Accelerating PCG power/ground network solver on GPGPU

摘要：

Currently fast and precise P/G (power/ground) solvers are critical for robust P/G designs,but traditional serial P/G solvers are somewhat incapable of millions of nodes in P/G.In spite of powerful computation capability of parallel hardware,paralleled P/G solvers are far from prevailing,especially on complicated special hardware.We anticipated it,and studied on parallelizing and accelerating P/G solvers on GPU. In our work,we developed a PCG(Preconditioned conjugate Gradient)-based P/G solver on the CUDA platform for structured P/G network,and identified advantages as well as constraints from GPU architecture. Our PCG-GPU solver can be up to 40 times faster than SuperLU,and also outperform multi-grid based solver on GPU.

关键词： P/G simulation PCG GPGPU

作者: Yici Cai Jin Shi

作者单位: EDA Lab, Department of Computer Science and Technology,Tsinghua University,Beijing,100084,China

会议类型: 国际会议

会议名称: 2009 IEEE 8th International Conference on ASIC(第八届IEEE国际专用集成电路大会)

会议地点: 长沙

会议语种:英文

页码: 650-653

在线出版日期: 2009-10-20（万方平台首次上网日期，不代表论文的发表时间）