A Parallel Aggregation Algorithm for Inter-Grid Transfer Operators in Algebraic Multigrid
Garcia Hilares_NA_T_2019_Fair Use Evaluation Documentation - Exposing fine-grained parallelism in algebraic multigrid methods.pdf (130.9Kb)
Garcia Hilares_NA_T_2019_Fair Use Evaluation Documentation - Iterative Methods for Sparse Linear Systems.pdf (129.3Kb)
Garcia Hilares_NA_T_2019_Fair Use Evaluation Documentation - The Theory and Practice of Algebraic Multigrid Methods.pdf (129.3Kb)
Garcia Hilares, Nilton Alan
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As finite element discretizations ever grow in size to address real-world problems, there is an increasing need for fast algorithms. Nowadays there are many GPU/CPU parallel approaches to solve such problems. Multigrid methods can be used to solve large-scale problems, or even better they can be used to precondition the conjugate gradient method, yielding better results in general. Capabilities of multigrid algorithms rely on the effectiveness of the inter-grid transfer operators. In this thesis we focus on the aggregation approach, discussing how different aggregation strategies affect the convergence rate. Based on these discussions, we propose an alternative parallel aggregation algorithm to improve convergence. We also provide numerous experimental results that compare different aggregation approaches, multigrid methods, and conjugate gradient iteration counts, showing that our proposed algorithm performs better in serial and parallel.
General Audience Abstract
Modeling real-world problems incurs a high computational cost because these mathematical models involve large-scale data manipulation. Thus we need fast and efficient algorithms. Nowadays there are many high-performance approaches for these problems. One such method is called the Multigrid algorithm. This approach models a physical domain using a hierarchy of grids, and so the effectiveness of these approaches relies on how well data can be transferred from grid to grid. In this thesis, we focus on the aggregation approach, which clusters a grid’s vertices according to its connections. We also provide an alternative parallel aggregation algorithm to give a faster solution. We show numerous experimental results that compare different aggregation approaches and multigrid methods, showing that our proposed algorithm performs better in serial and parallel than other popular implementations.
- Masters Theses