NICSLU: An Adaptive Sparse Matrix Solver for Parallel Circuit Simulation

摘要

The sparse matrix solver has become a bottleneck in simulation program with integrated circuit emphasis (SPICE)-like circuit simulators. It is difficult to parallelize the solver because of the high data dependency during the numeric LU factorization and the irregular structure of circuit matrices. This paper proposes an adaptive sparse matrix solver called NICSLU, which uses a multithreaded parallel LU factorization algorithm on shared-memory computers with multicore/multisocket central processing units to accelerate circuit simulation. The solver can be used in all the SPICE-like circuit simulators. A simple method is proposed to predict whether a matrix is suitable for parallel factorization, such that each matrix can achieve optimal performance. The experimental results on 35 matrices reveal that NICSLU achieves speedups of $2.08timessim 8.57times~({rm on~the~geometric~mean})$, compared with KLU, with 1–12 threads, for the matrices which are suitable for the parallel algorithm. NICSLU can be downloaded from http:/icslu.weebly.com.