Explain ML framework trends
Overview
This question evaluates understanding of modern machine learning framework evolution, the model-to-GPU compilation pipeline, common compiler optimizations (such as kernel fusion, quantization, memory planning), and hardware-aware deployment decisions, and it sits in the ML System Design domain intersecting compilers and GPU execution.
NVIDIA
Aug 4, 2025, 10:55 AM
Software Engineer
Technical Screen
ML System Design
0
0
ML Framework Trends, Compilation Pipeline to GPU, and Hardware-Aware Deployment
Context
You are asked to explain how modern machine learning frameworks evolve and compile models to run efficiently on GPUs. Address differences across frameworks, the model-to-GPU execution pipeline (from frontend to intermediate representations to compilation), common compiler optimizations (e.g., kernel fusion, quantization), and how data-center vs. edge hardware influences these choices.
Tasks
- Framework trends: How has the ecosystem evolved from NumPy to PyTorch to JAX? What high-level trends are happening at the framework level?
- PyTorch vs. JAX: List three key differences.
- Model-to-GPU stages: Describe the stages a model goes through from definition to GPU execution, including the typical frontend, intermediate representation (IR, e.g., ONNX computation graph), and compilation steps.
- GPU optimization techniques: What optimizations are applied during model compilation (e.g., kernel fusion, quantization, memory planning, layout, autotuning)?
- Hardware targets: Contrast data-center hardware vs. edge hardware and explain how that affects compilation and deployment choices.
Solution
Login required
Show