The Boat Hull Model: Enabling Performance Prediction for Parallel Computing Prior to Code Development

Multi-core and many-core were already major trends for the past six years and are expected to continue for the next decade. With these trends of parallel computing, it becomes increasingly difficult to decide on which processor to run a given application, mainly because the programming of these processors has become increasingly challenging. In this work, we present a model to predict the perfor-mance of a given application on a multi-core or many-core processor. Since programming these processors can be chal-lenging and time consuming, our model does not require source code to be available for the target processor. This is in contrast to existing performance prediction techniques such as mathematical models and simulators, which require code to be available and optimized for the target architec-ture. To enable performance prediction prior to algorithm im-plementation, we classify algorithms using an existing algo-rithm classification. For each class, we create a specific in-stance of the roofline model, resulting in a new class-specific model. This new model, named the boat hull model, enables performance prediction and processor selection prior to the development of architecture specific code. We demonstrate the boat hull model using GPUs and CPUs as target architectures. We show that performance is accurately predicted for an example real-life application