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Start Over Descriptor "I.6.3" Publisher ieee
2 results on '"I.6.3"'

1. Bayesian Optimization for auto-tuning GPU kernels

Author

Floris-Jan Willemsen, Ben Van Werkhoven, Rob Van Nieuwpoort, and Multiscale Networked Systems (IvI, FNWI)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, D.1.3, Computer Science - Performance, C.4, I.6.3, G.3, Machine Learning (cs.LG), Performance (cs.PF), Computer Science - Distributed, Parallel, and Cluster Computing, Optimization and Control (math.OC), FOS: Mathematics, Distributed, Parallel, and Cluster Computing (cs.DC), Mathematics - Optimization and Control
Abstract

Finding optimal parameter configurations for tunable GPU kernels is a non-trivial exercise for large search spaces, even when automated. This poses an optimization task on a non-convex search space, using an expensive to evaluate function with unknown derivative. These characteristics make a good candidate for Bayesian Optimization, which has not been applied to this problem before. However, the application of Bayesian Optimization to this problem is challenging. We demonstrate how to deal with the rough, discrete, constrained search spaces, containing invalid configurations. We introduce a novel contextual variance exploration factor, as well as new acquisition functions with improved scalability, combined with an informed acquisition function selection mechanism. By comparing the performance of our Bayesian Optimization implementation on various test cases to the existing search strategies in Kernel Tuner, as well as other Bayesian Optimization implementations, we demonstrate that our search strategies generalize well and consistently outperform other search strategies by a wide margin., In context of the 2021 International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) at SuperComputing 2021. Pending publication in IEEE Computer Society Technical Consortium on High Performance Computing (TCHPC)

Published
2021

2. Mitigating Multi-Stage Cascading Failure by Reinforcement Learning

Author

Yongli Zhu and Chengxi Liu

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, 020209 energy, Collapse (topology), Machine Learning (stat.ML), Systems and Control (eess.SY), Dynamical Systems (math.DS), 02 engineering and technology, Electrical Engineering and Systems Science - Systems and Control, Convolutional neural network, Machine Learning (cs.LG), Statistics - Machine Learning, Power system faults, Reinforcement learning, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Mathematics - Dynamical Systems, I.2.1, I.2.8, I.6.3, business.industry, Deep learning, Load flow, Cascading failure, Multi stage, Key (cryptography), Deep neural networks, 020201 artificial intelligence & image processing, Power system protection, Artificial intelligence, business, Games, Neural networks
Abstract

This paper proposes a cascading failure mitigation strategy based on Reinforcement Learning (RL) method. Firstly, the principles of RL are introduced. Then, the Multi-Stage Cascading Failure (MSCF) problem is presented and its challenges are investigated. The problem is then tackled by the RL based on DC-OPF (Optimal Power Flow). Designs of the key elements of the RL framework (rewards, states, etc.) are also discussed in detail. Experiments on the IEEE 118-bus system by both shallow and deep neural networks demonstrate promising results in terms of reduced system collapse rates., This paper has been accepted and presented in the IEEE ISGT-Asia conference in 2019

Published
2019

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