Scalable Solution of Radiative Heat Transfer Problems by the Photon Monte Carlo Algorithm on Hybrid Computing Architectures

Joo Hong Lee, Mark T. Jones, Paul E. Plassmann
Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, Virginia 24061
The 2012 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’12), 2012

   title={Scalable Solution of Radiative Heat Transfer Problems by the Photon Monte Carlo Algorithm on Hybrid Computing Architectures},

   author={Lee, J.H. and Jones, M.T. and Plassmann, P.E.},



Download Download (PDF)   View View   Source Source   



The simulation of Radiative Heat Transfer (RHT) effects by the Photon Monte Carlo (PMC) method is a computationally demanding problem. In this paper we present results and analysis of a new algorithm designed to solve this problem on a hybrid computing architecture. This architecture includes distributed memory, shared memory, and Graphics Processing Unit (GPU) accelerated components. In this paper we present an approach to obtain good parallel performance based on a partitioning of the application software into two parts. The first part is a multithreaded application code that manages the ray tracing aspects of the PMC. The second part is an asynchronous, GPU-accelerated pseudo-random number generation library. An advantage of this approach is that this software framework can be easily translated to other Monte Carlo applications. We present experimental results from a largescale hybrid computer for a standard RHT model problem and compare these results to our analytical model.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1665 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

339 people like HGPU on Facebook

* * *

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: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • 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: nVidia CUDA Toolkit 6.5.14, AMD APP SDK 3.0
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.3
  • SDK: AMD APP SDK 3.0

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 © 2010-2015 hgpu.org

All rights belong to the respective authors

Contact us: