NICSLU: An Adaptive Sparse Matrix Solver for Parallel Circuit Simulation
TLDR
An adaptive sparse matrix solver called NICSLU is proposed, which uses a multithreaded parallel LU factorization algorithm on shared-memory computers with multicore/multisocket central processing units to accelerate circuit simulation.Abstract:
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.read more
Citations
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Journal ArticleDOI
A survey of direct methods for sparse linear systems
TL;DR: The goal of this survey article is to impart a working knowledge of the underlying theory and practice of sparse direct methods for solving linear systems and least-squares problems, and to provide an overview of the algorithms, data structures, and software available to solve these problems.
Journal ArticleDOI
GPU-Accelerated Sparse LU Factorization for Circuit Simulation with Performance Modeling
TL;DR: This paper develops a hybrid parallel LU factorization approach combining task-level and data-level parallelism on GPUs and investigates bottlenecks of the proposed approach by a parametric performance model.
Book ChapterDOI
State-of-The-Art Sparse Direct Solvers
TL;DR: In this chapter, it is demonstrated how recent improvements in developing advanced direct solution methods have enabled speeding up parallel circuit simulation without sacrificing accuracy.
Journal ArticleDOI
GPU-Accelerated Parallel Sparse LU Factorization Method for Fast Circuit Analysis
TL;DR: This paper proposes a new sparse LU solver on GPUs for circuit simulation and more general scientific computing, based on a hybrid right-looking LU factorization algorithm for sparse matrices, and shows that more concurrency can be exploited in the right- looking method than the left-looking method on GPU platforms.
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