What software component optimizes deep learning operations on NVIDIA GPUs?

The software component that optimizes deep learning operations on NVIDIA GPUs is cuDNN. This library specifically provides optimized implementations of routines essential for deep learning, such as convolutions and activation functions, which are critical in neural network training and inference processes.

cuDNN is designed to take full advantage of the power of NVIDIA architectures, significantly speeding up the computations needed for deep learning tasks by utilizing GPU resources efficiently. It allows developers to implement complex neural network models without having to manage the low-level optimizations themselves, making it easier to build and deploy deep learning applications.

While other options are relevant in the context of GPU computing, they serve different purposes. NCCL is mainly focused on collective communication primitives for multi-GPU training, CUDA provides a parallel computing platform and application programming interface for general-purpose computing on GPUs, and TensorFlow is a comprehensive framework for building machine learning models, which can leverage cuDNN for optimized operations but is not specifically designed for optimization itself.