Distributed time, conservative parallel logic simulation on GPUs

TLDR: This work implemented a conservative parallel simulation approach, the CMB algorithm, on NVidia GPUs, and introduced a CPU/GPU co-processing strategy for the best usage of computing resources.

Abstract: Logical simulation is the primary method to verify the correctness of IC designs. However, today's complex VLSI designs pose ever higher demand for the throughput of logic simulators. In this work, a parallel logic simulator was developed by leveraging the computing power of modern graphics processing units (GPUs). To expose more parallelism, we implemented a conservative parallel simulation approach, the CMB algorithm, on NVidia GPUs. The simulation processing is mapped to GPU hardware at the finest granularity. With carefully designed data structures and data flow organizations, our GPU based simulator could overcome many problems that hindered efficient implementations of the CMB algorithm on traditional parallel computers. In order to efficiently use the relatively limited capacity of GPU memory, a novel memory management mechanism was proposed to dynamically allocate and recycle GPU memory during simulation. We also introduced a CPU/GPU co-processing strategy for the best usage of computing resources. Experimental results showed that our GPU based simulator could outperform a CPU baseline event driven simulator by a factor of 29.2.