Start by defining the key architectural differences between GPUs and CPUs, then map those differences to the types of computations they excel at. Use concrete examples like matrix multiplication and graphics rendering to illustrate your points, and tie back to ARM's ecosystem where relevant.
Pro tip: Mention that GPUs are not universally faster—they excel when the problem has high data parallelism and minimal branching. Showing awareness of trade-offs (e.g., latency vs. throughput) demonstrates engineering maturity.
Explain that GPUs have many simple cores designed for throughput, with SIMD/SIMT execution and deep memory hierarchies. Contrast with CPUs' few complex cores optimized for latency.
Describe patterns like data parallelism, regular memory access, and high arithmetic intensity. Give examples: matrix operations, image processing, neural networks, and scientific simulations.
Connect each pattern to architectural features: massive thread-level parallelism hides memory latency, SIMT amortizes instruction fetch/decode, and wide SIMD units boost throughput.
Note that GPUs struggle with divergent branching, irregular data structures, and low-parallelism tasks. This shows balanced understanding.
If relevant, mention ARM's Mali GPUs or compute platforms, and how they address mobile/embedded constraints like power efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.