Scheduling Methods and Systems for Distributed Heterogeneous Computing Power for AI Computing

• Published in: 2024 Asia Pacific Conference on Innovation in Technology (APCIT) pp. 1 - 6
• Main Authors: Cui, Wenpeng, Zheng, Zhe, Liu, Jinghua, Xiong, Yanwei, Yang, Bokuan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 26.07.2024
• Subjects: Accuracy; AI Computing; Distributed heterogeneous computing power; GPT theory; Heterogeneous networks; Key performance indicator; Processor scheduling; Scheduling; Scheduling method; system; Technological innovation; Whales
• DOI: 10.1109/APCIT62007.2024.10673704

In order to solve the challenges of the scheduling methods and systems of distributed heterogeneous computing power, in view of the shortcomings of the existing whale algorithms, this study introduces an innovative chip internal scheduling method and system method based on AI computing. This new scheme uses the principles of GPT theory to accurately identify and locate key influencing factors, and accordingly makes a wise classification of indicators to reduce possible interference. At the same time, using the unique mechanism of AI computing, the design strategy of the scheduling method is cleverly constructed. The empirical results show that this scheme shows a significant improvement compared with the traditional whale algorithm in terms of key performance indicators such as the internal scheduling method of the chip, the accuracy of the system, and the processing efficiency of key factors, showing its obvious strong advantages. In distributed heterogeneous computing power, the internal scheduling method and system of the chip play a crucial role, which can accurately predict and optimize the scheduling method of distributed heterogeneous computing power and the growth trend and output results of the system. However, in the face of complex simulation tasks, traditional whale algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of AI computing optimization chip internal scheduling method and system, and accurately controls the influencing parameters through GPT theory, and uses this as a road map for indicator allocation, and then uses AI computing innovation to build a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the scheduling method and system link of distributed heterogeneous computing power, the simulation scheme based on AI computing successfully overcomes the shortcomings of the traditional whale algorithm and significantly improves the accuracy and operation efficiency of the simulation.