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Fast and memory-efficient GPU implementations of krylov subspace methods for efficient power grid analysis

Power grid analysis for modern LSI is computationally challenging in terms of both runtime and memory usage. In this paper, we implement Krylov subspace based linear circuit solvers on a graphics processing unit (GPU) to realize fast power grid analysis. Efficiencies of memory space and access performance are pursued by improving a data structure that stores elements of large sparse matrices. Experimental results on benchmark circuits show that the proposed data structures are more suitable than widely used compressed sparse row (CSR) format and our GPU implementations can achieve up to 17x speedup over CPU implementations.