Computational Fluid Dynamics in the Exascale Era of Computation

百亿亿次计算时代的计算流体动力学

• 批准号: RGPIN-2022-03786
• 负责人: Tricco, Terrence
• 金额: $ 1.82万
• 依托单位: Memorial University of Newfoundland
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749934
• 关键词: Computational; Fluid; Dynamics; Exascale; Era

Fluids are everywhere in nature. Computational fluid dynamics (CFD) is the broad field of using computers to obtain solutions to the equations describing fluid flow. Performing simulations of fluid flow using CFD techniques has become an indispensable tool to study problems across a range of scientific disciplines. Simulations allow us to validate of our understanding of complex phemonena, and, in many cases, offers the only potential avenue to study problems that are intractable from a mathematical or experimental perspective. Simulations continually increase in scope, reflecting the need to connect small-scale processes with large-scale features, and the drive to create wholly consistent simulations that encompass all relevant physical processes. That simulations can increase in scale and complexity is permissible because supercomputing clusters continue to exponentially grow in computational power over time. Within the next decade, the first supercomputing clusters will come online that can compute at 10^18 floating point operations per second (an exaflop). However, there exist substantial challenges in designing parallel algorithms that can scale CFD solvers for exascale clusters. These clusters will have more computational power not by individual CPUs becoming faster, but by sheer number of CPUs, and by including a mixture of CPUs and GPUs (graphics processing units). Communication loads and memory bottlenecks will need to be overcome, and failure modes that are rare enough to ignore today will become commonplace. The primary objectives of this research program are to design parallel algorithms and multi-physics CFD solvers that enable the next generation of CFD simulations on exascale clusters. I will focus on smoothed particle hydrodynamics (SPH), a widely used mesh-free CFD method, and its applications to astrophysics. I will focus on two main areas: designing parallel algorithms for exascale clusters, and creating the multi-physics algorithms required to solve key astrophysical problems by exascale computation. The first exascale clusters will soon be reality, as the US National Strategic Computing Initiative aims to have a capable exascale cluster by 2023. The outcomes of this research program will have numerous beneficial impacts to Canadian society, as robust algorithms to utilize exascale resources will be useful in many areas, such as wind farm optimization, cancer drug discovery, and nuclear reactor design. Training of undergraduate and graduate students through my research program will yield highly-qualified personnel (HQP) with valuable skills, such as software engineering and data science, that are transferrable to a broad range of careers.

流体在自然界中无处不在。计算流体动力学（CFD）是使用计算机获得描述流体流动的方程的解的广泛领域。使用CFD技术进行流体流动模拟已成为研究一系列科学学科问题的不可或缺的工具。模拟使我们能够验证我们对复杂现象的理解，并且在许多情况下，提供了研究从数学或实验角度难以解决的问题的唯一潜在途径。模拟的范围不断扩大，反映了将小规模过程与大规模特征连接起来的需要，以及创建涵盖所有相关物理过程的完全一致的模拟的动力。模拟可以增加规模和复杂性是允许的，因为超级计算集群的计算能力随着时间的推移继续呈指数级增长。在未来十年内，第一批超级计算集群将上线，每秒可以计算10^18次浮点运算（一个exaflop）。 然而，在设计并行算法，可以扩展的计算流体动力学求解器的exascale集群存在很大的挑战。这些集群将拥有更多的计算能力，不是通过单个CPU变得更快，而是通过CPU的绝对数量，以及包括CPU和GPU（图形处理单元）的混合。通信负载和内存瓶颈将需要克服，今天很少被忽视的故障模式将变得司空见惯。该研究计划的主要目标是设计并行算法和多物理场CFD求解器，以实现exascale集群上的下一代CFD模拟。我将专注于光滑粒子流体动力学（SPH），一个广泛使用的无网格计算流体力学方法，及其应用天体物理学。我将专注于两个主要领域：设计exascale集群的并行算法，并创建通过exascale计算解决关键天体物理问题所需的多物理算法。第一个艾级集群将很快成为现实，因为美国国家战略计算计划的目标是到2023年拥有一个有能力的艾级集群。这项研究计划的成果将对加拿大社会产生许多有益的影响，因为利用艾级资源的强大算法将在许多领域有用，例如风电场优化，癌症药物发现和核反应堆设计。通过我的研究计划对本科生和研究生的培训将产生具有宝贵技能的高素质人才（HQP），如软件工程和数据科学，这些技能可转移到广泛的职业生涯中。