Checkpoint-Restart Simulator
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 check-point/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.
- Tonmoy Dey, Kento Sato, Bogdan Nicolae, Jian Guo, Jens Domke, Weikuan Yu, Franck Cappello, and Kathryn Mohror. “Optimizing Asynchronous Multi-Level Checkpoint/Restart Configurations with Machine Learning.” The IEEE International Workshop on High-Performance Storage, May, 2020
- Tonmoy Dey, Kento Sato, Jian Guo, Bogdan Nicolae, Jens Domke, Weikuan Yu, Franck Cappello and Kathryn Mohror, “Optimizing Asynchronous Multi-level Checkpoint/Restart Configurations with Machine Learning”, In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis 2019 (SC19), Regular Poster, Denver, USA, Nov, 2019.