If your PC resources (CPU, RAM) are sufficient, you can assign multiple workers to run simulations simultaneously, reducing the overall simulation time.

Once the overall simulation configuration is set, workers will automatically divide the range and run simulations independently. All results can be combined by simply executing a single code.

If you want to perform optimization over a wide range, it is recommended to use a high-performance server with multiple workers to reduce computation time. The suggested number of workers corresponds to the number of CPU cores.

Of course, if the number of workers is set to 1, the code will run as it normally would.

§ Prerequisite

1. Docker must be installed.

   sudo apt-get update
   sudo apt-get upgrade
   sudo apt-get install docker
   sudo apt-get install docker-compose
   

2. Pandas must be installed.

   sudo apt install python3-pip
   pip install pandas
   

§ Executing Simulation

1. Clone the repository from GitHub. Every command should be written in this folder directory.

   git clone <https://github.com/Bulnabi-SNU/DBF2025_FORCAST>
   

2. You need to set the number of workers to be used. In the cloned folder directory, open the worker-gen.sh file.

   vim worker-gen.sh
   

3. Change the number assigned to TOTAL_WORKERS to the desired number of workers.

   

4. Run the worker-gen.sh script.

   bash ./worker-gen.sh