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Title: Optimizing Asynchronous Multi-Level Checkpoint/Restart Configurations with Machine Learning
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 multilevel checkpointing efficiently, it is important to optimize checkpoint/restart configurations. Current approaches, namely modeling 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.  more » « less
Award ID(s):
1763547 1744336 1822737 1564647 1561041
NSF-PAR ID:
10156303
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
The IEEE International Workshop on High-Performance Storage
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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