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

在过去的几十年里，计算研究已经使复杂的流体动力学问题的高保真模拟成为可能。然而，新一代的高性能计算架构提出了重大挑战，在可移植性，更重要的是，复杂的科学应用软件的并行性能。本研究开发一个多用途的计算流体动力学求解器，可进行高精度高阶自适应分辨率模拟。它利用了最先进的高性能编程模型Legion和Kokkos，保证了各种现有和即将推出的高性能计算平台的性能可移植性。此外，它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。作为一个重要的好处，研究人员可以保持他们对物理建模的关注，而不需要深入了解新的高性能硬件的代码设计。该项目的教育和社区外展元素将发展一个不断增长的计算科学家和工程师社区，他们接受教育，以利用任务级并行的力量，并实现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，结合高阶，高精度，偏微分方程的解自适应离散化（重点是非理想流体的流动），适合于它们所代表的物理学。离散化是优化的高分辨率的效率，允许最佳使用的计算自由度和高利用率的计算机资源，由于其高的算术强度和数据的局部性。自适应网格加密与高阶多分辨率紧致格式相结合，可以方便地对复杂几何问题进行预处理和网格划分。将数值框架与Legion框架中的新开发一起设计，将允许对涉及计算内核和跨存储器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。该奖项反映了NSF的法定使命，并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，被认为值得支持。

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.