This one I thought I had cold, but partway through explaining the rasterization stage I realized I was glossing over things like perspective-correct interpolation and early-z rejection.
Structure your answer as a pipeline, starting from vertex input and ending with pixel output, highlighting key stages and their purposes. Emphasize how modern GPUs parallelize these stages and the trade-offs involved in their design.
Pro tip: Mention that while the pipeline is conceptually sequential, modern GPUs use extensive parallelism and fixed-function hardware for efficiency, and that ARM's Mali GPUs use a tile-based rendering approach to reduce memory bandwidth.
Describe how input vertices are transformed from object space to clip space via model, view, and projection matrices, and how vertex shaders allow programmable per-vertex operations.
Explain how vertices are assembled into triangles, clipped against the view frustum, and prepared for rasterization, including perspective divide and viewport mapping.
Detail how triangles are converted into fragments (potential pixels) by determining which pixels are covered, and how attributes are interpolated across the triangle.
Cover how fragment shaders compute color and depth per fragment, including texture mapping, lighting, and other per-pixel effects.
Describe how fragments are tested (depth, stencil, blending) and written to the framebuffer, and how modern GPUs optimize this with techniques like early-z and tile-based rendering.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second and started talking about out-of-order execution before catching myself.
Start by defining what a pipelined GPU execution unit is and why register read-after-write (RAW) hazards occur. Then explain the common hardware and software techniques used to handle these hazards, such as scoreboarding, operand forwarding, and compiler scheduling. Finally, discuss the trade-offs between these approaches in terms of performance, complexity, and power, relating them to GPU architecture.
Pro tip: Emphasize that GPUs often rely on massive multithreading to hide latency, so RAW hazards are frequently mitigated by switching to other warps rather than complex forwarding logic. This shows you understand the GPU design philosophy.
Explain that a RAW hazard occurs when an instruction needs to read a register that a previous instruction has not yet written back. In a pipelined execution unit, this can cause stalls or incorrect results if not handled.
Discuss techniques like scoreboarding, which tracks register availability, and operand forwarding (bypassing), where results are forwarded directly from the pipeline stage that produces them to the stage that needs them.
Highlight that GPUs often use warp scheduling to hide latency: when a warp stalls due to a RAW hazard, the scheduler switches to another ready warp. This reduces the need for complex forwarding.
Note that compilers can reorder instructions or insert NOPs to avoid hazards, and that GPU ISAs may include explicit dependency barriers or scheduling hints.
Compare the cost and benefit of each method: forwarding adds hardware complexity but reduces stalls; warp switching requires many warps but is simpler; compiler scheduling is free at runtime but may increase code size.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt like the easiest of the three but I probably oversimplified.
Start by defining the key architectural differences between GPUs and CPUs, then map those differences to the categories of computation they excel at. Use concrete examples like matrix multiplication and stencil computations 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 arithmetic intensity, but suffer on irregular or latency-sensitive tasks. This shows you understand trade-offs, which is crucial for system design roles.
Briefly explain that CPUs are optimized for low-latency serial execution with large caches and complex control, while GPUs prioritize throughput with many simple cores and high memory bandwidth.
List categories such as data-parallel operations, regular stencil computations, dense linear algebra, and throughput-oriented tasks like graphics and deep learning.
For each category, explain which GPU features (SIMT execution, massive multithreading, wide memory buses) make it suitable.
Acknowledge scenarios where GPUs are less effective, such as irregular parallelism, branch-heavy code, or low arithmetic intensity, to show balanced understanding.
If possible, mention how ARM's GPU architectures (e.g., Mali) or heterogeneous computing trends align with these principles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.