Fusion and CUDA graphs: less dispatch, fewer round trips

Picture it

Imagine an assembly line where a part is repeatedly taken off the belt, carried to a supervisor for inspection, and then put back. Fusion merges these steps, keeping the part in the worker's hands for multiple modifications without the wasteful handoffs. CUDA graphs take this a step further by recording the entire symphony of tasks into a single sheet of music. Instead of the conductor calling out each note individually, the orchestra plays the whole fluid sequence uninterrupted.

What to understand

• Kernel fusion, layouts, tiling, and mixed precision
• Graph capture, shape buckets, padding, compilation, and warmup

Follow the references

1. 1 · Tutorials

   Triton · GPU programming tutorials ↗

   Compare vector addition, fused softmax, and matrix multiplication tutorials.

2. 2 · Documentation

   vLLM · Optimization and tuning ↗

   Read the CUDA graph and compilation guidance for your engine version.

Make it concrete

Compare graph-enabled and eager execution on one supported engine. Sweep batch shapes; record latency and extra memory as well as capture/warmup time.