Allocating Modules to Processors in a Distributed System
IEEE Transactions on Software Engineering
Optimal selection theory for superconcurrency
Proceedings of the 1989 ACM/IEEE conference on Supercomputing
Scheduling parallel program tasks onto arbitrary target machines
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
A comparison of list schedules for parallel processing systems
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
IEEE Transactions on Parallel and Distributed Systems
SmartNet: a scheduling framework for heterogeneous computing
ISPAN '96 Proceedings of the 1996 International Symposium on Parallel Architectures, Algorithms and Networks
Would You Run it Here or There? AHS: Automatic Heterogeneous Supercomputing
ICPP '93 Proceedings of the 1993 International Conference on Parallel Processing - Volume 02
Adaptable scheduling algorithm for grids with resource redeployment capability
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
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Heterogeneous computing (HC) is the coordinated use of different types of machines, networks, and interfaces in order to maximize performance and/or cost effectiveness. In recent years, research related to HC has addressed one of its most fundamental challenges: how to develop a schedule of tasks on a set of heterogeneous hosts that minimizes the time required to execute the given tasks. The development of such a schedule is made difficult by diverse processing abilities among the hosts, data and precedence dependencies among the tasks, and other factors. This paper outlines a straightforward approach to solving this problem, termed generational scheduling (GS). GS provides fast, efficient matching of tasks to hosts and requires little overhead to implement. This study introduces the GS approach and illustrates its effectiveness in terms of the time to determine schedules and the quality of schedules produced. A communication-inclusive extension of GS is presented to illustrate how GS can be used when the overhead of transferring data produced be some tasks and consumed by others is significant. Finally, to illustrate the effectiveness of GS in a real-world environment, a series of experiments are presented using GS in the SmartNet scheduling framework, developed at US Navy's facility at the Naval Command, Control, and Ocean Surveillance Center in San Diego, California.