Sparse LU factorization for parallel circuit simulation on GPU

Abstract

Sparse solver has become the bottleneck of SPICE simulators. There has been few work on GPU-based sparse solver because of the high data-dependency. The strong data-dependency determines that parallel sparse LU factorization runs efficiently on shared-memory computing devices. But the number of CPU cores sharing the same memory is often limited. The state of the art Graphic Processing Units (GPU) naturally have numerous cores sharing the device memory, and provide a possible solution to the problem. In this paper, we propose a GPU-based sparse LU solver for circuit simulation. We optimize the work partitioning, the number of active thread groups, and the memory access pattern, based on GPU architecture. On matrices whose factorization involves many floating-point operations, our GPU-based sparse LU factorization achieves 7.90× speedup over 1-core CPU and 1.49× speedup over 8-core CPU. We also analyze the scalability of parallel sparse LU factorization and investigate the specifications on CPUs and GPUs that most influence the performance.