Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Efficient search techniques—an empirical study of the N-Queens problem
IBM Journal of Research and Development
Scheduling parallel program tasks onto arbitrary target machines
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
A polynomial time algorithm for the N-Queens problem
ACM SIGART Bulletin
3,000,000 Queens in less than one minute
ACM SIGART Bulletin
Efficient local search for very large-scale satisfiability problems
ACM SIGART Bulletin
Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems
Artificial Intelligence - Special volume on constraint-based reasoning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Efficient Local Search with Conflict Minimization: A Case Study of the n-Queens Problem
IEEE Transactions on Knowledge and Data Engineering
Hypertool: A Programming Aid for Message-Passing Systems
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors
IEEE Transactions on Parallel and Distributed Systems
(R) FAST: A Low-Complexity Algorithm for Efficient Scheduling of DAGs on Parallel Processors
ICPP '96 Proceedings of the Proceedings of the 1996 International Conference on Parallel Processing - Volume 2
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Switching supports for stateful object remoting on network processors
The Journal of Supercomputing
Expert Systems with Applications: An International Journal
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Scheduling DAGs to multiprocessors is one of the key issues in high-performance computing. Local search can be used to effectively improve the quality of a scheduling algorithm. In this paper, based on topological ordering, we present a fast local search algorithm which can improve the quality of DAG scheduling algorithms. This low complexity algorithm can effectively reduce the length of a given schedule.