Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Heuristic Technique for Processor and Link Assignment in Multicomputers
IEEE Transactions on Computers
List scheduling with and without communication delays
Parallel Computing
PMRSB: parallel multilevel recursive spectral bisection
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
An object-based infrastructure for program monitoring and steering
SPDT '98 Proceedings of the SIGMETRICS symposium on Parallel and distributed tools
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
A Generalized Scheme for Mapping Parallel Algorithms
IEEE Transactions on Parallel and Distributed Systems
DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors
IEEE Transactions on Parallel and Distributed Systems
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
IEEE Transactions on Computers
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High performance applications and the underlying hardware platforms are becoming increasingly dynamic; runtime changes in the behavior of both are likely to result in inappropriate mappings of tasks to parallel machines during application execution. This fact is prompting new research on mapping and scheduling the dataflow graphs that represent parallel applications. In contrast to recent research which focuses on critical paths in dataflow graphs, this paper presents new mapping methods that compute near-min-cut partitions of the dataflow graph. Our methods deliver mappings that are an order of magnitude more efficient than those of DSC, a state-of-the-art critical-path algorithm, for sample high performance applications.