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 […]

May 19, 2012 by hgpu

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 […]

May 1, 2012 by hgpu

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 […]

July 22, 2011 by hgpu

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. […]

January 12, 2011 by hgpu

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 […]

November 22, 2010 by hgpu