Taming parallel I/O complexity with auto-tuning

Author:

Behzad Babak1,Luu Huong Vu Thanh1,Huchette Joseph2,Byna Surendra3,Prabhat 3,Aydt Ruth4,Koziol Quincey4,Snir Marc1

Affiliation:

1. University of Illinois at Urbana-Champaign

2. Rice University

3. Lawrence Berkeley National Laboratory

4. The HDF Group

Funder

National Science Foundation

Publisher

ACM

Cited by 46 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. Adapting Multi-objectivized Software Configuration Tuning;Proceedings of the ACM on Software Engineering;2024-07-12

2. MMO: Meta Multi-Objectivization for Software Configuration Tuning;IEEE Transactions on Software Engineering;2024-06

3. A2FL: Autonomous and Adaptive File Layout in HPC through Real-time Access Pattern Analysis;2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2024-05-27

4. Application-Agnostic Auto-Tuning of Open MPI Collectives Using Bayesian Optimization;2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2024-05-27

5. Concealing Compression-accelerated I/O for HPC Applications through In Situ Task Scheduling;Proceedings of the Nineteenth European Conference on Computer Systems;2024-04-22

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