Question

A data science team is benchmarking a deep learning model training job. On a general-purpose server CPU with 32 high-performance cores, each iteration is slow, while the same job on a single GPU with thousands of smaller cores runs dramatically faster. A new team member asks why the GPU wins despite the CPU having faster individual cores. What is the best explanation of the architectural reason GPUs accelerate this workload?

Accepted Answer

The GPU has thousands of simpler cores designed to execute the same operation across many data elements in parallel, matching the massive matrix and vector math in neural network training.

Answer

The GPU runs each core at a much higher clock frequency than the CPU, so it completes sequential instructions faster.

Answer

The GPU offloads the operating system and I/O scheduling from the CPU, freeing the CPU to focus on the model math.

Answer

The GPU compresses the training data so fewer computations are required per iteration.