What is an effective action to resolve high network latency between GPU nodes during distributed deep learning?

An effective action to resolve high network latency between GPU nodes during distributed deep learning is to reconfigure the data distribution strategy. In a distributed training scenario, how data is distributed across various GPU nodes can significantly impact performance. If data isn't well-balanced or if it leads to inefficient connections between nodes, it can increase communication overhead and, consequently, latency.

By optimizing the data distribution strategy, you can minimize the data transfer times between nodes, ensuring that each GPU has the necessary data efficiently and at the right times. This might involve techniques such as using sharding, where data is partitioned evenly among the nodes, or ensuring that data is preprocessed to enhance communication efficiency. Reconfiguration could also involve adjusting the data loading mechanisms, using different communication protocols, or employing frameworks that better facilitate efficient data sharing in distributed systems.

While upgrading to faster GPUs or increasing the CPU cores might enhance overall system performance, these actions do not directly address the root cause of high latency in network communication. Additionally, changing the deep learning framework may not inherently resolve networking challenges unless that framework provides specific advantages for distributed training configurations.