TY - JOUR
T1 - Numerical study of geometric multigrid methods on CPU-GPU heterogeneous computers
AU - Feng, Chunsheng
AU - Shu, Shi
AU - Xu, Jinchao
AU - Zhang, Chen Song
N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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. © 2014 Global Science Press.
AB - 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. © 2014 Global Science Press.
UR - http://www.journals.cambridge.org/abstract_S2070073300002411
UR - http://www.scopus.com/inward/record.url?scp=84892377871&partnerID=8YFLogxK
U2 - 10.4208/aamm.2013.m87
DO - 10.4208/aamm.2013.m87
M3 - Article
SN - 2075-1354
VL - 6
SP - 1
EP - 23
JO - Advances in Applied Mathematics and Mechanics
JF - Advances in Applied Mathematics and Mechanics
IS - 1
ER -