degree of expertise to fulfill the expectations of high performance computation. On the one hand, application developers must tackle new programming paradigms, languages, libraries. On the other hand they must consider all the issues concerning application performance. On this context the Master/Worker paradigm appears as one of the most commonly used because it is quite easy to understand and there is a wide range of applications that match this paradigm. However, to reach high performance indeces it is necessary to tune the data distribution or the number of Workers considering the particular features of each run or even the actual behavior that can change dynamically during the execution. Dynamic tuning becomes a necessary and promising approach to reach the desired indeces. In this paper, we show the usage of a dynamic tuning environment that allows for adapting the data distribution applying Factoring algorithm on Master/Worker applications. The results show that such approach improves the execution time significantly when the application modifies its behavior during its execution.