Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend
MarkTechPost Sana Hassan
The article presents a tutorial that builds cuDNN Frontend graph API examples that fuse convolution+bias+ReLU, then expands to autotuning across multiple engine configurations and plan handling. It validates numerical correctness by asserting the maximum error stays below 5e-2 versus a PyTorch reference. As a result, it shows how to benchmark fused execution and how shipping a preselected autotuned plan (or a serialized one) can outperform relying on cuDNN’s default engine pick for fixed “hot” shapes.
Why it matters
Learn how to leverage NVIDIA’s cuDNN Frontend Graph API to build custom kernel fusions, autotuning engine configurations, FP8-style epilogues, scaled dot-product attention, dynamic shapes, and CUDA graph captures. This practical tutorial demonstrates how to optimize deep learning computations directly below framework abstractions while validating results against PyTorch The post Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend appeared first on MarkTechPost.
Related stories
Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference
MarkTechPost · 4 days ago ·
3
Accelerating Transformer Training with NVIDIA Transformer Engine, Fused Kernels, BF16, FP8, and GPU Benchmarking
MarkTechPost · 1 month ago ·
43
Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel
Hugging Face · 2 months ago ·
7