A parallel particle tracking framework for applications in scientific computing
Particle tracking methods are a versatile computational technique central to the simulation of a wide range of scientific applications. In this paper, we present a new parallel particle tracking framework for the applications of scientific computing. The framework includes the "in-element" particle tracking method, which is based on the assumption that particle trajectories are computed by problem data localized to individual elements, as well as the dynamic partitioning of particle-mesh computational systems. The ultimate goal of this research is to develop a parallel in-element particle tracking framework capable of interfacing with a different order of accuracy of ordinary differential equation (ODE) solver. The parallel efficiency of such particle-mesh systems depends on the partitioning of both the mesh elements and the particles; this distribution can change dramatically because of movement of the particles and adaptive refinement of the mesh. To address this problem we introduce a combined load function that is a function of both the particle and mesh element distributions. We present experimental results that detail the performance of this parallel load balancing approach for a three-dimensional particle-mesh test problem on an unstructured, adaptive mesh, and demonstrate the ability of interfacing with different ODE solvers.
particle tracking scientific computing three-dimensional parallel computing repartitioning strategy unstructured mesh distributed memory