Which approach optimizes resource utilization in multiple GPU clusters for deep learning workloads?

The approach that optimizes resource utilization in multiple GPU clusters for deep learning workloads is implementing a load-balancing algorithm based on real-time GPU usage. This method actively monitors and evaluates the current utilization levels of the GPUs across the clusters, allowing the system to dynamically allocate workloads to those that are underutilized.

By taking advantage of real-time data, this strategy ensures that the computational power of each GPU is utilized efficiently, minimizing idle time and improving overall throughput. This is particularly crucial in deep learning tasks where computational demands can fluctuate significantly, leading to potential bottlenecks in performance if not managed properly.

Load balancing adjusts based on ongoing performance metrics, making it a highly adaptive solution that inherently accommodates the variability of workload requirements. In contrast, static or less responsive scheduling methods fail to adapt based on current conditions, potentially leading to inefficient resource use.