Massively parallel Monte Carlo for many-particle simulations on GPUs

Joshua A. Anderson, Eric Jankowski, Thomas L. Grubb, Michael Engel, Sharon C. Glotzer
Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
arXiv:1211.1646 [physics.comp-ph] (7 Nov 2012)

   author={Anderson}, J.~A. and {Jankowski}, E. and {Grubb}, T.~L. and {Engel}, M. and {Glotzer}, S.~C.},

   title={"{Massively parallel Monte Carlo for many-particle simulations on GPUs}"},

   journal={ArXiv e-prints},




   keywords={Physics – Computational Physics, Condensed Matter – Materials Science, Condensed Matter – Soft Condensed Matter, Condensed Matter – Statistical Mechanics},




   adsnote={Provided by the SAO/NASA Astrophysics Data System}


Download Download (PDF)   View View   Source Source   



Current trends in parallel processors call for the design of efficient massively parallel algorithms for scientific computing. Parallel algorithms for Monte Carlo simulations of thermodynamic ensembles of particles have received little attention because of the inherent serial nature of the statistical sampling. In this paper, we present a massively parallel method that obeys detailed balance and implement it for a system of hard disks on the GPU. We reproduce results of serial high-precision Monte Carlo runs to verify the method. This is a good test case because the hard disk equation of state over the range where the liquid transforms into the solid is particularly sensitive to small deviations away from the balance conditions. On a GeForce GTX 680, our GPU implementation executes 95 times faster than on a single Intel Xeon E5540 CPU core, enabling 17 times better performance per dollar and cutting energy usage by a factor of 10.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1662 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

337 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: