Presentation
Late Breaking Results: Scalable GPU-Friendly Parallelization for Sweep-Based Maze Routing
DescriptionGlobal routing is a critical stage in the VLSI design flow, aiming to provide a robust guide for detailed routing and serve as early design feedback for placement. Many approaches have leveraged GPU parallelization to achieve significant acceleration and reduce runtime. However, with the increasing size and complexity of modern large-scale designs, recent GPU-accelerated maze routing approaches, driven by the sweep operation, struggle to find solutions efficiently within limited GPU memory resources. In order to address this issue, this paper proposes a scalable, GPU-friendly sweep-based maze routing methodology that requires significantly less memory and kernel function calls while accelerating overall runtime. We introduce a sweep-sharing technique that allows multiple nets to be routed simultaneously within a single sweeping process, significantly enhancing memory efficiency and reducing kernel launching overhead. We further propose an edge-level rip-up-and-reroute technique that selectively reroutes only overflowed segments, preserving feasible parts of the solution and substantially reducing runtime. Experimental results on the latest ISPD'24 Contest benchmarks demonstrate that our GPU-friendly maze routing with sweep-sharing technique can significantly improve the efficiency over the state-of-the-art GPU-accelerated maze router.
Event Type
Late Breaking Results
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby