Presentation
EastPCG: An Efficient and Self-Tuning Graph Sparsification Based PCG Solver for Circuit Simulation
DescriptionGraph spectral sparsification plays an important role in extensive EDA applications. For preconditioned conjugate gradient (PCG) solvers, graph spectral sparsification is a promising preconditioning technique in both theory and practice. In this paper, a highly efficient and stable graph sparsification algorithm based on spectral probability is proposed. Meanwhile, targeting at minimum total solution time of the linear equation with multiple right-hand sides, an efficient self-tuning PCG framework powered by neural networks is proposed. Combining the proposed techniques, an efficient and self-tuning graph sparsification based PCG solver, named EastPCG, is finally developed. Extensive experiments on various benchmarks have demonstrated the advantages of the proposed algorithms over existing counterparts.
Event Type
Research Manuscript
TimeMonday, July 274:30pm - 4:42pm PDT
LocationMtg Room 202AB