Abstract
Graphics Processing Units (GPUs) were originally designed to manipulate images, but due to their intrinsic parallel nature, they turned into a powerful tool for scientific applications. In this article, we evaluated GPU performance in an implementation of a traditional stochastic simulation – the correlated Brownian motion. This movement can be described by the Generalized Langevin Equation (GLE), which is a stochastic integro-differential equation, with applications in many areas like anomalous diffusion, transport in porous media, noise analysis, quantum dynamics, among many others. Our results show the power inherent in GPU programming when compared to traditional CPUs (Intel): we observed acceleration values up to sixty times by using a NVIDIA GPU in place of a single-core Intel CPU.
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