Validating the performance of GPU ports using differential performance models

Abstract

Offloading computation to the GPU is crucial to leverage many of today’s supercomputers. We expect the GPU port of an application to outperform the pure CPU implementation, but is this always true? Simple benchmarking only allows us to take a limited number of samples from a vast space of execution configurations and can, therefore, deliver only a fragmented answer. To answer the question systematically, even for individual application kernels, we propose a semi-automatic toolchain based on differential performance modeling and intuitive visualizations. We combine empirical performance models based on unified CPU–GPU profiles with hardware characteristics to derive differential performance models that can be easily compared across device types. In four case studies, we demonstrate how our toolchain pinpoints scaling issues in GPU ports, guides performance improvements, and identifies execution configurations with superior performance.