Numerical study of geometric multigrid methods on CPU-GPU heterogeneous computers

Chunsheng Feng, Shi Shu, Jinchao Xu, Chen Song Zhang

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The geometric multigrid method (GMG) is one of the most efficient solving techniques for discrete algebraic systems arising from elliptic partial differential equations. GMG utilizes a hierarchy of grids or discretizations and reduces the error at a number of frequencies simultaneously. Graphics processing units (GPUs) have recently burst onto the scientific computing scene as a technology that has yielded substantial performance and energy-efficiency improvements. A central challenge in implementing GMG on GPUs, though, is that computational work on coarse levels cannot fully utilize the capacity of a GPU. In this work, we perform numerical studies of GMG on CPU-GPU heterogeneous computers. Furthermore, we compare our implementation with an efficient CPU implementation of GMG and with the most popular fast Poisson solver, Fast Fourier Transform, in the cuFFT library developed by NVIDIA.

Original languageEnglish (US)
Pages (from-to)1-23
Number of pages23
JournalAdvances in Applied Mathematics and Mechanics
Volume6
Issue number1
DOIs
StatePublished - 2014

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Applied Mathematics

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