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# NVIDIA cuDNN

The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. It provides highly tuned implementations of operations arising frequently in deep neural network (DNN) applications:

* Scaled dot-product attention
* Convolution, including cross-correlation
* Matrix multiplication
* Normalizations, softmax, and pooling
* Arithmetic, mathematical, relational, and logical pointwise operations

Beyond just providing high-performance implementations of individual operations, cuDNN also supports a flexible set of multi-operation fusion patterns for further optimization. The goal is to achieve the best available performance on NVIDIA GPUs for important deep learning use cases.

In cuDNN, both single-operation and multi-operation computations are expressed as operation graphs. The following APIs are available for constructing these graphs:

* Python API
* C++ API

The [NVIDIA cuDNN frontend API](/developer/overview) provides a simplified programming model that is sufficient for most use cases.

Use the [NVIDIA cuDNN backend API](https://docs.nvidia.com/deeplearning/cudnn/backend/latest/api/overview.html#api-overview) only if you want to use the legacy fixed-function routines that are not graph-based interfaces and are not exposed by the frontend API layers, or if you need a C-only interface.