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A significant challenge in distributed systems is the cost of communication. In a network of nodes, each node must exchange data with others in addition to performing local computations. This data exchange introduces overhead, and in large networks, the expense of distributing and collecting data can become so substantial that it offsets the time savings gained from parallelism, thereby limiting overall performance and scalability.

Communication Cost in Distributed Systems

Based on the scenario described, explain why the training process was significantly faster on the high-speed network despite the computational resources being identical. Your explanation should focus on the primary factor limiting performance in this distributed system.

Analyzing Network Impact on Distributed Training

Explain why, in a distributed computing system, adding more processing nodes to a task does not always result in a proportional decrease in completion time. Describe the key factor that can limit these performance gains.

The Scalability Paradox in Distributed Systems

A team is training a large computational model on a distributed system. They find that increasing the number of processing nodes from 8 to 16 nearly halves the training time. However, when they increase the nodes from 16 to 32, the training time decreases only slightly. What is the most likely explanation for this diminishing return on performance?

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