Analyzing Performance and Power-Efficiency Variations among NVIDIA GPUs

Understanding the variations in performance and power-efficiency of compute nodes is important for enhancing these factors in modern supercomputing systems. Previous studies have focused on variations in CPUs and DRAMs, but there has been little attention on GPUs. This is despite many current supercomputing systems employing GPUs (which consume a significant fraction of the power of such systems) as power-efficient accelerators for HPC applications. This paper describes the first thorough evaluation of performance and power-efficiency variations in GPUs. Specifically, we execute 48 CUDA kernels on 856 devices selected from three generations of NVIDIA GPUs (P100, V100, and A100), and analyze the impact of differences in both the CUDA kernels and GPU generation on performance and power-efficiency. Our analysis shows that there are non-negligible variations in both performance and power-efficiency, and that these variations are strongly affected by both the kernels that are running and the GPU generation.