FastGR: Global Routing on CPU-GPU with Heterogeneous Task Graph Scheduler (Extended Abstract)

Abstract

Running time is a key metric across the standard physical design flow stages. However, with the rapid growth in design sizes, routing runtime has become the runtime bottleneck in the physical design flow. To improve the effectiveness of the modern global router, we propose a global routing framework with GPU-accelerated routing algorithms and a heterogeneous task graph scheduler, called FastGR. Its runtime-oriented version FastGRL achieves 2.489× speedup compared with the state-of-the-art global router. Furthermore, the GPU-accelerated L-shape pattern routing used in FastGRL can contribute to 9.324× speedup over the sequential algorithm on CPU. Its quality-oriented version FastGRH offers further quality improvement over FastGRL with similar acceleration.

Cite

Text

Liu et al. "FastGR: Global Routing on CPU-GPU with Heterogeneous Task Graph Scheduler (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/720

Markdown

[Liu et al. "FastGR: Global Routing on CPU-GPU with Heterogeneous Task Graph Scheduler (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/liu2023ijcai-fastgr/) doi:10.24963/IJCAI.2023/720

BibTeX

@inproceedings{liu2023ijcai-fastgr,
  title     = {{FastGR: Global Routing on CPU-GPU with Heterogeneous Task Graph Scheduler (Extended Abstract)}},
  author    = {Liu, Siting and Pu, Yuan and Liao, Peiyu and Wu, Hongzhong and Zhang, Rui and Chen, Zhitang and Lv, Wenlong and Lin, Yibo and Yu, Bei},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {6458-6462},
  doi       = {10.24963/IJCAI.2023/720},
  url       = {https://mlanthology.org/ijcai/2023/liu2023ijcai-fastgr/}
}