When addressing uneven GPU utilization in a deep learning application, what is the most effective solution?

Implementing dynamic load balancing for the GPUs is the most effective solution for addressing uneven GPU utilization in a deep learning application. This approach involves distributing computational workloads across GPUs in a way that optimizes their usage and effectively utilizes the available resources. When certain GPUs are underutilized while others are overburdened, dynamic load balancing can assess the current workload of each GPU and reallocate tasks accordingly to ensure all GPUs operate at or near their maximum capacity.

This method is particularly beneficial in deep learning, where workloads can frequently change and be unpredictable. By dynamically adjusting the distribution of workloads, the system can enhance overall efficiency, minimize idle time for any given GPU, and ensure that performance bottlenecks are addressed in real-time.

The other options do not effectively tackle the fundamental issue of uneven workload distribution: reducing dataset sizes might help in some scenarios but could compromise model performance; increasing the clock speed may lead to other thermal and power-related issues, and while adding more GPUs could help improve overall throughput, it does not resolve the inconsistency in workload distribution among the existing GPUs. Thus, implementing dynamic load balancing directly targets the root of uneven utilization, making it the best choice.