The relative performance of various mapping algorithms is independent of sizable variances in run-time predictions
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Summary
The author studies the performance of four mapping algorithms and concludes that the use of intelligent mapping algorithms is beneficial, even when the expected time for completion of a job is not deterministic.
- Type
- article
- Published
- 1998-03-30
- Cited by
- 300
- References
- 77
- Access
- Open access
- OpenAlex
- https://openalex.org/W1784710995
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:1460003
Keywords
Computer science, Greedy algorithm, Algorithm
References
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- Roundhouse a security architecture for active networks
- High Assurance Multilevel Services For Off-The-Shelf Workstation Applications
- Why the mean is inadequate for accurate scheduling decisions
- SmartNet: a scheduling framework for heterogeneous computing
- The Effects of the Interface on Navigation in Virtual Environments
- An Introduction to Object-Oriented Programming With Java
- Task Matching and Scheduling in Heterogenous Computing Environments Using a Genetic-Algorithm-Based Approach
- The Prospero Resource Manager: A scalable framework for processor allocation in distributed systems
- Secure information flow in a multi-threaded imperative language
- Confinement properties for programming languages
- Master of science in software engineering via distance learning
- Distributed And Parallel Computing
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- Effcient Scheduling Heuristics for Independent Tasks in Computational Grids
- A case-based recommender for task assignment in heterogeneous computing systems
- A novel approach towards improving performance of load balancing using Genetic Algorithm in cloud computing
- Evaluation of a heuristic approach for efficient scheduling of residential smart home appliances
- A Primarily Survey on Energy Efficiency in Cloud and Distributed Computing Systems
- Intelligent strategies for DAG scheduling optimization in Grid environments
- Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm
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- Biologically inspired, self organizing communication networks
- Utilization-Based Techniques for Statically Mapping Heterogeneous Applications onto the HiPer-D Heterogeneous Computing System
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