Graphics Processing Units Genetic Algorithm

• GPUGA stands for Graphics Processing Units Genetic Algorithm. It is a code for empirical potential fitting using the genetic algorithm (GA).
• Using my laptop with a GeForce RTX 2070 GPU card, fitting an empirical potential using GPUGA only takes about one minute!

• You need to have a GPU card with compute capability no less than 3.5 and a CUDA toolkit which supports your GPU card installed.
• Works in both Windows and Linux.

• Go to the src directory and type make. When the compilation finishes, an executable named gpuga will be generated in the src directory.

• To run the provided example, go to the directory where you can see src and type src/gpuga < examples/input.txt
• The example corresponds to the case study for diamond silicon in the paper below.

If you use GPUGA in your published work, we kindly ask you to cite the following paper which describes the central algorithms used in GPUGA:

• Zheyong Fan, Yanzhou Wang, Xiaokun Gu, Ping Qian, Yanjing Su, and Tapio Ala-Nissila, A minimal Tersoff potential for diamond silicon with improved descriptions of elastic and phonon transport properties, J. Phys.: Condens. Matter 32, 135901 (2020).

• Zheyong Fan (Bohai University and Aalto University)
  • brucenju(at)gmail.com