Question

A data science team benchmarks the same deep learning training job on a high-end multi-core CPU server and on a single data-center GPU. The GPU completes each training epoch roughly 30x faster. When asked to explain the primary architectural reason for this speedup to non-technical management, which explanation is most accurate?

Accepted Answer

The GPU contains thousands of smaller cores plus high-bandwidth memory, allowing massive parallel execution of the matrix and vector operations that dominate deep learning.

Answer

The GPU runs at a much higher clock frequency per core than the CPU, so each individual instruction executes far faster.

Answer

The GPU includes a larger operating-system cache that stores the entire dataset, eliminating disk reads during training.

Answer

The GPU offloads work to the CPU's idle cores, effectively doubling the number of sequential threads available for the job.