CCF: SHF: EAGER: Collaborative: Asynchronous Algorithms for Exascale Computing Systems

CCF：SHF：EAGER：协作：百亿亿次计算系统的异步算法

• 批准号: 1349017
• 负责人: Jonathan Rogers
• 金额: $ 1万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1349017&HistoricalAwards=false
• 关键词: CCF; SHF; EAGER; Collaborative; Asynchronous

Future computing systems will allow computations to be performed simultaneously with massive parallelization. However, current practices in scientific simulations cannot utilize the maximum potential of these machines. This is fundamentally due to the need for data synchronization across computing cores, which may cause up to 80% idling of the machines, and there is a need for new methods to overcome this inefficiency.This research is to develop a new framework for high performance computing where synchronization across the processing elements is relaxed. This will eliminate the overhead associated with extreme parallelism and potentially lay the foundation for simulations at scale. The framework is based on asynchronous model of computation for high performance computing to better utilize future systems. The price to pay for asynchrony is poor predictability of the code, resulting in uncertainty in the calculations. The central theme of this project is to accurately quantify this induced uncertainty and develop techniques to mitigate it. Specific research thrusts include: i) study of numerical stability, consistency and accuracy of widely used numerical algorithms, in the context of fluid-flow, under asynchronous conditions; ii) development of new schemes which can maintain its accuracy under asynchronous conditions; iii) determination of efficient implementations of the resulting algorithms on current and future systems. These research goals are addressed in a dynamical systems framework. The behavior of asynchronous numerical algorithms is modeled as Markov jump systems. Issues related to consistency, numerical stability and error control is addressed by using tools for analysis and design of Markov jump systems.

未来的计算系统将允许计算与大规模并行化同时执行。然而，目前的科学模拟实践不能利用这些机器的最大潜力。这从根本上是由于需要跨计算核心的数据同步，这可能会导致高达80%的机器空闲，并且需要新的方法来克服这种低效率。本研究旨在开发一种新的高性能计算框架，其中跨处理元件的同步是放松的。这将消除与极端并行性相关的开销，并可能为大规模模拟奠定基础。该框架是基于异步计算模型的高性能计算，以更好地利用未来的系统。 代码的可预测性很差，导致计算中的不确定性。该项目的中心主题是准确地量化这种不确定性并开发减轻这种不确定性的技术，具体的研究方向包括：i）在流体流动的背景下，在异步条件下研究广泛使用的数值算法的数值稳定性、一致性和精度; ii）开发在异步条件下能够保持其精度的新格式; iii）确定所得到的算法在当前和未来系统上的有效实现。这些研究目标是在一个动态系统的框架。异步数值算法的行为被建模为马尔可夫跳变系统。通过使用马尔可夫跳跃系统的分析和设计工具，解决了与一致性，数值稳定性和误差控制有关的问题。