Heterogeneous GPU Cluster Resource Management Framework

This paper presents a novel resource management framework for heterogeneous GPU clusters. The framework comprises two main components: a resource scheduler and a task scheduler. The resource scheduler dynamically allocates GPU resources based on GPU type, quantity, and load conditions. The task scheduler assigns tasks to the most suitable GPUs based on task type, priority, and resource requirements. Additionally, the framework provides monitoring and statistical functionalities, enabling administrators to track GPU cluster usage and identify performance bottlenecks. Experimental results demonstrate the framework's effectiveness in enhancing GPU cluster utilization and task execution efficiency.