In optimizing GPU resource allocation for deep learning, which method can dynamically adjust to workload variations?

The ability to dynamically adjust to workload variations is crucial for optimizing GPU resource allocation in deep learning applications. A Dynamic Workload Management System enables real-time monitoring of resource usage and workload demands. This system can assess current GPU utilization, predict future workloads based on historical patterns or real-time metrics, and allocate resources accordingly. By doing so, it ensures that GPUs are effectively utilized, reducing idle times and maximizing computational efficiency.

In contrast, static resource allocation provides a fixed distribution of resources regardless of fluctuating workloads, which can lead to inefficiencies. Dedicated GPU allocation assigns specific GPUs to a task, limiting flexibility and adaptability as workload demands change. Manual resource scaling involves human intervention to manage resource distribution, which is slower and less efficient than automatic adjustments made by a dynamic system. Thus, relying on a Dynamic Workload Management System for GPU resource allocation optimizes performance based on real-time needs.