Gvim: Gpu-accelerated virtual machines

Vishakha Gupta, Ada Gavrilovska, K. Schwan, Harshvardhan Kharche, Niraj Tolia, Vanish Talwar, Parthasarathy Ranganathan
Georgia Institute of Technology
Proceedings of the 3rd ACM Workshop on Systemlevel Virtualization for High Performance Computing HPCVirt 09 (2009) Publisher: ACM Press, Pages: 17-24


   title={GViM: GPU-accelerated virtual machines},

   author={Gupta, V. and Gavrilovska, A. and Schwan, K. and Kharche, H. and Tolia, N. and Talwar, V. and Ranganathan, P.},

   booktitle={Proceedings of the 3rd ACM Workshop on System-level Virtualization for High Performance Computing},





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The use of virtualization to abstract underlying hardware can aid in sharing such resources and in efficiently managing their use by high performance applications. Unfortunately, virtualization also prevents efficient access to accelerators, such as Graphics Processing Units (GPUs), that have become critical components in the design and architecture of HPC systems. Supporting General Purpose computing on GPUs (GPGPU) with accelerators from different vendors presents significant challenges due to proprietary programming models, heterogeneity, and the need to share accelerator resources between different Virtual Machines (VMs). To address this problem, this paper presents GViM, a system designed for virtualizing and managing the resources of a general purpose system accelerated by graphics processors. Using the NVIDIA GPU as an example, we discuss how such accelerators can be virtualized without additional hardware support and describe the basic extensions needed for resource management. Our evaluation with a Xen-based implementation of GViM demonstrate efficiency and flexibility in system usage coupled with only small performance penalties for the virtualized vs. non-virtualized solutions.
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