Efficient Large Scale Simulation of Stochastic Lattice Models on GPUs

With growing importance of nano-patterned surfaces and nano-composite materials in many applications from energy technologies to nano-electronics, a thorough understanding of the self-organized evolution of nano-structures needs to be established. Modelling and simulations of such processes can help in this endeavor and provide predictions for the turnout of manufacturing processes.

In this talk GPGPU-enabled implementations of two stochastic lattice models will be discussed, shedding light on the complications which arise when simulations of stochastic processes are to make efficient use of massively parallel GPU architectures.
A single-GPU implementation of the (2+1)-dimensional Roof-Top-model allows very precise large-scale studies of surface growth processes in the Kardar-Parisi-Zhang universality class.[1] Furthermore a multi-GPU enabled version of the 3d kinetic Metropolis lattice Monte-Carlo method[2] provides the capability to study the evolution of nano-structures both towards and out-of-equilibrium at spatio-temporal scales of experiments using only small to medium-sized GPU clusters.

[1] J. Kelling, G. Ódor Extremely large-scale simulation of a Kardar-Parisi-Zhang model using graphics cards, Physical Review E 84, 061150 (2011)
[1] J. Kelling, G. Ódor, F. Nagy, H. Schulz, K. Heinig Comparison of different parallel implementations of the 2+1-dimensional KPZ model and the 3-dimensional KMC model, The European Physical Journal - Special Topics 210, 175-187 (2012)