Explain ML compilation optimizations and hardware fit

Q: Explain ML compilation optimizations and hardware fit

This is a ML System Design interview question from NVIDIA for Software Engineer roles. View the full question and solution on PracHub.

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Question

ML Compiler Optimizations and Platform Targeting

Context

You are designing a compiler/runtime stack for deep learning workloads that must run efficiently on both data-center accelerators and resource-constrained edge devices. The interviewer wants to understand your knowledge of compilation-time and run-time optimizations and how hardware targets influence those choices.

Prompt

What compilation and execution optimizations are commonly applied to ML workloads? Discuss techniques such as kernel fusion, quantization, memory planning, scheduling/tiling, layout selection, sparsity, mixed precision, graph-level rewrites, and runtime tactics.
How do data-center versus edge targets influence which optimizations you apply? Explain trade-offs driven by latency vs throughput, power, memory capacity/bandwidth, determinism, and multi-device scaling.

Explain ML compilation optimizations and hardware fit

ML Compiler Optimizations and Platform Targeting

Context

Prompt

Solution

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