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

随着高性能计算（HPC）领域向更强大的系统（Exascale）发展，它面临着两个关键挑战：弹性和功率效率。在不显著增加功耗预算的情况下提高计算能力可能涉及新的架构设计，例如低功耗和/或低利润率的架构设计。尽管未来的系统预计会经历故障数量的增加（因为内核数量的增加和特征尺寸的减小），但由于更紧凑的余量和其他设计妥协，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：这种错误被称为导致静默数据损坏（SDC）。在存在SDCs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。该项目的智力优势在于将数值方法、编程模型设计和体系结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建筑设计和研究的影响。该项目还将为教育、人力资源开发和增加多样性做出贡献，包括向不同受众教授并行计算（和编程）、指导博士生（包括来自代表性不足群体的博士生）以及为本科生提供数学生物学跨学科培训计划。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，这些应用程序具有高能效、低功耗/边际架构（偶尔会出现故障），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，结合了高性能计算编程模型、运行时系统、近阈值电压（NTV）架构和数值方法（算法）方面的进展。具体来说，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，以及(c)有限差分和有限体积数值模型。

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.