Question

A data science team notices that their deep learning training jobs run dramatically faster on GPUs than on high-end CPUs. When explaining this to management, which characteristic of GPU architecture best accounts for the acceleration of neural network training workloads?

Accepted Answer

GPUs contain thousands of smaller cores that execute the same operation across many data elements in parallel, matching the matrix and vector math in neural networks

Answer

GPUs run at significantly higher clock speeds than CPUs, allowing each individual instruction to complete faster

Answer

GPUs use larger per-core cache hierarchies that eliminate the need to access system memory during training

Answer

GPUs process instructions strictly one at a time but with far lower latency per instruction than a CPU