A High-Performance Heterogeneous Critical Path Analysis Framework
Yasin Zamani, Tsung‐Wei Huang
Abstract
Emphasis on static timing analysis (STA) has shifted from graph-based analysis (GBA) to path-based analysis (PBA) for reducing unwanted slack pessimism. However, it is extremely time-consuming for a PBA engine to analyze a large set of critical paths. Recent years have seen many parallel PBA applications, but most of them are limited to CPU parallelism and do not scale beyond a few threads. To overcome this challenge, we propose in this paper a high-performance graphics processing unit (GPU)-accelerated PBA framework that efficiently analyzes the timing of a generated critical path set. We represent the path set in three dimensions, timing test, critical path, and pin, to structure the granularity of parallelism scalable to arbitrary problem sizes. In addition, we leverage task-based parallelism to decompose the PBA workload into CPU-GPU dependent tasks where kernel computation and data processing overlap efficiently. Experimental results show that our framework applied to an important PBA application can speed up the state-of-the-art baseline up to 10× on a million-gate design.