On implementation of GPU virtualization using PCI pass-through

In this paper, we use PCI pass-through technology and make the virtual machines in a virtual environment are able to use the NVIDIA graphics card, which uses the CUDA parallel programming. It makes the virtual machine have not only the virtual CPU but also the real GPU for computing. The performance of virtual machine is predicted to increase dramatically. This paper will measure the performance differences between virtual machines and physical machines by using CUDA; and how virtual machines would verify CPU numbers under influence of CUDA performance. At last, we compare two open source virtualization environment hypervisor, whether it is after PCI pass-through CUDA performance differences or not. Through the experiment, we will be able to know which environment will reach the best efficiency in a virtual environment by using CUDA.

[1]  Vanish Talwar,et al.  GViM: GPU-accelerated virtual machines , 2009, HPCVirt '09.

[2]  Federico Silla,et al.  rCUDA: Reducing the number of GPU-based accelerators in high performance clusters , 2010, 2010 International Conference on High Performance Computing & Simulation.

[3]  Muli Ben-Yehuda,et al.  IOMMU: strategies for mitigating the IOTLB bottleneck , 2010, ISCA'10.

[4]  Gianni De Fabritiis,et al.  Swan: A tool for porting CUDA programs to OpenCL , 2011, Comput. Phys. Commun..

[5]  Sungbo Jung Parallelized pairwise sequence alignment using CUDA on multiple GPUs , 2009, BMC Bioinformatics.

[6]  Giulio Giunta,et al.  A GPGPU Transparent Virtualization Component for High Performance Computing Clouds , 2010, Euro-Par.

[7]  Scott B. Baden,et al.  Source-to-Source Optimization of CUDA C for GPU Accelerated Cardiac Cell Modeling , 2010, Euro-Par.

[8]  Kevin Skadron,et al.  A performance study of general-purpose applications on graphics processors using CUDA , 2008, J. Parallel Distributed Comput..

[9]  Lin Shi,et al.  vCUDA: GPU accelerated high performance computing in virtual machines , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[10]  Federico Silla,et al.  Enabling CUDA acceleration within virtual machines using rCUDA , 2011, 2011 18th International Conference on High Performance Computing.

[11]  Federico Silla,et al.  Performance of CUDA Virtualized Remote GPUs in High Performance Clusters , 2011, 2011 International Conference on Parallel Processing.