检查用于检查点的变量
Scrutinizing Variables for Checkpoint Using Automatic Differentiation
AI总结：
本文提出利用自动微分技术识别检查点中关键与非关键变量元素，以提高存储效率。
摘要：
检查点/重启（C/R）定期保存程序的运行状态，这会消耗大量的系统资源。我们观察到，在典型的HPC应用中，并不是所有的数据都参与计算；那些未使用的数据应从检查点中排除，以获得更好的存储/计算效率。为此，我们提出了一种系统的方法，利用自动微分（AD）来检查每个变量（例如数组）中的元素，以确定哪些元素是关键的或不重要的，从而将不重要的元素排除在检查点之外。具体来说，我们使用AD工具检查每个变量中的每个元素，以确定该元素是否对应用程序输出有影响。我们通过八个来自NAS并行基准（NPB）套件的基准测试来验证我们的方法。我们成功地可视化了变量中关键/不重要的元素/区域，以确定其对应用程序输出的影响（是或否）。我们发现关键/不重要元素/区域的模式/分布相当有趣，并遵循算法的物理公式/逻辑。对NPB基准测试的评估表明，我们的方法在检查点存储方面节省了高达20%的空间。
英文摘要：
Checkpoint/Restart (C/R) saves the running state of the programs periodically, which consumes considerable system resources. We observe that not every piece of data is involved in the computation in typical HPC applications; such unused data should be excluded from checkpointing for better storage/compute efficiency. To find out, we propose a systematic approach that leverages automatic differentiation (AD) to scrutinize every element within variables (e.g., arrays) for checkpointing allowing us to identify critical/uncritical elements and eliminate uncritical elements from checkpointing. Specifically, we inspect every single element within a variable for checkpointing with an AD tool to determine whether the element has an impact on the application output or not. We empirically validate our approach with eight benchmarks from the NAS Parallel Benchmark (NPB) suite. We successfully visualize critical/uncritical elements/regions within a variable with respect to its impact (yes or no) on the application output. We find patterns/distributions of critical/uncritical elements/regions quite interesting and follow the physical formulation/logic of the algorithm.The evaluation on NPB benchmarks shows that our approach saves storage for checkpointing by up to 20%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
