A Quantitative Study of the Spatiotemporal I/O Burstiness of HPC Application

Author:

Yang Wenxiang1,Liao Xiangke1,Dong Dezun1,Yu Jie2

Affiliation:

1. College of Computer, National University of Defense Technology,Changsha,China

2. Computational Aerodynamics Institute,China Aerodynamics Research and Development Center,Mianyang,China

Publisher

IEEE

Reference28 articles.

1. Uncovering access, reuse, and sharing characteristics of i/o-intensive files on large-scale production {HPC} systems;patel;18th USENIX Conference on File and Storage Technologies ( FAST 20),2020

2. Comparative I/O workload characterization of two leadership class storage clusters

3. Server-Side Log Data Analytics for I/O Workload Characterization and Coordination on Large Shared Storage Systems

4. Spatially Bursty I/O on Supercomputers: Causes, Impacts and Solutions

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1. Mobilizing underutilized storage nodes via job path: A job-aware file striping approach;Parallel Computing;2024-09

2. Capturing Periodic I/O Using Frequency Techniques;2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2024-05-27

3. Temporal Staggering of Applications Based on Job Classification and I/O Burst Prediction;2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2022-12

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