Predicting GPU Kernel’s Performance on Upcoming Architectures

With the advent of heterogeneous systems that combine CPUs and GPUs, designing a supercomputer becomes more and more complex. The hardware characteristics of GPUs significantly impact the performance. Choosing the GPU that will maximize performance for a limited budget is tedious because it requires predicting the performance on a non-existing hardware platform. In this paper, we propose a new methodology for predicting the performance of kernels running on GPUs. This method analyzes the behavior of an application running on an existing platform, and projects its performance on another GPU based on the target hardware characteristics. The performance projection relies on a hierarchical roofline model as well as on a comparison of the kernel’s assembly instructions of both GPUs to estimate the operational intensity of the target GPU. We demonstrate the validity of our methodology on modern NVIDIA GPUs on several mini-applications. The experiments show that the performance is predicted with a mean absolute percentage error of 20.3 % for LULESH, 10.2 % for MiniMDock, and 5.9 % for Quicksilver.