Optimization of AI computation in mixed reality based on GPU virtualization
This research explores the optimization of AI computation in Mixed Reality(MR)applications through GPU virtualiza-tion,focusing on multi-task scheduling and resource sharing.A model is proposed in this study that includes a mechanism for dynami-cally allocating GPU resources to tasks in progress based on task priority,resource demand,and wait time.Simultaneously,the model adopts an optimized multi-task scheduling algorithm to enhance scheduling efficiency.Experimental results show that although the model's execution time,GPU utilization rate,and memory usage are slightly inferior to the physical GPU in single-task performance tests,it demonstrates significant advantages in multi-task concurrency and resource sharing.Future research will explore the design of more fair and efficient resource sharing strategies and further optimize the multi-task scheduling algorithm.
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