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Machine Learning Assisted HPC Workload Trace Generation for Leadership Scale Storage Systems.

, , , and . HPDC, page 199-212. ACM, (2022)

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Modeling the Impact of System-Level Parameters on I/O Performance of HPC Applications., , , and . MASCOTS, page 1-4. IEEE, (2023)An I/O Performance Evaluation of Varying CephFS Striping Patterns., , , and . CLUSTER Workshops, page 25-31. IEEE, (2023)SHADE: Enable Fundamental Cacheability for Distributed Deep Learning Training., , , , , , , and . FAST, page 135-152. USENIX Association, (2023)Analyzing File Access Patterns on Large-Scale HPC Systems: Opportunities for File Prefetching., , , , and . MASCOTS, page 1-4. IEEE, (2023)Characteristics of Deep Learning Workloads in Industry, Academic Institutions and National Laboratories., , , and . ICDCN, page 318-323. ACM, (2023)Does Varying BeeGFS Configuration Affect the I/O Performance of HPC Workloads?, , , , , , and . CLUSTER Workshops, page 5-7. IEEE, (2023)Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications., , , , , , and . CLUSTER, page 324-335. IEEE, (2022)I/O performance analysis of machine learning workloads on leadership scale supercomputer., , and . Perform. Evaluation, (2022)A Data-Centric Approach for Analyzing Large-Scale Deep Learning Applications., , , and . ICDCN, page 282-283. ACM, (2023)Machine Learning Assisted HPC Workload Trace Generation for Leadership Scale Storage Systems., , , and . HPDC, page 199-212. ACM, (2022)