Dynamic Domain Decomposition for a Parallel 3-D Electrostatic PIC/MCC Code using Unstructured Mesh

Abstract

Summary form only given. In the past, there have been very few studies concentrating on developing dynamic load-balancing technique for particle-based PIC/MCC code. The only exception to the authors' best knowledge is the work by Seidel et al., in which the method dynamically repartitioned the computational domain and intended to balance the workload among processors under the framework of structured mesh. However, there are several disadvantages in using structured mesh. For example, it becomes rather cumbersome to treat the boundary conditions if complicated geometry is involved. In the current paper, method of dynamic domain decomposition (DDD), based on multi-level graph-partitioning technique, for a parallel 3D electrostatic PIC/MCC code that utilized unstructured tetrahedral mesh is proposed and verified. This parallel code is designed to run on memory-distributed machine with standard MPI protocol. In a typical particle simulation, for example, DSMC, the computational weight (~actual computational time) is generally in proportion to the number of particles. In PIC/MCC method, weight for each graph vertex (or cell center) is instead a combination of particle computational weight and cell computational weight. The computational weight of the former results from time required for the particle push and Monte Carlo collisions, while the latter results from the time required for solving the Poisson's equation. A preliminary simulation can determine the computational weights of particle and cell, respectively. A quasi-1D argon gas discharge with application of 1000 V difference at 42 mtorr is used as the test case for studying the performance of DDD. Strategy of deciding when to repartition the domain and resulting parallel performance is discussed in the meeting. In addition, the contribution to the total computational time from actual computation, communication and repartition is also discussed by emphasizing their impact to the resulting parallel performance