K.A. Hawick, D.P. Playne
The Heisenberg model of classical spins makes use of both Monte Carlo stochastic dynamics as well as time-integration of its equation of motion. These two schemes have different parallelisation strategies and tradeoffs. We implement both algorithms using a data-parallel approach for Graphical Processing Units (GPUs) and we discuss the resulting performance on various combinations of […]
View View   Download Download (PDF)   
Taras Yavors'kii, Martin Weigel
We develop a highly optimized code for simulating the Edwards-Anderson Heisenberg model on graphics processing units (GPUs). Using a number of computational tricks such as tiling, data compression and appropriate memory layouts, the simulation code combining over-relaxation, heat bath and parallel tempering moves achieves a peak performance of 0.29 ns per spin update on realistic […]
View View   Download Download (PDF)   
Alessandra Campos, J. Pecanha, Patricia Pampanelli, Rafael de Almeida, Marcelo Lobosco, Marcelo Vieira, Socrates de O. Dantas
The study of magnetic phenomena in nanometer scale is essential for development of new technologies and materials. It also leads to a better understanding of magnetic properties of matter. An approach to the study of magnetic phenomena is the use of a physical model and its computational simulation. For this purpose, in previous works we […]
Martin Weigel
Graphics processing units (GPUs) are recently being used to an increasing degree for general computational purposes. This development is motivated by their theoretical peak performance, which significantly exceeds that of broadly available CPUs. For practical purposes, however, it is far from clear how much of this theoretical performance can be realized in actual scientific applications. […]
J. F. Yu, H. C. Hsiao, Ying-Jer Kao
We use the graphical processing unit (GPU) to accelerate the tensor contractions, which is the most time consuming operations in the variational method based on the plaquette renormalized states. Using a frustrated Heisenberg J1-J2 model on a square lattice as an example, we implement the algorithm based on the compute unified device architecture (CUDA). For […]

* * *

* * *

Follow us on Twitter

HGPU group

1655 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

334 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: