Research on parallel optimization and acceleration technology for new energy power generation large model computing based on localized supercomputing platforms
Rundong Gan, Bin Wang, Chen Luo, Xiangchao Mu, Che Wang, Haibin Su, Bin Liu · 2024
Due to the disconnection between arithmetic demand and supply, and encountering the limitations of cloud blocking in the upgrading process, in order to solve the challenges of computational complexity and accuracy, as well as the problems of data security and privacy protection, we propose the research of parallel optimization and acceleration technology for new energy power generation large-scale model computation based on localized supercomputing platform. This study enhances efficient parallel algorithms in the prediction process by parallelizing the processing and improving the time complexity of the algorithms, dividing the large-scale computational task into multiple subtasks and executing these subtasks simultaneously, establishing regular performance monitoring and optimization plans, and periodically evaluating the performance of the system. After the optimization measures are implemented, performance testing and validation are carried out, and the experiments show that the computational parallel optimization acceleration of new energy generation capacity prediction is realized on the supercomputing platform. The optimization effect meets the expectation and achieves 100% of the set performance index.