A parallel log barrier-based mesh warping algorithm for distributed memory machines

作者：Panitanarak Thap^*; Shontz Suzanne M

来源：Engineering with Computers, 2018, 34(1): 59-76.

DOI：10.1007/s00366-017-0521-2

• Parallel computing
• Mesh warping
• Mesh deformation
• Log-barrier method
• Tetrahedral meshes
• Sparse linear solvers
• Multiple right-hand side problem

Parallel dynamic meshes are essential for computational simulations of large-scale scientific applications involving motion. To address this need, we propose parallel LBWARP, a parallel log barrier-based tetrahedral mesh warping algorithm for distributed memory machines. Our algorithm is a general-purpose, geometric mesh warping algorithm that parallelizes the sequential LBWARP algorithm proposed by Shontz and Vavasis. The first step of the algorithm involves computation of a set of local weights for each interior node which describe the relative distances of the node to each of its neighbors. The weight computation step is the most time consuming in the parallel algorithm. Based on our choice of the mesh partition and the corresponding distribution of data and assignment of tasks to processors, communication among processors is avoided in an embarrassingly parallel computation of the weights. Once this representation of the initial mesh is determined, a target deformation of the boundary is applied, also in an embarrassingly parallel manner. Finally, new coordinates of the interior nodes are obtained by solving a system of linear equations with multiple right-hand sides that is based on the weights and boundary deformation. This linear system can be solved using one of three parallel sparse linear solvers, i.e., the distributed block BiCG, block GMRES, or LU algorithm, all of which support the solution of linear systems with multiple right-hand side vectors. Our numerical results demonstrate good efficiency and strong scalability of parallel LBWARP on up to 64 processors, as the experiments show close to linear speedup in all cases. Weak scalability is also demonstrated. The performance of the parallel sparse linear solvers is dependent on factors such as the mesh size, the amount of available memory, and the number of processors. For example, the distributed LU algorithm gives better performance on small meshes, whereas the distributed block BiCG and distributed block GMRES algorithms yield better performance when the amount of available memory is limited. Finally, we demonstrate the parallel LBWARP performance for a sequence of mesh deformations which can significantly reduce the runtime of the overall algorithm. When applied to k deformations, parallel LBWARP reuses the weight matrix, that was computed during the first deformation, when the distributed LU linear solver is employed. This gives close to k-time performance for sufficiently many deformations.

• 出版日期2018-1