Learning automata: an introduction
Learning automata: an introduction
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
PVM: Parallel virtual machine: a users' guide and tutorial for networked parallel computing
Modeling performance of heterogeneous parallel computing systems
Parallel Computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Parallel performance prediction using lost cycles analysis
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Load Balancing Highly Irregular Computations with the Adaptive Factoring
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Efficient Manipulation of Large Datasets on Heterogeneous Storage Systems
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Learning to Reach the Pareto Optimal Nash Equilibrium as a Team
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Customized dynamic load balancing for a network of workstations
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
Modeling and characterizing parallel computing performance on heterogeneous networks of workstations
SPDP '95 Proceedings of the 7th IEEE Symposium on Parallel and Distributeed Processing
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Adaptive load balancing: a study in multi-agent learning
Journal of Artificial Intelligence Research
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
Autonomous Agents and Multi-Agent Systems
Multi-resource Load Optimization Strategy in Agent-Based Systems
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
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We report on the improvements that can be achieved by applying machine learning techniques, in particular reinforcement learning, for the dynamic load balancing of parallel applications. The applications being considered in this paper are coarse grain data intensive applications. Such applications put high pressure on the interconnect of the hardware. Synchronization and load balancing in complex, heterogeneous networks need fast, flexible, adaptive load balancing algorithms. Viewing a parallel application as a one-state coordination game in the framework of multi-agent reinforcement learning, and by using a recently introduced multi-agent exploration technique, we are able to improve upon the classic job farming approach. The improvements are achieved with limited computation and communication overhead.