No abstract

No abstract

No abstract

Abstract-The sparse matrix solver has become the bottleneck in a Simulation Program with Integrated Circuit Emphasis circuit simulator. It is difficult to parallelize the sparse matrix solver because of the high data dependence during the numerical LU factorization. In this brief, a parallel LU factorization algorithm is developed on shared-memory computers with multicore central processing units, based on KLU algorithms. An Elimination Scheduler (EScheduler) is proposed to represent the data dependence during the LU factorization. Based on the EScheduler, the parallel tasks are scheduled in two modes to achieve a high level of concurrence, i.e., cluster mode and pipeline mode. The experimental results on 26 circuit matrices reveal that the developed algorithm can achieve speedup of 1.18-4.55× (on geometric average), as compared with KLU, with 1-8 threads. The result analysis indicates that for different data dependence, different parallel strategies should be dynamically selected to obtain optimal performance.

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 2.08× ∼ 8.57× (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://nicslu.weebly.com.