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This tutorial provides a deep dive into NVIDIA's cuDNN Frontend Graph API, demonstrating how to construct custom kernel fusions and manage execution plans directly beneath high-level framework abstractions like PyTorch. By validating fused operations against standard references and exploring autotuning configurations, the content offers critical insights into optimizing deep learning workloads for specific hardware architectures and precision formats such as FP8.

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Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend

MarkTechPost15 septembrie 20265 min de cititArticolul și perspectiva sunt în engleză.

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.

In this tutorial, we work through the cuDNN Frontend‘s graph API from below the framework: we describe a computation as a graph of operations, let cuDNN pick an engine to run it, and then take control of that choice ourselves. Every kernel we build here is expressed the same way: we declare tensors by their dimensions and strides, chain operations onto them, run the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans, and then execute against a variant pack of pointers. We…

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