A Comprehensive I/O Knowledge Cycle for Modular and Automated HPC Workload Analysis
Author:
Affiliation:
1. Institute of Computer Science, Goethe University Frankfurt,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9912642/9912651/09912689.pdf?arnumber=9912689
Cited by 6 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. Tarazu: An Adaptive End-to-end I/O Load-balancing Framework for Large-scale Parallel File Systems;ACM Transactions on Storage;2024-04-04
2. Characterization of Large-scale HPC Workloads with non-naïve I/O Roofline Modeling and Scoring;2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS);2023-12-17
3. Optimizing HPC I/O Performance with Regression Analysis and Ensemble Learning;2023 IEEE International Conference on Cluster Computing (CLUSTER);2023-10-31
4. Modeling the Impact of System-Level Parameters on I/O Performance of HPC Applications;2023 31st International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS);2023-10-16
5. An Empirical Roofline Model for Extreme-Scale I/O Workload Analysis;2023 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW);2023-05
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