AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis
<|reference_start|>AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis: Checkpoint/Restart (C/R) has been widely deployed in numerous HPC systems, Clouds, and industrial data centers, which are typically operated by system engineers. Nevertheless, there is no existing approach that helps system engineers without domain expertise, and domain scientists without system fault tolerance knowledge identify those critical variables accounted for correct application execution restoration in a failure for C/R. To address this problem, we propose an analytical model and a tool (AutoCheck) that can automatically identify critical variables to checkpoint for C/R. AutoCheck relies on first, analytically tracking and optimizing data dependency between variables and other application execution state, and second, a set of heuristics that identify critical variables for checkpointing from the refined data dependency graph (DDG). AutoCheck allows programmers to pinpoint critical variables to checkpoint quickly within a few minutes. We evaluate AutoCheck on 14 representative HPC benchmarks, demonstrating that AutoCheck can efficiently identify correct critical variables to checkpoint.<|reference_end|>
@article{fu2024autocheck:,
    title={AutoCheck: Automatically Identifying Variables for Checkpointing by Data
  Dependency Analysis},
    author={Xiang Fu (Nanchang Hangkong University), Weiping Zhang (Nanchang
  Hangkong University), Xin Huang (Nanchang Hangkong University), Wubiao Xu
  (Nanchang Hangkong University), Shiman Meng (Nanchang Hangkong University),
  Luanzheng Guo (Pacific Northwest National Laboratory), Kento Sato (R-CCS,
  RIKEN)},
    journal={arXiv preprint arXiv:2408.06082},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06082},
    primaryClass={cs.SE}
}