We consider the problem of mapping large scale FEM graphs to highly parallel distributed memory computers. Typically, these programs show a low-dimensional grid-like communication structure. We argue that conventional domain decomposition methods that are usually employed today are not well suited for future highly parallel computers as they do not take into account the interconnection structure of the parallel computer resulting in a large communication overhead. Therefore we propose a new mapping heuristic which performs both, partitioning of the solution domain and processor allocation in one integrated step. Our procedure is based on the ability of Kohonen neural networks to exploit topological similarities of an input space and a grid-like structured network: to complete a neighbourhood preserving mapping between the set of discretization points and the parallel computer.
Index Terms:
finite element analysis; self-organising feature maps; distributed memory systems; parallel architectures; resource allocation; parallel algorithms; graph theory; FEM-graph partitioning; self-organization; FEM-graph mapping; highly parallel distributed memory computers; low-dimensional grid-like communication structure; domain decomposition methods; highly parallel computers; interconnection structure; communication overhead; mapping heuristic; processor allocation; Kohonen neural networks; topological similarities; input space; grid-like structured network; neighbourhood preserving mapping; discretization points; finite element method
Citation:
M. Dormanns, Hans-Ulrich Heiss, "Partitioning and mapping of large FEM-graphs by self-organization," pdp, pp.227, 3rd Euromicro Workshop on Parallel and Distributed Processing, 1995
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