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Computer Networks and ISDN Systems
Processor Scheduling for Linearly Connected Parallel Processors
IEEE Transactions on Computers
Framework for formulation and analysis of parallel computation structures
Parallel Computing
Interprocessor Traffic Scheduling Algorithm for Multiple-Processor Networks
IEEE Transactions on Computers
A Mapping Strategy for Parallel Processing
IEEE Transactions on Computers
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IEEE Transactions on Computers
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IEEE Transactions on Computers
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On Optimal Scheduling Algorithms for Time-Shared Systems
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Deterministic Processor Scheduling
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Medusa: an experiment in distributed operating system structure
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A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
StarOS, a multiprocessor operating system for the support of task forces
SOSP '79 Proceedings of the seventh ACM symposium on Operating systems principles
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Distributed allocation with pools of servers
PODC '82 Proceedings of the first ACM SIGACT-SIGOPS symposium on Principles of distributed computing
Real time resource allocation in distributed systems
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On the Computational Complexity of a Generalized Scheduling Problem
IEEE Transactions on Computers
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Allocating Task Interaction Graphs to Processors in Heterogeneous Networks
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Genetic Scheduling for Parallel Processor Systems: Comparative Studies and Performance Issues
IEEE Transactions on Parallel and Distributed Systems
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IEEE Parallel & Distributed Technology: Systems & Technology
Min-Cut Methods for Mapping Dataflow Graphs
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
Clustering and reassignment-based mapping strategy for message-passing architectures
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Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems
Computers and Operations Research
Automatic choice of scheduling heuristics for parallel/distributed computing
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Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems
Computers and Operations Research
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Computer Communications
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A generalized mapping strategy that uses a combination of graph theory, mathematicalprogramming, and heuristics is proposed. The authors use the knowledge from the givenalgorithm and the architecture to guide the mapping. The approach begins with agraphical representation of the parallel algorithm (problem graph) and the parallelcomputer (host graph). Using these representations, the authors generate a newgraphical representation (extended host graph) on which the problem graph is mapped.An accurate characterization of the communication overhead is used in the objectivefunctions to evaluate the optimality of the mapping. An efficient mapping scheme isdeveloped which uses two levels of optimization procedures. The objective functionsinclude minimizing the communication overhead and minimizing the total execution timewhich includes both computation and communication times. The mapping scheme istested by simulation and further confirmed by mapping a real world application ontoactual distributed environments.