A data-centre engineer is evaluating whether to assign a large matrix-multiplication workload from a deep learning training job to a GPU or a CPU. Which architectural characteristic most directly explains why a GPU handles this workload more efficiently?
- AGPUs contain a small number of high-frequency cores optimised for low-latency branch prediction and serial execution.
- BGPUs use a DPU co-processor to offload matrix arithmetic from the main processing units, freeing those units for other tasks.
- CGPUs expose thousands of simpler cores that execute the same instruction across many data elements simultaneously, matching the uniform parallel structure of matrix operations. Correct
- DGPUs prioritise large per-core cache hierarchies that keep frequently accessed matrix rows resident, reducing instruction latency.
Why A is wrong: This describes the CPU design philosophy, not the GPU. CPUs favour latency-optimised cores suited to sequential, branchy code rather than the throughput model used for matrix work.
Why B is wrong: A DPU (Data Processing Unit) offloads networking, storage, and security tasks from the host CPU; it is not a component inside a GPU and plays no role in matrix arithmetic acceleration.
Why C is correct: The GPU Single Instruction, Multiple Thread model runs thousands of threads in lock-step, directly exploiting the uniform, data-parallel nature of matrix multiplication where the same arithmetic is applied to large arrays of values.
Why D is wrong: CPUs invest heavily in large, multi-level caches to reduce latency for individual threads. GPUs instead hide latency by switching between many concurrent threads rather than caching for a few fast ones.