SHF:Medium:Collaborative Research:A comprehensive methodology to pursue reproducible accuracy in ensemble scientific simulations on multi- and many-core platforms

SHF：中：协作研究：在多核和众核平台上追求集合科学模拟的可重复精度的综合方法

• 批准号: 1841552
• 负责人: Michela Taufer
• 金额: $ 15.73万
• 依托单位: University of Tennessee Knoxville
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841552&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; comprehensive

Ensemble simulations of scientific phenomena typically run for weeks or even months on high-performance computing clusters. The already high level of concurrency of these computing environments is expected to significantly increase in the near future, causing simulations to suffer not only from numerical errors due to limited arithmetic precision but also from the non-determinism in the execution associated with multithreading. Ultimately this trend can compromise the simulation results and break the scientific community's trust in ensemble simulations. This project tackles this problem and defines a methodology to enable the reproducible accuracy of large ensemble simulations on exascale platforms that include multi- and many-core processors. This project moves along two major fronts. First, the investigators identify common sources of accuracy errors and study their accumulation, propagation, and runtime effects in a controlled environment. This phase includes three research activities: (i) generating code motifs that model those computations that may lead to accuracy errors; (ii) providing multiple implementations of these motifs, called code inspectors, targeting different parallel platforms; and (iii) evaluating the accuracy and runtime of these implementations using a variety of datasets and stress conditions. Second, by installing these code inspectors in real scientific code bases, the investigators study their behavior in uncertain environments. This phase includes two research activities: (i) prioritizing code segments based on quantitative impact scores and matching segments to inspector motifs; and (ii) finding the optimal code inspector implementations and patching the code with them so as to optimize the overall result variance. The applications targeted in this project are deterministic chaotic applications including n-body atomic system simulations and astrophysical simulations.

科学现象的集合模拟通常在高性能计算集群上运行数周甚至数月。这些计算环境已经很高的并发性在不久的将来会显着增加，这会导致模拟不仅因算术精度有限而遭受数值错误，而且会遭受与多线程相关的执行中的非确定性。最终，这种趋势可以损害模拟结果，并打破科学界对集合模拟的信任。该项目解决了此问题，并定义了一种方法，以使大型集合模拟在包括多核处理器在内的Exascale平台上的可重复准确性。 该项目沿两个主要战线移动。首先，研究人员确定了准确性错误的常见来源，并研究其在受控环境中的积累，传播和运行时效应。此阶段包括三个研究活动：（i）生成代码图案，以建模可能导致准确性错误的计算； （ii）提供这些图案的多个实现，称为代码检查员，以不同的并行平台为目标； （iii）使用各种数据集和应力条件评估这些实现的准确性和运行时间。其次，通过将这些代码检查员安装在实际的科学代码库中，研究人员在不确定的环境中研究了其行为。此阶段包括两项研究活动：（i）根据定量影响得分和匹配范围与检查员图案相匹配的代码段的优先级； （ii）找到最佳代码检查器实现并使用它们修补代码，以优化总体结果差异。该项目中针对的应用程序是确定性混乱应用，包括N体原子系统模拟和天体物理模拟。