Performance predictors for graphics processing units applied to dark-silicon-aware design space exploration

The limitations on the scalability of computer systems imposed by the dark-silicon effects are so severe that they support the extensive use of heterogeneity such as the GP-GPU for general purpose processing. Performance simulators of GP-GPU heterogeneous systems aim to provide performance accuracy at the cost of execution time. In this work, we handle time-consuming simulations of design space exploration systems based on GPUs. We have developed performance predictors based on machine learning (ML) algorithms and evaluated them in accuracy and throughput (number of predictions per second). We measure model accuracy through the mean absolute percentage error (MAPE) and the model efficiency through a throughput metric (millions of predictions per second). Our experiments revealed that decision trees predictors are the most promising regarding accuracy and efficiency. We applied the best predictors into the MultiExplorer, a dark silicon-aware design space exploration tool that allows designers to explore the architecture and microarchitecture of multicore/manycore system design.