H13-311_V3.5 · Question #110
GPU Good at computationally intensive and easy to parallel programs.
The correct answer is A. TRUE. A (TRUE) is correct because GPUs are architected specifically for tasks that can be broken into thousands of smaller operations running simultaneously - their massively parallel design (thousands of cores vs. a CPU's handful) excels at computationally intensive workloads like…
Question
GPU Good at computationally intensive and easy to parallel programs.
Options
- ATRUE
- BFALSE
How the community answered
(50 responses)- A84% (42)
- B16% (8)
Explanation
A (TRUE) is correct because GPUs are architected specifically for tasks that can be broken into thousands of smaller operations running simultaneously - their massively parallel design (thousands of cores vs. a CPU's handful) excels at computationally intensive workloads like matrix math, image processing, and machine learning where the same operation repeats across large datasets.
B (FALSE) is wrong because it contradicts how GPUs actually work; they were designed precisely for parallelism and heavy computation, not despite it.
Memory tip: Think of a GPU as a massive assembly line with thousands of workers doing simple repetitive tasks at once, versus a CPU as a small team of specialists tackling complex, sequential problems. If the work can be "divided and conquered" in parallel, the GPU wins.
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