Harnessing the Crowd for Autotuning High-Performance Computing Applications

Author:

Cho Younghyun1,Demmel James W.1,King Jacob2,Li Xiaoye S.3,Liu Yang3,Luo Hengrui3

Affiliation:

1. University of California,Berkeley

2. Tech-X

3. Lawrence Berkeley National Laboratory

Funder

National Energy Research Scientific Computing Center

U.S. Department of Energy

Publisher

IEEE

Reference24 articles.

1. The university of Florida sparse matrix collection

2. Google Vizier: A Service for Black-Box Optimization;golovin,0

3. A Communication-Avoiding 3D LU Factorization Algorithm for Sparse Matrices

4. Hybrid models for mixed variables in bayesian optimization;luo,2022

Cited by 3 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. Non-smooth Bayesian optimization in tuning scientific applications;The International Journal of High Performance Computing Applications;2024-09-06

2. Predicting Cross-Architecture Performance of Parallel Programs;2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2024-05-27

3. Cost-Effective Methodology for Complex Tuning Searches in HPC: Navigating Interdependencies and Dimensionality;2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2024-05-27

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