GPU-Accelerated Scalable Solver for Banded Linear Systems
George Washington University
IEEE Cluster, 2013
@article{liu2013gpu,
title={GPU-Accelerated Scalable Solver for Banded Linear Systems},
author={Liu, Hang and Seo, Jung-Hee and Mittal, Rajat and Howie Huang, H.},
year={2013}
}
Solving a banded linear system efficiently is important to many scientific and engineering applications. Current solvers achieve good scalability only on the linear systems that can be partitioned into independent subsystems. In this paper, we present a GPU based, scalable Bi-Conjugate Gradient Stabilized solver that can be used to solve a wide range of banded linear systems. We utilize a row-oriented matrix decomposition method to divide the banded linear system into several correlated sublinear systems and solve them on multiple GPUs collaboratively. We design a number of GPU and MPI optimizations to speedup inter-GPU and inter-machine communications. We evaluate the solver on Poisson equation and advection diffusion equation as well as several other banded linear systems. The solver achieves a speedup of more than 21 times running from 6 to 192 GPUs on the XSEDE’s Keeneland supercomputer and because of small communication overhead, can scale upto 32 GPUs on Amazon EC2 with relatively slow ethernet network.
August 16, 2013 by hgpu
