An Active Learning Method for Empirical Modeling in Performance Tuning
Author:
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9136850/9139768/09139798.pdf?arnumber=9139798
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Automatic Multi-Parameter Performance Modeling of HPC Applications on a New Sunway Supercomputer;IEEE Transactions on Parallel and Distributed Systems;2023-11
2. Performance Tuning for GPU-Embedded Systems: Machine-Learning-Based and Analytical Model-Driven Tuning Methodologies;2023 IEEE 35th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD);2023-10-17
3. Facilitating hardware-aware neural architecture search with learning-based predictive models;Journal of Systems Architecture;2023-04
4. ML-based Performance Portability for Time-Dependent Density Functional Theory in HPC Environments;2022 IEEE/ACM International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS);2022-11
5. TPUPoint: Automatic Characterization of Hardware-Accelerated Machine-Learning Behavior for Cloud Computing;2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS);2021-03
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