high performance computing on graphics processing units: hgpu.org

In this research, we developed a framework to execute genetic algorithms (GA) in various parallel environments. GA researchers can prepare implementations of GA operators and fitness functions using this framework. We have prepared several types of communication library in various parallel environments. Combining GA implementations and our libraries, GA researchers can benefit from parallel processing without requiring deep knowledge of different parallel architectures. In the proposed framework, the GA model is restricted to a micro-grained model. In this paper, parallel libraries for a Windows cluster environment, multi-core CPU environment, and GPGPU environment are described. A simple GA was implemented with the proposed framework. Computational performance is also discussed through numerical examples. In this research, we developed a framework to execute genetic algorithms (GA) in various parallel environments. GA researchers can prepare implementations of GA operators and fitness functions using this framework. We have prepared several types of communication library in various parallel environments. Combining GA implementations and our libraries, GA researchers can benefit from parallel processing without requiring deep knowledge of different parallel architectures. In the proposed framework, the GA model is restricted to a micro-grained model. In this paper, parallel libraries for a Windows cluster environment, multi-core CPU environment, and GPGPU environment are described. A simple GA was implemented with the proposed framework. Computational performance is also discussed through numerical examples.