Accelerating Large-Scale Sparse LU Factorization for RF Circuit Simulation

Sparse LU factorization is the indispensable building block of the circuit simulation, and dominates the simulation time, especially when dealing with large-scale circuits. Radio frequency (RF) circuits have been increasingly emphasized with the evolution of ubiquitous wireless communication (i.e., 5G and WiFi). The RF simulation matrices show a distinctive pattern of structured dense blocks, and this pattern has been inadvertently overlooked by prior works, leading to the underutilization of computational resources. In this paper, by exploiting the block structure, we propose a novel blocked format for L and U factors and re-design the large-scale sparse LU factorization accordingly, which leverages the data locality inherent in RF matrices. The data format transformation is streamlined, strategically eliminating the redundant data movement and costly indirect memory access. Moreover, the vector operations are converted into matrix operations, enabling efficient data reuse and enhancing data-level parallelism. The experiment results demonstrate that our method achieves superior performance compared to state-of-the-art implementation.