Application of Parallel Computing Thought in Training Model for Computer Talents

Compared with serial computing, parallel computing can execute multiple instructions at a time, which can increase computing speed and expand the scale of solutions, thereby solving large and complex computing problems. The training of computer talents is also a complex system engineering. Therefore, we propose a parallel dual-system training model for computer talents with industry-education integration and quality-development. Different from general parallel computing, the model we proposed has both temporal and spatial parallelism. The temporal parallelism is reflected in the two system lines being carried out at the same time in the training process, while the spatial parallelism is reflected in the staggering of the two system lines in the same course. In industry-education integration system, we establish the cooperation between schools and enterprises in building a series of curriculums, including mobile application (MA), artificial intelligence (Al), cloud computing (CC), big data (BD), innovation and entrepreneurship (IES), internships at enterprises (IE), to effectively improve students’ technological foresight and mastery. Moreover, the quality-development system consists of the compulsory projects integrated with theoretical courses and the elective projects integrated with extracurricular practice, so as to improve students’ comprehensive quality. In this model, the credits of courses in industry-education system accounts for 37.5%, quality-development system accounts for 32.7%, and other courses account for 29.8%. Practical results show that by applying this talent training model, the counterpart employment rate of computer science graduates in 2021 has increased by 10%, and the number of postgraduate admissions has increased by 80%, compared with graduates in 2020, the number of professional competition awards has increased by 15%, compared with students in 2020.