A Highly Efficient GPU-CPU Hybrid Parallel Implementation of Sparse LU Factorization
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Chinese Journal of Electronics, Vol.21, No.1, 2012
@article{liu2012highly,
title={A Highly Efficient GPU-CPU Hybrid Parallel Implementation of Sparse LU Factorization},
author={LIU, L. and YANG, G.},
year={2012}
}
In this paper, we try to accelerate sparse LU factorization on GPU. We present a tiled storage format and a parallel algorithm to improve the memory access pattern, and a register blocking method to compress the on-chip working set. The OPENMP implementation of our algorithm gives more stable performance over different matrices, and outperforms SuperLU and KLU by 1.88~6 times on an Intel 8-core CPU (Central processing unit) for matrices from the Florida matrix collection. Based on this algorithm, we further propose a GPU-CPU hybrid pipelined scheme to overlap computations on CPU with computations on GPU. Compared to the better of SuperLU and KLU on an Intel 8-core CPU, our algorithm achieves 1.1~19.7-fold speedup on GPU for double precision. Compared to the OPENMP implementation of our algorithm on an Intel 8-core CPU, our GPU implementation gets a 2-fold speedup for the best cases.
January 8, 2012 by hgpu
