9014

CU2rCU: A CUDA-to-rCUDA Converter

Carlos Reano Gonzalez
Universitat Politecnica de Valencia
Universitat Politecnica de Valencia, 2013
@article{reano2013cu2rcu,

   title={CU2rCU: A CUDA-to-rCUDA Converter},

   author={Rea{~n}o Gonz{‘a}lez, Carlos},

   year={2013}

}

Download Download (PDF)   View View   Source Source   

405

views

GPUs (Graphics Processor Units) are being increasingly embraced by the high performance computing and computational communities as an effective way of considerably reducing application execution time by accelerating significant parts of their codes. CUDA (Compute Unified Device Architecture) is a new technology developed by NVIDIA which leverages the parallel compute engine in GPUs. However, the use of GPUs in current HPC clusters presents certain negative side-effects, mainly related with acquisition costs and power consumption. rCUDA (remote CUDA) was recently developed as a software solution to address these concerns. Specifically, it is a middleware that allows transparently sharing a reduced number of CUDA-compatible GPUs among the nodes in a cluster, reducing acquisition costs and power consumption. While the initial prototype versions of rCUDA demonstrated its functionality, they also revealed several concerns related with usability and performance. With respect to usability, the rCUDA framework was limited by its lack of support for the CUDA extensions to the C language. Thus, it was necessary to manually convert the original CUDA source code into C plain code functionally identical but that does not include such extensions. For such purpose, in this document we present a new component of the rCUDA suite that allows an automatic transformation of any CUDA source code into plain C code, so that it can be effectively accommodated within the rCUDA technology.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

169 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1276 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: