GPU-Accelerated Parallel Sparse LU Factorization Method for Fast Circuit Analysis

Abstract—LU factorization for sparse matrices is the most important computing step for circuit simulation problems. However parallelizing LU factorization on the Graphic Processing Units (GPU) turns out to be a difficult problem due to intrinsic data dependency and irregular memory access, which diminish GPU computing power. In this article, we propose a new sparse LU solver on GPUs for circuit simulation and more general scientific computing. The new method, which is called GLU solver (for GPU LU), is based on a hybrid right-looking LU factorization algorithm for sparse matrices. We show that more concurrency can be exploited in the right-looking method than the left-looking method, which is more popular for circuit analysis, on GPU platforms. At the same time, the GLU also preserves the benefit of column-based left-looking LU method such as symbolic analysis and column-level concurrency. We show that the resulting new parallel GPU LU solver allows the parallelization of all thre...

• Type: Journal
• Year: 2016
• Where: TVLSI
• Authors: Kai He, Sheldon X.-D. Tan, Hai Wang, Guoyong Shi
• Keywords: TVLSI 2016