UET scheduling with unit interprocessor communication delays
Discrete Applied Mathematics
Scheduling precedence graphs in systems with interprocessor communication times
SIAM Journal on Computing
Automatic determination of grain size for efficient parallel processing
Communications of the ACM - Special issue: multiprocessing
ANDES: evaluating mapping strategies with synthetic programs
Journal of Systems Architecture: the EUROMICRO Journal
Benchmarking and comparison of the task graph scheduling algorithms
Journal of Parallel and Distributed Computing
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Partitioning and Scheduling Parallel Programs for Multiprocessors
Partitioning and Scheduling Parallel Programs for Multiprocessors
Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences
Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences
DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors
IEEE Transactions on Parallel and Distributed Systems
A New Clustering Algorithm for Large Communication Delays
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Efficient Techniques for Clustering and Scheduling onto Embedded Multiprocessors
IEEE Transactions on Parallel and Distributed Systems
Task Scheduling for Parallel Systems (Wiley Series on Parallel and Distributed Computing)
Task Scheduling for Parallel Systems (Wiley Series on Parallel and Distributed Computing)
Scheduling with uncertainties on new computing platforms
Computational Optimization and Applications
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In modern parallel and distributed systems, the time for exchanging data is usually larger than that for computing elementary operations. Consequently, these communications slow down the execution of the application scheduled on such systems. Accounting for these communications is essential for attaining efficient hardware and software utilization. Therefore, we provide in this paper a new combined approach for scheduling parallel applications with large communication delays on an arbitrary number of processors. In this approach, a genetic algorithm is improved with the introduction of some extra knowledge about the scheduling problem. This knowledge is represented by a class of clustering algorithms introduced recently, namely, convex clusters which are based on structural properties of the parallel applications. The developed algorithm is assessed by simulations run on some families of synthetic task graphs and randomly generated applications. The comparison with related approaches emphasizes its interest.