This is a big question and I tried to structure it by separating training-time from inference-time concerns, which helped.
Structure your answer by first distinguishing training vs. inference optimization goals, then walk through each technique (quantization, pruning, distillation, kernel fusion, memory/throughput optimizations) explaining how it works, its goal, and trade-offs. Emphasize practical deployment considerations and NVIDIA-specific tools like TensorRT, cuDNN, and mixed precision.
Pro tip: Tie each technique to concrete NVIDIA hardware features (e.g., Tensor Cores for mixed precision, structured sparsity in Ampere+ GPUs) and mention how you'd validate accuracy and performance using profiling tools like Nsight Systems.
Briefly contrast training (maximize accuracy, handle large batches, backprop) and inference (minimize latency, memory, cost, often batch size 1). This frames why different optimizations apply.
For each, state the goal (e.g., reduce model size/compute), how it works (e.g., FP32→INT8, removing weights, teacher-student), and trade-offs (accuracy loss, retraining needs, hardware support).
Explain how fusion reduces kernel launch overhead and memory traffic, and how memory layout, caching, and batching improve throughput. Mention trade-offs like increased complexity and reduced flexibility.
Show how techniques combine (e.g., quantization + fusion + distillation) and the order of application. Highlight NVIDIA tools (TensorRT, cuDNN, DALI) that automate or support these optimizations.
Conclude with a balanced view: accuracy vs. speed, development effort vs. gain, and the importance of profiling and accuracy checks before deployment.
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Knew pipeline parallelism reasonably well but tensor parallelism tripped me up when they pushed on the all-reduce communication pattern across devices.
Start by defining both parallelism strategies and explaining their mechanics, then contrast their communication patterns and trade-offs. Use concrete examples (e.g., Transformer layers) to illustrate when each is preferred, and discuss bottlenecks like communication overhead and pipeline bubbles. Conclude with how they can be combined in practice.
Pro tip: Emphasize that tensor parallelism is typically used within a node (NVLink) while pipeline parallelism spans nodes (InfiniBand), and mention that hybrid approaches (e.g., Megatron-LM) are common in large-scale training.
Clearly define tensor parallelism (splitting individual layers/operations across devices) and pipeline parallelism (splitting model layers into stages across devices). Highlight that tensor parallelism partitions computations within a layer, while pipeline parallelism partitions the model vertically.
Describe how tensor parallelism requires all-reduce or all-gather communications for each layer (e.g., splitting weight matrices and combining results). Pipeline parallelism uses point-to-point communication between stages, with micro-batches to keep devices busy.
Compare communication overhead, scalability, and ease of implementation. Tensor parallelism has higher communication frequency but lower latency per op; pipeline parallelism has lower communication frequency but suffers from pipeline bubbles. Choose tensor parallelism for intra-node high-bandwidth interconnects; pipeline parallelism for inter-node scaling.
For tensor parallelism, bottlenecks include all-reduce latency and bandwidth saturation. For pipeline parallelism, bottlenecks include pipeline bubbles (idle time) and load imbalance across stages. Mention that both can be mitigated with techniques like overlapping communication and computation, and micro-batch tuning.
Summarize that in practice, large models often use a combination (e.g., tensor parallelism within a node and pipeline parallelism across nodes). Mention frameworks like Megatron-LM and DeepSpeed that implement these strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.