NekRS, a GPU-accelerated spectral element Navier–Stokes solver

IF 2 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Parallel Computing Pub Date : 2022-12-01 DOI:10.1016/j.parco.2022.102982

Paul Fischer , Stefan Kerkemeier , Misun Min , Yu-Hsiang Lan , Malachi Phillips , Thilina Rathnayake , Elia Merzari , Ananias Tomboulides , Ali Karakus , Noel Chalmers , Tim Warburton

引用次数: 53

Abstract

The development of NekRS, a GPU-oriented thermal-fluids simulation code based on the spectral element method (SEM) is described. For performance portability, the code is based on the open concurrent compute abstraction and leverages scalable developments in the SEM code Nek5000 and in libParanumal, which is a library of high-performance kernels for high-order discretizations and PDE-based miniapps. Critical performance sections of the Navier–Stokes time advancement are addressed. Performance results on several platforms are presented, including scaling to 27,648 V100s on OLCF Summit, for calculations of up to 60B grid points (240B degrees-of-freedom).

查看原文本刊更多论文

NekRS, gpu加速谱元Navier-Stokes解算器

介绍了基于谱元法(SEM)的面向gpu的热流体模拟程序NekRS的开发过程。为了性能可移植性，代码基于开放并发计算抽象，并利用SEM代码Nek5000和libParanumal中的可扩展开发，libParanumal是一个用于高阶离散化和基于pde的小型应用程序的高性能内核库。解决了Navier-Stokes时间推进的关键性能部分。给出了在几个平台上的性能结果，包括在OLCF Summit上扩展到27,648 v100，用于计算多达60B个网格点(240B自由度)。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Parallel Computing 工程技术-计算机：理论方法

CiteScore

3.50

自引率

7.10%

发文量

审稿时长

4.5 months

期刊介绍： Parallel Computing is an international journal presenting the practical use of parallel computer systems, including high performance architecture, system software, programming systems and tools, and applications. Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems. Parallel Computing features original research work and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. We also welcome studies reproducing prior publications that either confirm or disprove prior published results. Particular technical areas of interest include, but are not limited to: -System software for parallel computer systems including programming languages (new languages as well as compilation techniques), operating systems (including middleware), and resource management (scheduling and load-balancing). -Enabling software including debuggers, performance tools, and system and numeric libraries. -General hardware (architecture) concepts, new technologies enabling the realization of such new concepts, and details of commercially available systems -Software engineering and productivity as it relates to parallel computing -Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism -Performance measurement results on state-of-the-art systems -Approaches to effectively utilize large-scale parallel computing including new algorithms or algorithm analysis with demonstrated relevance to real applications using existing or next generation parallel computer architectures. -Parallel I/O systems both hardware and software -Networking technology for support of high-speed computing demonstrating the impact of high-speed computation on parallel applications