A Partitioning Strategy for Nonuniform Problems on Multiprocessors
IEEE Transactions on Computers
Nearest-neighbor mapping of finite element graphs onto processor meshes
IEEE Transactions on Computers
Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
A run-time load balancing strategy for highly parallel systems
Acta Informatica
Mapping unstructured grid computations to massively parallel computers
Mapping unstructured grid computations to massively parallel computers
Load balancing and communication support for irregular problems
Load balancing and communication support for irregular problems
Rectilinear partitioning of irregular data parallel computations
Journal of Parallel and Distributed Computing
Partitioning & Mapping of Unstructured Meshes to Parallel Machine Topologies
IRREGULAR '95 Proceedings of the Second International Workshop on Parallel Algorithms for Irregularly Structured Problems
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This paper introduces an infrastructure for parallel mesh computations running on distributed-memory computers. The infrastructure consists of the mesh partitioning algorithm GARP and the domain-specific communication library GRAPHlib. Unlike existing algorithms, GARP exploits geometrical properties of the mesh shape in order to produce shape-adequate rectilinear partitions. The structure of such partitions is exploited by GRAPHlib using an optimized message ordering strategy. We describe the concepts behind GARP and GRAPHlib and show that for meshes with particular shapes our infrastructure provides better utilization of the parallel computer than solutions using existing partitioning algorithms and communication libraries.