The Boat Hull Model: Adapting the Roofline Model to Enable Performance Prediction for Parallel Computing
Multi-core and many-core were already major trends for the past six years, and are expected to continue for the next decades. With these trends of parallel computing, it becomes increasingly difficult to decide on which architecture to run a given application. In this work, we use an algorithm classification to predict performance prior to algorithm implementation. For this purpose, we modify the roofline model to include class information. In this way, we enable architectural choice through performance prediction prior to the development of architecture specific code. The new model, the boat hull model, is demonstrated using a GPU as a target architecture. We show for 6 example algorithms that performance is predicted accurately without requiring code to be available.
the roofline model performance performance prediction parallel computing many-core accelerators