GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation
Department of Electrical and Computer Engineering, University of California at Riverside, Riverside, CA 92521, USA
arXiv:1908.00204 [cs.DC], (1 Aug 2019)
@misc{peng2019glu30,
title={GLU3.0: Fast GPU-based Parallel Sparse LU Factorization for Circuit Simulation},
author={Shaoyi Peng and Sheldon X. -D. Tan},
year={2019},
eprint={1908.00204},
archivePrefix={arXiv},
primaryClass={cs.DC}
}
In this article, we propose a new GPU-based sparse LU factorization method, called GLU3.0, solves the aforementioned problems. First, it introduces a much more efficient double-U dependency detection algorithm to make the detection much simpler. Second, we observe that the potential parallelism is different as the matrix factorization goes on. We then develop three different modes of GPU kernel to adapt to different stages to accommodate the computing task changes in the factorization. As a result, the new GLU can dynamically allocate GPU blocks and wraps based on the number of columns in a level to better balance the computing demands and resources during the LU factorization process. Experimental results on circuit matrices from University of Florida Sparse Matrix Collection (UFL) show that the GLU3.0 can deliver 2-3 orders of magnitude speedup over GLU2.0 for the data dependency detection. Furthermore, GLU3.0 achieve 13.0X (arithmetic mean) and 6.7X (geometric mean) speedup over GLU2.0 and 7.1X (arithmetic mean) and 4.8X (geometric mean) over the recently proposed enhanced GLU2.0 sparse LU solver on the same set of circuit matrices.
August 5, 2019 by hgpu
