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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.

Automatic Tuning of Data Distribution Using Factoring in Master/Worker Applications