  To understand and predict the performance of scientific applications, several
analytical and machine learning approaches have been proposed, each having its
advantages and disadvantages. In this paper, we propose and validate a hybrid
approach for performance modeling and prediction, which combines analytical and
machine learning models. The proposed hybrid model aims to minimize prediction
cost while providing reasonable prediction accuracy. Our validation results
show that the hybrid model is able to learn and correct the analytical models
to better match the actual performance. Furthermore, the proposed hybrid model
improves the prediction accuracy in comparison to pure machine learning
techniques while using small training datasets, thus making it suitable for
hardware and workload changes.
