Practical guide: cuDNN Graph API — kernel fusion, autotuning and plan reuse

To the point: the goal is to reduce inference latency and GPU cost by applying low-level optimizations - kernel fusion, autotuning and plan reuse via the cuDNN Frontend Graph API.

How to prepare the environment and requirements

Before deploying, ensure compatible CUDA drivers, matching cuDNN and a supported cuDNN Frontend version; NVIDIA documents the precise API and compatibility (according to NVIDIA Developer Documentation). Validate functional results against your framework (for example PyTorch) as demonstrated in the MarkTechPost tutorial (according to MarkTechPost).

Step-by-step - building the graph, kernel fusion and epilogues

1. Define the computation graph at the cuDNN Frontend level, mapping framework ops to cuDNN operators.
2. Group operators that can be fused (e.g., convolution + bias + activation) into compound nodes so the planner can emit fused kernels (according to NVIDIA).
3. Attach epilogues matching numeric needs (FP32/FP16/FP8-style) and verify outputs against the framework reference (according to MarkTechPost).

How to run autotuning and persist chosen plans

1. Enable cuDNN Frontend autotuning by running graph samples on a representative input set.
2. Persist evaluated "plans" (engine configurations) with metadata: graph signature, dynamic sizes, cuDNN version and device ID.
3. On production startup, load the cached plan and trigger re-autotune if the environment (driver/library/device) differs.

Where risks and limitations lie and criteria for use

Fusion typically benefits compute-bound workloads; for memory-bound kernels it can harm throughput - profile to decide. Dynamic shapes demand either permissive graph signatures or a layered plan cache. Expect to re-tune after driver or cuDNN updates and always re-validate numerics after changes (according to NVIDIA).

Summary

Make reproducibility the priority: 1) construct and test the graph locally, 2) autotune on representative data and cache plans, 3) deploy with validation and re-tune on environment changes. Implementation examples and walkthroughs are available in the MarkTechPost article and NVIDIA documentation (according to MarkTechPost; according to NVIDIA Developer Documentation).

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