CBMS Conference: Parallel Time Integration

该奖项为将于2020年6月1日至5日在密歇根理工大学举行的NSF-CBMS并行时间集成会议提供支持。会议的主要重点是教育和激励研究人员和学生在新的和创新的数值技术，用于现代超级计算架构上的大规模演化问题的并行时间解决方案，并刺激其分析和应用的进一步研究。它符合国家战略计算计划（NSCI）的目标：“提高建模/仿真技术与数据分析技术之间的一致性”。计算模拟是政府，工业和学术界科学研究的关键部分，补充了实验室实验和理论。计算机体系结构的变化导致未来的超级计算机将拥有数十亿个处理器，而不是今天的数百万个。然而，每个单独的处理器将不会比今天的单独处理器更快。因此，这些下一代机器将不再自动为现有的计算模拟提供加速，必须开发和部署新的数学算法，以利用这前所未有的处理器数量。时间并行方法提供了一类这样的数学算法。它们增加了一个新的并行维度（时间），从而允许现有的计算机模型扩展到下一代超级计算机。潜在的应用范围是巨大的：计算分子动力学，如蛋白质和DNA折叠，计算生物学（例如，心脏建模），计算流体动力学（例如，燃烧，气候和天气），和机器学习。会议将以并行时间积分专家甘德教授的十场讲座为特色。使用适当的数学方法，从偏微分方程理论的功能分析设置，数值离散化，集成技术，这些迭代方法的收敛性分析，与会者将接触到并行的时间方法的数值分析及其实现。提出的主题包括多重射击型方法，波形松弛方法，时间多重网格方法，和直接时间并行方法。这些讲座将提供给来自广泛学科的广大观众，包括数学，计算机科学和工程。会议网站是-http://conferences.math.mtu.edu/cbms2020/This奖反映了NSF的法定使命，并已被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

This award provides support for the NSF-CBMS Conference on Parallel Time Integration to be held on June 1-5, 2020, at Michigan Technological University. The primary focus of the conference is to educate and inspire researchers and students in new and innovative numerical techniques for the parallel-in-time solution of large-scale evolution problems on modern supercomputing architectures, and to stimulate further studies in their analysis and applications. It aligns with the National Strategic Computing Initiative (NSCI) objective: "increase coherence between technology for modeling/simulation and data analytics". Computational simulations are a key part of scientific research for government, industry, and academia, complementing laboratory experimentation and theory. Changes in computer architectures are leading to future supercomputers that will have billions of processors, as opposed to millions today. However, each individual processor will be no faster than individual processors today. Thus, these next generation machines will no longer automatically provide a speedup to existing computational simulations, and new mathematical algorithms must be developed and deployed that can utilize this unprecedented number of processors. Parallel-in-time methods provide one such class of mathematical algorithms. They add a new dimension (time) of parallelism and thus allow existing computer models to be extended to next generation supercomputers. The range of potential applications is vast: computational molecular dynamics such as protein and DNA folding, computational biology (e.g., heart modeling), computational fluid dynamics (e.g., combustion, climate, and weather), and machine learning.The conference will feature ten lectures by Professor Gander, an expert in parallel time integration. Using appropriate mathematical methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretizations, integration techniques, and convergence analyses of these iterative methods, conference participants will be exposed to the numerical analysis of parallel-in-time methodologies and their implementations. The proposed topics include multiple shooting type methods, waveform relaxation methods, time-multigrid methods, and direct time-parallel methods. These lectures will be accessible to a wide audience from a broad range of disciplines, including mathematics, computer science and engineering. The conference website is at- http://conferences.math.mtu.edu/cbms2020/This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.