Titlebook: Parallel Sparse Direct Solver for Integrated Circuit Simulation; Xiaoming Chen,Yu Wang,Huazhong Yang Book 2017 Springer International Publ

Chameleon

Xiaoming Chen,Yu Wang,Huazhong Yang sich an, diese Aufgaben zur Einiibung der Lerninhalte des Grundstudiums der Informatik oder als begleitende Lekture für ein Studium mit Nebenfach Informatik zu bearbeiten. Die in diesem Buch enthaltenen Aufgaben eignen sich auch sehr gut, um im Selbststudium Wissensliicken zu erkennen und zu schlie

開(kāi)始從未

Related Work,nt of parallelization techniques for SPICE-like circuit simulation [.]. In this chapter, we will comprehensively review state-of-the-art studies on parallel circuit simulation techniques. Before that, we would like to briefly introduce classifications of these parallel techniques. Based on different

疲勞

Parallel Sparse Left-Looking Algorithm,zation faces three major challenges: the high sparsity of circuit matrices, the irregular structure of the symbolic pattern, and the strong data dependence during sparse LU factorization. To overcome these challenges, we propose an innovative framework to realize parallel sparse LU factorization. Th

Sinus-Node

Performance Model,to understand the performance difference and find possible limiting factors of the scalability, further investigations are required. Toward this goal, in this chapter, we will build a performance model to analyze the performance and find bottlenecks of the scalability of NICSLU.

profligate

Condense

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江湖郎中

Overall Solver Flow,In this chapter, we will present the basic flow of our proposed solver NICSLU, as a necessary background of the parallelization techniques.

cloture

Improvement Techniques,In the previous two chapters, we have presented the basic flow of our solver and the parallelization methodologies for both numerical full factorization and re-factorization, as well as the factorization method selection strategy.

MEET

Test Results,In this chapter, we will present the experimental results of NICSLU and the comparisons with PARDISO and KLU.