Elements: AMR-H: Adaptive multi-resolution high-order solver for multiphase compressible flows on heterogeneous platforms

要素：AMR-H：异构平台上多相可压缩流的自适应多分辨率高阶求解器

• 批准号: 2103509
• 负责人: Sanjiva Lele
• 金额: $ 60万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103509&HistoricalAwards=false
• 关键词: Elements; AMR; Adaptive; multi; resolution

In the past decades, computational research has enabled high-fidelity simulations of complex fluid dynamics problems. However, the new generation of high performance computing architectures present significant challenges in portability and, more importantly, parallel performance of complex science application software. This work develops a multi-purpose computational fluid dynamics solver for high-fidelity high-order adaptive resolution simulations. It leverages the state-of-the-art high performance programming models, Legion and Kokkos, which guarantees the performance portability on various existing and upcoming high performance computing platforms. Additionally, it integrates the commonly used numerical and physical modules, with an easy-to-use programming interface for users. As a significant benefit, the researchers can maintain their focus on physical modeling and not require a deep understanding of code design for new high-performance hardware. The educational and community outreach elements of the project will develop a growing community of computational scientists and engineers who are educated to exploit the power of task-level parallelism and enable a new era in high-fidelity computational science.This project develops a general computational framework combining high-order, high accuracy, solution-adaptive discretizations of partial differential equations (with emphasis on flows of non-ideal fluids) tailored to the physics they represent. The discretization is optimized for high resolving efficiency, allows optimal use of the computational degrees of freedom and high utilization of the computer resources due to its high arithmetic intensity and data locality. Adaptive mesh refinement in combination with high-order multi-resolution compact scheme allows for easy pre-processing and meshing for complex-geometry problems. Co-designing the numerical framework with new developments in the Legion framework would allow for automated, optimized runtime scheduling of tasks involving computational kernels and data movement across memory hierarchies. This, combined with efficient leveraging of Kokkos, would free the computational scientist/engineer from hardware specific programming models and allow exascale computations on heterogeneous computers. It will enable first of its kind simulations of compressible multiphase flow phenomena in turbulent flow regimes for retrograde fluids on exascale platforms.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.

在过去的几十年中，计算研究已经实现了复杂流体动力学问题的高保真模拟。然而，新一代高性能计算架构在可移植性方面提出了重大挑战，更重要的是，在复杂科学应用软件的并行性能方面提出了重大挑战。这项工作开发了一种用于高保真高阶自适应分辨率模拟的多用途计算流体动力学求解器。它利用最先进的高性能编程模型Legion和Kokkos，保证了在各种现有和即将推出的高性能计算平台上的性能可移植性。此外，它还集成了常用的数值和物理模块，为用户提供了易于使用的编程界面。一个显着的好处是，研究人员可以继续专注于物理建模，而不需要深入了解新高性能硬件的代码设计。该项目的教育和社区外展要素将发展一个不断壮大的计算科学家和工程师社区，他们接受教育以利用任务级并行性的力量，并开启高保真计算科学的新时代。该项目开发了一个通用计算框架结合了偏微分方程的高阶、高精度、解自适应离散化（重点是非理想流体的流动），适合它们所代表的物理场。离散化针对高解析效率进行了优化，由于其高算术强度和数据局部性，可以优化计算自由度和计算机资源的高利用率。自适应网格细化与高阶多分辨率紧凑方案相结合，可以轻松地对复杂几何问题进行预处理和网格划分。与 Legion 框架的新开发共同设计数值框架将允许对涉及计算内核和跨内存层次结构的数据移动的任务进行自动化、优化的运行时调度。这与 Kokkos 的有效利用相结合，将使计算科学家/工程师从特定于硬件的编程模型中解放出来，并允许在异构计算机上进行百亿亿次计算。它将首次实现对百亿亿级平台上的逆行流体的湍流流态中的可压缩多相流现象进行模拟。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Conservative and Robust Compact Finite Difference Approach for Simulations of Dense Gas Flows

用于模拟稠密气流的保守且鲁棒的紧致有限差分法

• 发表时间: 2023-06
• 期刊: AIAA paper
• 作者: Song, Hang;Ghate, Aditya S.;Dai, Steven' Chandra;Lele, Sanjiva K.
• 通讯作者: Lele, Sanjiva K.

Scalable parallel linear solver for compact banded systems on heterogeneous architectures

适用于异构架构上紧凑带状系统的可扩展并行线性求解器

• DOI: 10.1016/j.jcp.2022.111443
• 发表时间: 2022-11
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Song, Hang;Matsuno, Kristen V.;West, Jacob R.;Subramaniam, Akshay;Ghate, Aditya S.;Lele, Sanjiva K.
• 通讯作者: Lele, Sanjiva K.