8464

GPU Virtualization

Kristoffer Robin Stokke
Department of Informatics, University of Oslo
University of Oslo, 2012
@article{stokke2012gpu,

   title={GPU Virtualization},

   author={Stokke, K.R.},

   year={2012}

}

Download Download (PDF)   View View   Source Source   Source codes Source codes

Package:

812

views

In modern computing, the Graphical Processing Unit (GPU) has proven its worth beyond that of graphics rendering. Its usage is extended into the field of general purpose computing, where applications exploit the GPU’s massive parallelism to accelerate their tasks. Meanwhile, Virtual Machines (VM) continue to provide utility and security by emulating entire computer hardware platforms in software. In the context of VMs, however, there is the problem that their emulated hardware arsenal do not provide any modern, high end GPU. Thus any application running in a VM will not have access to this computing resource, even if it can be backed by physically available resources. In this thesis we address this problem. We discuss different approaches to provide VMs with GPU acceleration, and use this to design and implement Vocale (pronounced "Vocal"). Vocale is an extension to VM technology that enables applications in a VM to accelerate their operation using a virtual GPU. A critical look at our implementation is made to find out what makes this task difficult, and what kind of demands such systems place on the supporting software architecture. To do this we evaluate the efficiency of our virtual GPU contra a GPU running directly on physical hardware. This thesis holds value for readers who are interested in virtualization and GPU technology. Vocale, however, also features a broad range of technologies. Anyone with interest in programming in C / C++, software libraries, kernel modules, Python scripting and automatic code generation will have a chance of finding something interesting here.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

184 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1311 peoples are following HGPU @twitter

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: