From Model Definition to GPU Execution: Pipeline and Optimizations

You are asked to explain the end-to-end path a machine learning model takes from authoring to high-performance inference on a GPU.

Task

Walk through the stages below and describe what happens at each step:

1. Frontend representation
   • How a model is defined in a high-level framework (e.g., dynamic vs. static graphs, tracing vs. scripting).
2. Export to an intermediate representation (IR)
   • Exporting to ONNX or a similar IR; making shapes/layouts explicit; simplifying the graph.
3. Compilation to device code
   • Lowering from IR to kernel calls or device code; scheduling; autotuning; static vs. dynamic shapes.
4. Runtime execution
   • Memory management, kernel launch, streams, batching, and handling dynamic inputs.

Then, discuss common compiler/runtime optimization techniques and their trade-offs, including:

• Kernel/operator fusion
• Quantization (e.g., INT8, FP16/FP8)
• Operator specialization and autotuning
• Layout and precision selection
• Memory planning and graph partitioning

Finally, contrast considerations for data-center versus edge hardware targets:

• Throughput vs. latency priorities
• Batch size, power/thermal limits, memory budgets
• JIT vs. AOT, startup time, binary size, determinism

Assume a modern GPU software stack with an IR (e.g., ONNX/MLIR/Relay/XLA), a compiler/runtime (e.g., ONNX Runtime, vendor-specific runtimes), and access to common math libraries (e.g., BLAS/DNN). Keep your explanation structured and concise.