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

未来的计算系统将允许计算与大规模并行同时执行。然而，目前的科学模拟实践不能最大限度地利用这些机器的潜力。这从根本上是因为需要跨计算核心进行数据同步，这可能会导致高达80%的机器空闲，因此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，其中跨处理元素的同步被放松。这将消除与极端并行性相关的开销，并潜在地为大规模模拟奠定基础。该框架基于高性能计算的异步计算模型，以更好地利用未来的系统。异步化的代价是代码的可预测性很差，导致计算中的不确定性。这个项目的中心主题是准确地量化这种引起的不确定性，并开发技术来减轻它。具体的研究工作包括：i)在流体流动的背景下，研究在非同步条件下广泛使用的数值算法的数值稳定性、一致性和精度；ii)开发能够在非同步条件下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施。这些研究目标是在一个动力系统框架内解决的。将异步数值算法的行为建模为马尔可夫跳跃系统。利用马尔可夫跳跃系统的分析和设计工具，研究了马尔可夫跳跃系统的一致性、数值稳定性和误差控制问题。

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.