Explain ML compilation optimizations and hardware fit
Company: NVIDIA
Role: Software Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Technical Screen
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?
Quick Answer: 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.