XPS: FULL: Integrating Programming Model, Runtime, Algorithmic, and Architectural Support To Use Inexact and Heterogeneous Hardware for Scientific Computations

XPS：完整：集成编程模型、运行时、算法和架构支持，以使用不精确和异构硬件进行科学计算

• 批准号: 1629392
• 负责人: Radu Teodorescu
• 金额: $ 87.5万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629392&HistoricalAwards=false
• 关键词: XPS; FULL; Integrating; Programming; Model

As the High Performance Computing (HPC) field moves towards even more powerful (Exascale) systems, it faces two key challenges: resilience and power efficiency. Increasing computing power while not significantly increasing the power budget potentially involves new architectural designs, such as the ones with low power and/or low margins. Though future systems are expected to experience an increase in the number of faults (because of increasing number of cores and decreasing feature sizes), energy efficient designs will likely suffer even more errors due to tighter margins and other design compromises. Of particular concern are the errors that escape hardware detection: such errors are said to cause Silent Data Corruption (SDC). Maintaining correctness of a numerical simulation in the presence of SDCs is a very challenging problem. The intellectual merit of this project is in combining ideas from numerical methods, programming model design and architecture design to address the correctness of numerical simulations. The broader significance and importance of the project includes impact on scientific and high performance computing, system software for parallel computing, and architectural designs and research. This project will also make several contributions towards education, human resource development, and increasing diversity, with activities like teaching parallel computing (and programming) to diverse audience, mentoring of doctoral students, including those from underrepresented groups, and an interdisciplinary training program in Mathematical Biology for undergraduates. Technically, the project addresses the challenge of developing and executing scientific applications with energy efficient low-power/margin architectures that experience occasional faults, while maintaining programmer productivity and accuracy of results. This project develops a synergistic research program combining advances in HPC programming models, runtime systems, Near Threshold Voltage (NTV) architectures, and numerical methods (algorithms). Specifically, the project involves close collaboration between researchers from three areas: (a) parallel programming models, applications, and runtime systems, (b) architecture, and (c) finite difference and finite volume numerical models.

随着高性能计算（HPC）领域向更强大的（Exascale）系统发展，它面临着两个关键挑战：弹性和能效。增加计算能力而不显著增加功率预算潜在地涉及新的架构设计，诸如具有低功率和/或低裕度的架构设计。虽然未来的系统预计会遇到更多的故障（由于核心数量的增加和特征尺寸的减小），但由于更严格的裕度和其他设计妥协，节能设计可能会遭受更多的错误。特别值得关注的是逃避硬件检测的错误：据说这种错误会导致静默数据损坏（SDC）。 在SDC存在的情况下保持数值模拟的正确性是一个非常具有挑战性的问题。 该项目的智力价值在于将数值方法、编程模型设计和架构设计的思想结合起来，以解决数值模拟的正确性。该项目更广泛的意义和重要性包括对科学和高性能计算、并行计算系统软件以及架构设计和研究的影响。该项目还将对教育、人力资源开发和增加多样性作出若干贡献， 这些活动包括向不同的受众教授并行计算（和编程），指导博士生，包括来自代表性不足的群体的博士生，以及为本科生提供数学生物学的跨学科培训计划。 从技术上讲，该项目解决了开发和执行科学应用程序的挑战，具有节能的低功耗/利润架构，偶尔会遇到故障，同时保持程序员的生产力和结果的准确性。该项目开发了一个协同研究计划，结合了HPC编程模型，运行时系统，近阈值电压（NTV）架构和数值方法（算法）的进步。具体而言，该项目涉及三个领域的研究人员之间的密切合作：（a）并行编程模型，应用程序和运行时系统，（B）架构，（c）有限差分和有限体积数值模型。

项目成果

Using Undervolting as an on-Device Defense Against Adversarial Machine Learning Attacks

• DOI: 10.1109/host49136.2021.9702287
• 发表时间: 2021-07
• 期刊: 2021 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)
• 作者: Saikat Majumdar;Mohammad Hossein Samavatian;Kristin Barber;R. Teodorescu