OAC Core: Small: Architecture and Network-aware Partitioning Algorithms for Scalable PDE Solvers

OAC 核心：小型：可扩展 PDE 求解器的架构和网络感知分区算法

• 批准号: 2008772
• 负责人: Hari Sundar
• 金额: $ 49.93万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008772&HistoricalAwards=false
• 关键词: OAC; Core; Small; Architecture; Network

Solving large-scale partial differential equations (PDE) is common in science and engineering, from studying gravitational waves to designing aerodynamic cars. Given the scale of these problems, solving such PDEs requires supercomputing resources. The latest supercomputing architectures are different from the previous generation of leadership class architectures and and are characterized by high levels of diversity within and across machines. Such diversity and heterogeneity makes it extremely difficult to effectively distribute work, i.e., partition the data or tasks, across disparate computing resources. Since the primary objective of building leadership class machines is to further scientific discovery and national prosperity, it is essential that applications, old and new, are able to scale and utilize these machines to their full potential. This project develops novel data and task partitioning algorithms that factor in the architectural characteristics of modern supercomputers to enable efficient and scalable utilization of current and future computing architectures.Existing data and task partitioning schemes do not explicitly consider the underlying architectural topology while partitioning or is done indirectly during the design of the algorithm and codes. Ignoring topology leads to loss of scalability and performance, while shifting the burden to algorithm/code design increases the development costs and complexity, and decreases portability. While data and task partitioning for parallelization, along with mapping the partitions to processes, have been studied for a long time, they have not been considered as a combined problem. To a large extent this was due to the simple structures and symmetry that existed in cluster computing architectures. Inefficiencies that did not significantly impact performance and scalability ten years ago are starting to inhibit scalability and thereby scientific discovery. This project is a first step in ensuring that scientific discovery is not hampered as a result of the difficulty in porting codes to new architectures. This project develops new graph and space-filling-curve-based partitioning algorithms that are aware of the architectural topology and are able to automatically generate data/task partitions and mappings to address problems with current schemes. The algorithms and software developed as part of this proposal will have wide-ranging impact, by improving the performance and scalability of legacy applications and reducing the development cost and improving the portability of new applications, to run efficiently on systems ranging from simple shared memory architectures to the largest heterogeneous clusters.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.

从研究引力波到设计空气动力学汽车，求解大规模偏微分方程（PDE）在科学和工程中很常见。考虑到这些问题的规模，解决此类PDE需要超级计算资源。最新的超级计算体系结构与上一代领导班体系结构不同，并且具有跨机器内部和跨机器内的高度多样性的特征。这种多样性和异质性使得有效地分发工作，即在不同的计算资源上分配数据或任务非常困难。由于建立领导力班级机器的主要目标是进一步的科学发现和国家繁荣，因此，新旧的应用程序必须能够扩展和利用这些机器的全部潜力，这一点至关重要。该项目开发了新颖的数据和任务分配算法，这些算法因现代超级计算机的体系结构特征来启用当前和未来计算体系结构的有效且可扩展的利用。存在数据和任务分配方案并未明确考虑在划分或在设计过程中划分或在设计层次上进行构图和编码的架构上的潜在拓扑。忽略拓扑会导致可扩展性和性能的丧失，同时将负担转移到算法/代码设计会增加开发成本和复杂性，并降低便携性。 虽然已长时间研究了并行的数据和任务分区，以及将分区映射到流程中，但尚未将其视为一个组合问题。在很大程度上，这是由于群集计算体系结构中存在的简单结构和对称性。十年前没有显着影响性能和可伸缩性的低效率开始抑制可伸缩性，从而抑制了科学发现。该项目是确保由于将代码移植到新体系结构的困难而不会受到阻碍的科学发现的第一步。该项目开发了新的图形和基于空间曲线的分区算法，这些算法意识到建筑拓扑，并能够自动生成数据/任务分区和映射以解决当前方案的问题。作为该提案的一部分开发的算法和软件将通过提高遗产应用程序的性能和可伸缩性，降低发展成本并提高新应用程序的可移植性，在简单共享内存架构上有效运行，从简单的共享体系结构上有效运行，从而通过评估NSF的启发，并以nsf的启发，可以有效地运行。和更广泛的影响审查标准。

项目成果

Multi-discretization domain specific language and code generation for differential equations

微分方程的多离散化域特定语言和代码生成

• 发表时间: 2023
• 期刊: Journal of computational science
• 影响因子: 3.3
• 作者: Heisler, Eric;Deshmukh, Aadesh;Mazumder, Sandip;Sadayappan, Ponnuswamy;Sundar, Hari
• 通讯作者: Sundar, Hari

Scalable Adaptive PDE Solvers in Arbitrary Domains

• DOI: 10.1145/3458817.3476220
• 发表时间: 2021-08
• 期刊: SC21: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: K. Saurabh;Masado Ishii;Milinda Fernando;Boshun Gao;Kendrick Tan;M. Hsu;A. Krishnamurthy;H. Sundar;B. Ganapathysubramanian

A Domain Specific Language Applied to Phonon Boltzmann Transport for Heat Conduction

应用于热传导声子玻尔兹曼输运的领域特定语言

• DOI: 10.1115/imece2022-95034
• 发表时间: 2022
• 期刊: ASME International Mechanical Engineering Congress and Exposition
• 作者: Heisler, Eric;Saurav, Siddharth;Deshmukh, Aadesh;Mazumder, Sandip;Sadayappan, Ponnuswamy;Sundar, Hari
• 通讯作者: Sundar, Hari