Cluster-Based and GPU-Driven Parallel Computing Model to Accelerate Circuit Simulation

Abstract

In this digital age circuit design, analysis and validation is not only fundamental step but quite crucial in all the industries and in research. Simulation software is available for circuit analysis, but they all prove to be slower for very large circuit simulation or to execute thousands of iteration of transient analysis. Accelerating simulator is as important as speeding up circuit design. In this paper we have addressed circuit analysis using parallel computing approach on Graphics Processing Unit (GPU). Now a day’s high end GPUs are available with sufficient memory in the architecture itself. Circuit processing functions are analysed to search compute intensive functions. Mathematical operations are redesigned so that it will execute in parallel. LU decomposition algorithm and complex math operations are converted in parallel form. Some mathematical operations are simplified to merge them in suitable cluster. Clustering approach is used which helps in finding kernel of uniform operations to map on GPU cores. GPU programming strategies like if-else in-lining, parallel reduction etc are useful in accelerating circuit operations. Use of look up tables in shared memory or constant memory proves to be useful in fast data access. At least 15% speed gain is achieved for operational analysis and 40% for transient analysis of regular circuits.