UC Riverside

This paper introduces a predictive modeling framework for GPU performance. The key innovation underlying this approach is that performance statistics collected from representative workloads running on current generation GPUs can effectively predict the performance of next-generation GPUs. This is useful when simulators are available for the next-generation device, but simulation times are exorbitant, rendering early design space exploration of microarchitectural parameters and other features infeasible. When predicting performance across three Intel GPU generations (Haswell, Broadwell, Skylake), our models achieved impressively low out-of-sample-errors ranging from 7.45% to 8.91%, while running 29 481 to 44 214 times faster than cycle-accurate simulations. A detailed ranking of the most impactful features selected for these models provides an insight as to which microarchitectural subsystems have the greatest impact on performance from one generation to the next.