G-PathGen: An Efficient GPU-Parallel k-Critical Path Generation Algorithm
Jun 2, 2026·
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0 min read
Che Chang
Yi-Hua Chung
Cheng-Hsiang Chiu
Wan-Luan Lee
Boyang Zhang
Ulf Schlichtmann
Ing-Chao Lin
Xiangyao Yu
Tsung-Wei Huang
Abstract
Critical path generation (CPG) plays a key role in many circuit timing analysis (CTA) applications. As the design complexity continues to increase, CPG runtime has become a major bottleneck in many timing-driven applications. To mitigate this runtime challenge, several CPU-based algorithms have been introduced by both the CTA and parallel computing communities, but they remain slow for large CPG problems. While GPU-accelerated solutions exist, they are often inexact and incur significant overhead from iterative CPU–GPU data transfers, limiting their practical use in CTA applications. To overcome this challenge, we propose G-PathGen, an exact GPU-parallel CPG algorithm targeting CTA applications. G-PathGen introduces efficient kernel algorithms for generating critical paths in parallel and dynamically adjusts the generated path count to maximize GPU utilization while minimizing redundant work. Compared to a state-of-the-art GPU solution, G-PathGen is 1.6x–243.8xfaster when generating one million critical paths on industrial circuit graphs.
Type
Publication
ACM International Conference on Supercomputing (ICS), Belfast, Northern Ireland, UK, 2026

Authors
Yi-Hua Chung
(she/her)
Ph.D. Student
I am a fourth-year Ph.D. student in the Department of Electrical and Computer Engineering at UW-Madison, advised by Prof. Tsung-Wei (TW) Huang. My research focuses on GPU-accelerated algorithms for compiler optimization, graph partitioning, and design automation applications.
I developed SimPart, a GPU-parallel graph partitioner for logic simulation by integrating disjoint-set and replication-aided strategies, further optimized with conditional CUDA Graphs. I also collaborate with Synopsys to develop GPU-parallel algorithms for gate sizing in an industrial-use EDA tool.