- Bruno Turcksin, Departments of Mathematics, Texas A&M University, College Station, TX
Parallelization and adaptive mesh refinement (AMR) are two techniques that can be exploited to speedup computation and to solve problems that would otherwise be inaccessible due to large memory requirements. In the case of parallelization, the speedup is obtained by partitioning the work between more processors while larger problems can be solved by having access to more memory. Meanwhile, in the case of adaptive mesh refinement, the mesh, and occasionally the polynomial order of the finite elements, is adapted to the problem to reduce the number of unknowns needed to achieve a given accuracy. This results in a smaller system to solve and a diminution of the memory required to solve a given problem. Here, the complementaries and the
difficulties of applying these two techniques simultaneously will be illustrated through examples from neutron transport using AMR with MPI and hp-FEM for Stokes problem with multithreading.
About the Speaker
Dr. Bruno Turcksin earned a Ph.D. in Nuclear Engineering from Texas A&M University in 2012. He is now a visiting assistant professor in the department of Mathematics at Texas A&M, working on the deal.II finite element library. His primary areas of expertise are numerical methods for neutron and electron transport, adaptive mesh refinement, and high performance computing.
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