DATE'25 "Timing-Driven Global Placement by Efficient Critical Path Extraction"

We provide the implementation of the method proposed in the paper. It is built upon the popular open-source infrastructure DREAMPlace.

Build with Docker

We highly recommend the use of Docker to enable a smooth environment configuration.

The following steps are borrowed from DREAMPlace repository. We make minor revisions to make it more clear.

Option 1: pull from the cloud limbo018/dreamplace.
```
docker pull limbo018/dreamplace:cuda
```

Option 2: build the container.

docker build . --file Dockerfile --tag your_name/dreamplace:cuda

Enter bash environment of the container. Replace limbo018 with your name if option 2 is chosen in the previous step.

Option 1: Run with GPU on Linux.

docker run --gpus 1 -it -v $(pwd):/DATE25-TDP limbo018/dreamplace:cuda bash

Option 2: Run with CPU on Linux.

docker run -it -v $(pwd):/DREAMPlace limbo018/dreamplace:cuda bash

Build.

mkdir build
cd build
cmake .. 
make
make install

Get benchmarks: download the cases here: https://drive.google.com/file/d/1xeauwLR9lOxnYvsK2JGPSY0INQh8VuE4/view?usp=sharing. Unzip the package and put it in the following directory:
```
install/benchmarks/iccad2015.ot
```

Run our method on case superblue1 of ICCAD2015 timing-driven placement contest:

python dreamplace/Placer.py test/iccad2015.pin2pin/$case.json

Or you can run all 8 cases by:

cd install
./run.sh