Parallel GPU implementations of numerical methods for fluid dynamics

Abstract

This article presents the application of parallel computing techniques using Graphic Processing Unit (GPU) in order to improve the computational efficiency of numerical methods applied to uid dynamics problems. In the last ten years, GPUs have emerged as a major paradigm for solving complex problems using parallel computing techniques. Fluid dynamics problems usually requires large execution times to perform simulations for realistic scenarios. In this work, two numerical models for fluid dynamics are presented, and parallel implementations on GPU for the Strongly Implicit Procedure and the Cyclic Reduction methods for solving linear systems are introduced. The experimental evaluation of the proposed methods demonstrates that a significant reduction on the computing times can be attained when solving linear systems with representative dimensions, and preliminary results show that the efficiency gains also propagate to the numerical models for fluid dynamics.