SHF:Small:GPU-Based Many-Core Parallel Simulation of Interconnect and High-Frequency Circuits

University Of California-Riverside, Riverside CA

Investigators

Abstract

Parallel computing based on the general purpose Graphic Processing Unit (GPU) provide massive many-core parallelism and can deliver staggering performance improvements over traditional single-core and existing general multi-core computing techniques. The recent introduction of general-purpose GPU (GPGPU) has gained strong interests from the scientific community to tackle many computationally intensive problems. The GPU computing powers, however, have not been fully exploited for many important engineering computing problems in the VLSI design practices. Simulation of massive global interconnects, radio-frequency (RF) and millimeter-wave (MM) integrated circuits (ICs) at very high frequencies remain as difficult problems confronting chip designers. Designing new parallel and scalable computing algorithms, which can unleash the potentials of GPU-based parallel computing techniques, become highly desirable. This research seeks to investigate new parallel simulation approaches to solving massive interconnect circuits and analog/RF/MM integrated circuits based on single node general GPU or networked GPUs on a computer (GPU-cluster). First, the PI will investigate new parallel simulation algorithms based on analytic solution for structured interconnect circuits like on-chip power delivery and clock distribution networks on a GPU or GPU-cluster. Second, the PI proposes developing a very efficient numerical parallel simulation algorithm for analyzing general interconnects. The new algorithm will perform circuit complexity reduction to improve the efficiency. The PI?s team will investigate to parallelize the major computing steps in this method. Third, the PI plans to develop new parallel shooting-Newton methods for high-frequency circuits (RF/MM). The new method will explore structured Krylov-subspace, and GPU-based parallelization to improve efficiency as well as the convergence of RF/MM integrated circuit simulation. The outcome of this research will add significantly to the core knowledge of parallel numerical analysis of linear and nonlinear dynamic systems on the GPU and GPU-cluster systems. By working with the industry partner, the PI expects to bring immediate impacts on the design community to improve the design productivity for nanometer VLSI systems. The research results will also help the electronic design automation (EDA) community to gain more insight in exploring the current and future general-purpose GPUs for parallelizing entire EDA tools on GPUs and multicore systems. The interdisciplinary nature of proposed research and relevant training will allow students to gain critical skills in the highly competitive high-tech job market. This grant will enable the PI to hire more female and underrepresented minority students to further contribute to the diversity in America?s science and technology workforce.