A Comparative Performance Analysis of Remote GPU Virtualization over Three Generations of GPUs

Abstract

The use of Graphics Processing Units (GPUs) has become a very popular way to accelerate the execution of many applications. However, GPUs are not exempt from side effects. For instance, GPUs are expensive devices which additionally consume a non-negligible amount of energy even when they are not performing any computation. Furthermore, most applications present low GPU utilization. To address these concerns, the use of GPU virtualization has been proposed. In particular, remote GPU virtualization is a promising technology that allows applications to transparently leverage GPUs installed in any node of the cluster. In this paper the remote GPU virtualization mechanism is comparatively analyzed across three different generations of GPUs. The goal of this study is to analyze how the performance of the remote GPU virtualization technique is impacted by the underlying hardware. To that end, the Tesla K20, Tesla K40 and Tesla P100 GPUs along with FDR and EDR InfiniBand fabrics are used in the study. The analysis is performed in the context of the rCUDA middleware. It is clearly shown that the GPU virtualization middleware requires a comprehensive design of its communication layer, which should be perfectly adapted to every hardware generation in order to avoid a reduction in performance.