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首页> 外文学位 >Accelerating the discontinuous Galerkin cell-vertex scheme (DG-CVS) solver on CPU-GPU heterogeneous systems.
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Accelerating the discontinuous Galerkin cell-vertex scheme (DG-CVS) solver on CPU-GPU heterogeneous systems.

机译：在CPU-GPU异构系统上加速不连续Galerkin单元顶点方案（DG-CVS）求解器。

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摘要

DG-CVS (Discontinuous Galerkin Cell-Vertex Scheme) is an efficient, accurate and robust numerical solver for general hyperbolic conservation laws. It can solve a broad range of conservation laws such as the shallow water equation and magnetohydrodynamics equations. DG-CVS is a Riemann-solver-free high order space-time method for arbitrary space conservation laws. It fuses the Discontinuous Galerkin (DG) method and the Conservation Element/Solution Element (CE/SE) method to take advantage of the best features of both methods. Thanks to the CE/SE method, the time derivative of the solution is treated as an independent unknown, which is amendable to GPU's parallel execution.;In this thesis, we use a CPU-GPU heterogeneous processor to accelerate DG-CVS to demonstrate that complex scientific applications can benefit from a heterogeneous computing system. There are challenges that such scientific program poses on the GPU architecture such as thread divergence and low kernel occupancy. We developed optimizations to address these concerns. Our proposed optimizations include thread remapping to minimize thread divergence and register pressure reduction to increase kernel occupancy. Our experiment results show that DG-CVS on GPU outperforms CPU by up to 57% before optimization and 145% afterwards. We also use DG-CVS as a real world application to explore the possibility of using Shared Virtual Memory (SVM) for tighter collaboration between CPU and GPU. However, SVM did not help improve the performance due to the overhead of address translation and atomic operations. We developed a microbenchmark to better understand the performance impact of SVM.
机译：DG-CVS（不连续Galerkin单元顶点方案）是一种有效，准确且健壮的数值解法器，适用于一般的双曲守恒定律。它可以解决各种各样的守恒定律，例如浅水方程和磁流体动力学方程。 DG-CVS是用于任意空间守恒定律的无Riemann求解器的高阶时空方法。它融合了间断Galerkin（DG）方法和保守元素/溶液元素（CE / SE）方法，以利用两种方法的最佳功能。得益于CE / SE方法，该解决方案的时间导数被视为一个独立的未知数，可以修改GPU的并行执行。本文采用CPU-GPU异构处理器来加速DG-CVS，以证明复杂的科学应用可以受益于异构计算系统。这种科学程序对GPU体系结构构成了挑战，例如线程发散和内核占用率低。我们开发了优化程序来解决这些问题。我们提出的优化措施包括重新映射线程以最大程度地减少线程分歧，并减少寄存器压力以增加内核占用率。我们的实验结果表明，在优化之前，GPU上的DG-CVS性能比CPU高出57％，而在优化之后则高达145％。我们还将DG-CVS用作现实应用程序，以探索使用共享虚拟内存（SVM）在CPU和GPU之间进行更紧密协作的可能性。但是，由于地址转换和原子操作的开销，SVM无法帮助提高性能。我们开发了一个微基准测试，以更好地了解SVM的性能影响。

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  • 作者

    Hu, Xiaoqi.;

  • 作者单位

    The University of Mississippi.;

  • 授予单位 The University of Mississippi.;
  • 学科 Computer science.;Civil engineering.
  • 学位 M.S.
  • 年度 2017
  • 页码 45 p.
  • 总页数 45
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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