Explain ML compilation optimizations and hardware fit
NVIDIASoftware Engineer · Technical Screen
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This question evaluates a candidate's understanding of ML compiler optimizations and hardware-aware runtime strategies, assessing competencies in techniques such as kernel fusion, quantization, memory planning, scheduling/tiling, layout selection, sparsity, mixed precision, graph-level rewrites and runtime tactics.
What compilation optimization techniques are used for ML workloads (e.g., kernel fusion, quantization, memory planning)? Are you familiar with data-center versus edge hardware, and how do target platforms influence which optimizations you apply?
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