Predicting Performance of Hybrid Master/Worker Applications Using Model-Based Regression Trees

Nowadays, there are several features related to node architecture, network topology and programming model that significantly affect the performance of applications. Therefore, the task of adjusting the values of parameters of hybrid parallel applications to achieve the best performance requires a high degree of expertise and a huge effort. Determining a performance model that considers all the system and application features is a very complex task that in most cases produces poor results. In order to simplify this goal and improve the results, we introduce a model-based regression tree technique to improve the accuracy of performance prediction for parallel Master/Worker applications on homogeneous multicore systems. The technique has been used to model the iteration time of the general expression for performance prediction. This approach significantly reduces the effort in getting an accurate prediction model, although it requires a relatively large training data set. The proposed model determines the configuration of the appropriate number of workers and threads of the hybrid application to achieve the best possible performance.