Research on Optimization Strategies for GPU Cluster Cloud Rendering Virtual Simulation Resources

This article aims to explore the application of GPU cluster cloud rendering technology in virtual simulation resource construction, and address the challenges it faces. A series of optimization strategies are proposed, including dynamic load balancing strategy, task scheduling algorithm optimization, resource scheduling and optimization techniques, as well as parallel computing and GPU feature utilization. By establishing application scenarios and setting up 5 tasks from small to large scale, experimental evaluations were conducted to verify the effectiveness and feasibility of these optimization strategies in improving GPU cluster cloud rendering performance. The experimental results show that the optimization strategy significantly improves rendering speed, achieves load balancing, and enhances resource utilization. These optimization strategies are of great significance for improving the overall performance and user experience of GPU cluster cloud rendering. And propose exploring new optimization strategies for future research, such as utilizing artificial intelligence and machine learning for dynamic task allocation and load balancing, researching new resource scheduling algorithms to improve resource utilization, and delving into the parallel computing capabilities and high-performance characteristics of GPUs.