This paper summarizes the opportunities in accelerating simulation on parallel processing hardware platforms such as GPUs. First, we give a summary of prior art. Then, we propose the idea that coding frameworks usually used for popular machine learning (ML) topics, such as PyTorch/DGL.ai, can also be used for exploring simulation purposes. We demo a crude oblivious two-value cycle gate-level simulator using the higher level ML framework APIs that exhibits >20X speedup, despite its simplistic construction. Next, we summarize recent advances in GPU features that may provide additional opportunities to further state-of-the-art results. Finally, we conclude and touch upon some potential areas for furthering research into the topic of GPU accelerated simulation.
(Invited talk for ICCAD tutorial - GPU Acceleration in CAD: Opportunities and Challenges)
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