9659

GPU-Accelerated Real-Time Visualization and Interaction for Coupled Fluid Dynamics

Florian De Vuyst, Christophe Labourdette, Christian Rey
Centre de Mathematiques et de Leurs Applications (CMLA)
hal-00837555, (22 June 2013)
@unpublished{devuyst:hal-00837555,

   hal_id={hal-00837555},

   url={http://hal.archives-ouvertes.fr/hal-00837555},

   title={GPU-accelerated real-time visualization and interaction for coupled Fluid Dynamics},

   author={De Vuyst, Florian and Labourdette, Christophe and Rey, Christian},

   keywords={Graphics processing unit; gpu; real time CFD; interaction;},

   language={Anglais},

   affiliation={Centre de Math{‘e}matiques et de Leurs Applications – CMLA , Laboratoire de M{‘e}canique et Technologie – LMT},

   note={Conference paper},

   year={2013},

   month={Jun},

   pdf={http://hal.archives-ouvertes.fr/hal-00837555/PDF/cfm2013-devuyst_symposium_MathMeca.pdf}

}

Download Download (PDF)   View View   Source Source   

291

views

For real-time applications (dynamic data-driven applications systems like computer-assisted surgery, command and control, etc.), it is necessary to design fast or strongly-accelerated computational approaches. Reduced-order modeling (ROM) is a candidate methodology that summarizes all the parameter-dependent PDE solutions into an easy-to-compute condensed form. ROM usually requires an offline learning process that returns the essential components of the solutions. However, it is known that ROM methodology is not suitable for all problems, especially problems with a large Kolmogorov $n$-width, like for example dynamical problems involving a continuous multiscale spectrum (like turbulence). In this case, direct simulation is needed and one has to find acceleration strategies. Graphics Processing units (GPU) are a cheap but relevant way to parallelize computations on thousands of cores leading to speedups of order 200 for some algorithms. This paper talks about real-time CFD computations allowing for real time visualization and flow interaction.
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

1314 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: