A performance comparison of CUDA remote GPU virtualization frameworks

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Using GPUs reduces execution time of many applications but increases acquisition cost and power consumption. Furthermore, GPUs usually attain a relatively low utilization. In this context, remote GPU virtualization solutions were recently created to overcome the drawbacks of using GPUs. Currently, many different remote GPU virtualization frameworks exist, all of them presenting very different characteristics. These differences among them may lead to differences in performance. In this work we present a performance comparison among the only three CUDA remote GPU virtualization frameworks publicly available at no cost. Results show that performance greatly depends on the exact framework used, being the rCUDA virtualization solution the one that stands out among them. Furthermore, rCUDA doubles performance over CUDA for pageable memory copies. This work was funded by the Generalitat Valenciana under Grant PROMETEOII/2013/009 of the PROMETEO program phase II. Authors are also grateful for the generous support provided by Mellanox Technologies Reaño González, C.; Silla Jiménez, F. (2015). A performance comparison of CUDA remote GPU virtualization frameworks. IEEE. https://doi.org/10.1109/CLUSTER.2015.76