Application Robustification

应用程序强化

• 批准号: 1118391
• 负责人: Rakesh Kumar
• 金额: $ 9.86万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1118391&HistoricalAwards=false
• 关键词: Application; Robustification

All of computing today relies on an abstraction where software expects the hardware to behave flawlessly for all inputs under all conditions. However, for emerging circuits/devices, the cost of maintaining the abstraction of flawless hardware will be prohibitive due to variations and we may need to rethink the correctness contract between hardware and software. The primary focus of the project is application robustification ? fundamental algorithmic methodologies to transform arbitrary applications such that they can continue to make forward progress in spite of errors produced by the hardware. In this project, our preliminary research effort is focused on a) techniques to convert different classes of application kernels into robust, efficiently solvable stochastic optimization problems that can tolerate hardware errors, b) techniques based on Krylov subspace methods, gradient projection, quasi-Newton approaches, stochastic approximation theory-based approaches, preconditioning techniques, and intelligent step sizing to reduce the cost of robustness for different forms of hardware variations, and c) low overhead checksum-based techniques robustifying sparse linear algebra libraries and graph algorithms. Broader impact of this project includes development of a potentially promising approach to ride Moore's Law and training students in both the hardware and software aspects of computing in face of errors. Broader education will also be achieved through research artifacts (e.g., library of error tolerant kernels) that will be made available for research and education.

今天所有的计算都依赖于一种抽象，即软件期望硬件在所有条件下的所有输入都能完美地运行。然而，对于新兴的电路/设备，由于各种变化，维护完美硬件抽象的成本将令人望而却步，我们可能需要重新考虑硬件和软件之间的正确性契约。该项目的主要焦点是应用程序健壮性。转换任意应用程序的基本算法方法，使它们能够在硬件产生错误的情况下继续向前发展。在这个项目中，我们的初步研究工作集中在a)将不同类别的应用核转换为可容忍硬件错误的鲁棒的、可有效解决的随机优化问题的技术，b)基于Krylov子空间方法、梯度投影、准牛顿方法、基于随机逼近理论的方法、预处理技术，智能步长以减少不同形式硬件变化的鲁棒性成本，以及c)低开销的基于校验和的技术，增强稀疏线性代数库和图算法的鲁棒性。这个项目更广泛的影响包括开发一种潜在的有前途的方法来驾驭摩尔定律，并在面对错误的计算的硬件和软件方面训练学生。更广泛的教育也将通过研究工件（例如，容错内核库）来实现，这些工件将用于研究和教育。