Virtualizing GPU direct packet I/O on commodity Ethernet to accelerate GPU-NFV

Abstract

Network functions (NFs) play an important role in the rapid and dynamic deployment of various services. Network function virtualization (NFV) on commodity servers is becoming popular; and is associated with benefits in terms of cost, elasticity, and liveness, instead of dedicated hardware middleboxes. In this recent trend, graphics processing units (GPUs) have been shown to be highly suitable for NF acceleration, owing to their high parallelism and memory bandwidth. This paper proposes a new GPU-NFV architecture called Janus to accelerate NFs in virtualized environments. Janus is an extension of a previous study conducted by the present authors and involves GPU direct packet input/output (I/O) on commodity Ethernet, to a virtualization environment. Janus uses single-root I/O virtualization (SR-IOV) to enable high-performance NIC sharing between the host and guest virtual machine running GPU direct packet I/O. Internally, the received packets for GPU-NFV are transferred to the GPU memory directly through a combination of SR-IOV and peer-to-peer DMA. The evaluation results show that Janus’ GPU-NFV performance is up to 2.3x higher than that of the state-of-the-art data-plane development kit (DPDK).