G-PASTA: GPU-Accelerated Partitioning Algorithm for Static Timing Analysis
Boyang Zhang, Dian-Lun Lin, Che Chang, Cheng-Hsiang Chiu, Bojue Wang, Wan Luan Lee, Chih-Chun Chang, Donghao Fang, Tsung‐Wei Huang
Abstract
Recent static timing analysis (STA) engines have leveraged task dependency graph (TDG) parallelism to accelerate various STA algorithms, including graph-based analysis and path-based analysis. Despite the promising speedup via task parallelism, the scheduling cost of a TDG has become dominant when handling large TDGs. To overcome this challenge, we propose G-PASTA, a simple and fast TDG partitioning algorithm to reduce the scheduling cost of large task-parallel STA algorithms. By harnessing the power of GPU computing, G-PASTA incurs minimal cost of partitioning while bringing significant runtime improvement to task-parallel STA algorithms. Compared to a state-of-the-art CPU-based TDG partitioner, G-PASTA is up to 41.8× faster in partitioning runtime and can improve the overall STA performance by 43% on large designs.