在 Noisette 代码中以异构并行方式实现全逼近多网格法

A. V. Gorobets, S. A. Soukov, A. R. Magomedov
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引用次数: 0

摘要

摘要 本文研究了利用全近似方案多网格(FAS MG)方法加速模拟混合集群系统上的可压缩流。基本数值算法具有以下特点:在空间离散化方面,采用非结构化混合元素网格和在网格节点定义网格函数的方案;在时间离散化方面,采用全隐式方案。这项研究的目的是在中央处理器和图形处理器上实现静态仿真加速,而不会显著降低并行效率。我们介绍了一种构建网格级别的方法;一种提高建模对象几何形状网格表示质量的技术;以及在复杂并行化框架内的并行实施,将用于分布式内存并行模型的消息传递接口 (MPI)、用于共享内存模型的 OpenMP 和用于在各种架构的 GPU 上进行计算的 OpenCL 结合起来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Heterogeneous Parallel Implementation of a Multigrid Method with Full Approximation in the Noisette Code

Abstract

This article studies accelerating simulations of compressible flows on hybrid cluster systems using the full approximation scheme multigrid (FAS MG) method. The basic numerical algorithm has the following features: for spatial discretization, unstructured mixed-element meshes and schemes with the definition of mesh functions at mesh nodes are used; for temporal discretization, a fully implicit scheme is used. The aim of this study is to achieve acceleration of stationary simulations on both central and graphics processors without significant losses in parallel efficiency. We describe an approach to construct mesh levels; a technique for improving the quality of the mesh representation of the geometry of the modeled objects; and parallel implementation within the framework of complex parallelization, combining the message passing interface (MPI) for a distributed-memory parallel model, OpenMP for a shared-memory model, and OpenCL for computing on GPUs of various architectures.

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来源期刊
Mathematical Models and Computer Simulations
Mathematical Models and Computer Simulations Mathematics-Computational Mathematics
CiteScore
1.20
自引率
0.00%
发文量
99
期刊介绍: Mathematical Models and Computer Simulations  is a journal that publishes high-quality and original articles at the forefront of development of mathematical models, numerical methods, computer-assisted studies in science and engineering with the potential for impact across the sciences, and construction of massively parallel codes for supercomputers. The problem-oriented papers are devoted to various problems including industrial mathematics, numerical simulation in multiscale and multiphysics, materials science, chemistry, economics, social, and life sciences.
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