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Conference papers
Optimizing Asynchronous Multi-Level Checkpoint/Restart Configurations with Machine Learning
Abstract : With the emergence of versatile storage systems, multi-level checkpointing (MLC) has become a common approach to gain efficiency. However, multi-level checkpoint/restart can cause enormous I/O traffic on HPC systems. To use multi-level checkpointing efficiently, it is important to optimize check-point/restart configurations. Current approaches, namely model-ing and simulation, are either inaccurate or slow in determining the optimal configuration for a large scale system. In this paper, we show that machine learning models can be used in combination with accurate simulation to determine the optimal checkpoint configurations. We also demonstrate that more advanced techniques such as neural networks can further improve the performance in optimizing checkpoint configurations.
Cited literature [19 references]
https://hal.archives-ouvertes.fr/hal-02914478
Contributor : Bogdan Nicolae <>
Submitted on : Tuesday, August 11, 2020 - 10:18:09 PM
Last modification on : Friday, May 21, 2021 - 6:38:02 PM
Long-term archiving on: : Monday, November 30, 2020 - 6:33:20 PM
Contributor : Bogdan Nicolae <>
Submitted on : Tuesday, August 11, 2020 - 10:18:09 PM
Last modification on : Friday, May 21, 2021 - 6:38:02 PM
Long-term archiving on: : Monday, November 30, 2020 - 6:33:20 PM
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