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UID:submissions.supercomputing.org_SC24_sess394_pap699@linklings.com
SUMMARY:AutoCheck: Automatically Identifying Variables for Checkpointing b
 y Data Dependency Analysis
DESCRIPTION:Xiang Fu, Weiping Zhang, Xin Huang, Wubiao Xu, and Shiman Meng
  (Nanchang Hangkong University); Luanzheng Guo (Pacific Northwest National
  Laboratory (PNNL)); and Kento Sato (RIKEN)\n\nCheckpoint/Restart (C/R) ha
 s been widely deployed in numerous HPC systems, Clouds, and industrial dat
 a 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 i
 dentify those critical variables accounted for correct application executi
 on restoration in a failure for C/R. To address this problem, we propose a
 n analytical model and a tool (AutoCheck) that can automatically identify 
 critical variables to checkpoint for C/R. AutoCheck relies on first, analy
 tically tracking and optimizing data dependency between variables and othe
 r application execution state, and second, a set of heuristics that identi
 fy critical variables for checkpointing from the refined data-dependency g
 raph (DDG). AutoCheck allows programmers to pinpoint critical variables to
  checkpoint quickly within a few minutes. We evaluate AutoCheck on 13 repr
 esentative HPC benchmarks, demonstrating that AutoCheck can efficiently id
 entify correct critical variables to checkpoint.\n\nTag: Cloud Computing, 
 Fault-Tolerance, Reliability, Maintainability, and Adaptability, Heterogen
 eous Computing, Performance Optimization, Resource Management, State of th
 e Practice\n\nRegistration Category: Tech Program Reg Pass\n\nSession Chai
 r: Silvina Caino-Lores (National Institute for Research in Digital Science
  and Technology (Inria))\n\n
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