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README.md
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@ -6,7 +6,7 @@ We are happy to announce that Xplace 2.0 is now released. Compared to [Xplace 1.
- Support deterministic mode with only 5~25% extra GP runtime overhead. - Support deterministic mode with only 5~25% extra GP runtime overhead.
- Implement an extremely fast GPU-accelerated detailed-routability-driven placement algorithm. - Implement an extremely fast GPU-accelerated detailed-routability-driven placement algorithm.
- Integrate with a GPU-accelerated detailed placer and a GPU-accelerated global router. - Integrate with a GPU-accelerated detailed placer and a GPU-accelerated global router.
- Support a superfast **GPU-accelerated place and global route flow** ([Xplace](https://dl.acm.org/doi/abs/10.1145/3489517.3530485) + [GGR](https://dl.acm.org/doi/10.1145/3508352.3549474))! Input your LEF/DEF, the flow will output the **placement DEF** and the **global routing guide**! - Support a superfast **GPU-accelerated place and global route flow** ([Xplace](https://dl.acm.org/doi/abs/10.1145/3489517.3530485) + [GGR](cpp_to_py/gpugr/README.md))! Input your LEF/DEF, the flow will output the **placement DEF** and the **global routing guide**!
- Provide benchmark download and preprocess scripts, and three routability evaluation scripts. - Provide benchmark download and preprocess scripts, and three routability evaluation scripts.
- Code refactoring. - Code refactoring.
@ -128,7 +128,7 @@ python main.py --dataset ispd2005 --run_all True --load_from_raw False
3. If you want to run `pt` mode for the custom dataset, you need to add the custom dataset path in `utils/get_design_params.py`. 3. If you want to run `pt` mode for the custom dataset, you need to add the custom dataset path in `utils/get_design_params.py`.
## GPU-accelerated place and global route flow (Xplace + GGR) ## GPU-accelerated place and global route flow (Xplace + GGR)
Set `--final_route_eval True` in Python arguments to invoke the internal global router [GGR](https://dl.acm.org/doi/10.1145/3508352.3549474) to run GPU-accelerated PnR flow. The flow will output the **placement DEF** and the **global routing guide** in `./result/exp_id/output`. Besides, GR metrics are reported in the log and recorded in `./result/exp_id/log/route.csv`. Set `--final_route_eval True` in Python arguments to invoke the internal global router [GGR](cpp_to_py/gpugr/README.md) to run GPU-accelerated PnR flow. The flow will output the **placement DEF** and the **global routing guide** in `./result/exp_id/output`. Besides, GR metrics are reported in the log and recorded in `./result/exp_id/log/route.csv`.
- To run Place and Global Route flow for ISPD2015 dataset: - To run Place and Global Route flow for ISPD2015 dataset:
```bash ```bash
@ -140,7 +140,7 @@ More details about using GGR in Xplace can be found in [cpp_to_py/gpugr/README.m
## Evaluate the routability of Xplace's solution ## Evaluate the routability of Xplace's solution
We provide three ways to evaluate the routability of a placement solution: We provide three ways to evaluate the routability of a placement solution:
1. Set `--final_route_eval True` to invoke [GGR](https://dl.acm.org/doi/10.1145/3508352.3549474) to evaluate the placement solution. 1. Set `--final_route_eval True` to invoke [GGR](cpp_to_py/gpugr/README.md) to evaluate the placement solution.
2. Use [CU-GR](https://github.com/cuhk-eda/cu-gr) to evaluate the placement solution by global routing. Please refer to [tool/cugr_ispd2015_fix/README.md](tool/cugr_ispd2015_fix/README.md) for instructions. 2. Use [CU-GR](https://github.com/cuhk-eda/cu-gr) to evaluate the placement solution by global routing. Please refer to [tool/cugr_ispd2015_fix/README.md](tool/cugr_ispd2015_fix/README.md) for instructions.

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cpp_to_py/gpugr/README.md
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@ -5,7 +5,7 @@ More details are in the following paper:
Shiju Lin, Jinwei Liu, Evangeline F.Y. Young and Martin D.F. Wong. "[GAMER: GPU-Accelerated Maze Routing](https://ieeexplore.ieee.org/document/9799536)". In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 42, no. 2, pp. 583-593, Feb. 2023. Shiju Lin, Jinwei Liu, Evangeline F.Y. Young and Martin D.F. Wong. "[GAMER: GPU-Accelerated Maze Routing](https://ieeexplore.ieee.org/document/9799536)". In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 42, no. 2, pp. 583-593, Feb. 2023.
Shiju Lin and Martin D. F. Wong. "[Superfast Full-Scale CPU-Accelerated Global Routing](https://doi.org/10.1145/3508352.3549474)". In Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (ICCAD '22). Association for Computing Machinery, New York, NY, USA, Article 51, 1–8. Shiju Lin and Martin D. F. Wong. "[Superfast Full-Scale GPU-Accelerated Global Routing](https://doi.org/10.1145/3508352.3549474)". In Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (ICCAD '22). Association for Computing Machinery, New York, NY, USA, Article 51, 1–8.
**GGR is integrated in Xplace now!** **GGR is integrated in Xplace now!**
@ -30,7 +30,7 @@ If you find **GGR** useful in your research, please consider to cite:
@inproceedings{lin2022ggr, @inproceedings{lin2022ggr,
author = {Lin, Shiju and Wong, Martin D. F.}, author = {Lin, Shiju and Wong, Martin D. F.},
booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design}, booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design},
title = {Superfast Full-Scale CPU-Accelerated Global Routing}, title = {Superfast Full-Scale GPU-Accelerated Global Routing},
year = {2022}, year = {2022},
} }