AutoCheck: Automatically Identifying Variables for Checkpointing by Data Dependency Analysis

TL;DR

AutoCheck is a tool that automatically identifies critical variables for checkpointing in HPC applications by analyzing data dependencies, simplifying the process for system engineers and scientists without requiring deep domain knowledge.

Contribution

It introduces an analytical model and heuristics to automatically determine variables crucial for checkpointing, improving efficiency and accessibility.

Findings

AutoCheck accurately identifies critical variables in 14 HPC benchmarks.

The tool reduces the time needed to pinpoint checkpoint variables to a few minutes.

AutoCheck demonstrates effectiveness across diverse HPC applications.

Abstract

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.