Accelerating PCG power/ground network solver on GPGPU

Abstract

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.