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GPU Passthrough Performance: A Comparison of KVM, Xen, VMWare ESXi, and LXC for CUDA and OpenCL Applications

John Paul Walters, Andrew J. Younge, Dong-In Kang, Ke-Thia Yao, Mikyung Kang, Stephen P. Crago, Geoffrey C. Fox
Information Sciences Institute, University of Southern California, Arlington, VA 22203
International Conference on Cloud Computing, 2014
@article{walters2014gpu,

   title={GPU Passthrough Performance: A Comparison of KVM, Xen, VMWare ESXi, and LXC for CUDA and OpenCL Applications},

   author={Walters, John Paul and Younge, Andrew J and Kang, Dong-In and Yao, Ke-Thia and Kang, Mikyung and Crago, Stephen P and Fox, Geoffrey C},

   year={2014}

}

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As more scientific workloads are moved into the cloud, the need for high performance accelerators increases. Accelerators such as GPUs offer improvements in both performance and power efficiency over traditional multi-core processors; however, their use in the cloud has been limited. Today, several common hypervisors support GPU passthrough, but their performance has not been systematically characterized. In this paper we show that low overhead GPU passthrough is achievable across 4 major hypervisors and two processor microarchitectures. We compare the performance of two generations of NVIDIA GPUs within the Xen, VMWare ESXi, and KVM hypervisors, and we also compare the performance to that of Linux Containers (LXC). We show that GPU passthrough to KVM achieves 98-100% of the base system’s performance across two architectures, while Xen and VMWare achieve 96-99% of the base systems performance, respectively. In addition, we describe several valuable lessons learned through our analysis and share the advantages and disadvantages of each hypervisor/GPU passthrough solution.
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