Which component manages the distribution of deep learning model training across multiple GPUs?

The correct choice, which pertains to the component that manages the distribution of deep learning model training across multiple GPUs, is NCCL (NVIDIA Collective Communications Library). NCCL is specifically designed to facilitate efficient multi-GPU and multi-node communication by providing optimized routines for collective operations such as all-reduce, all-gather, and broadcast. This support is vital for deep learning tasks where model parameters must be synchronized among multiple GPUs to ensure that they learn effectively and efficiently.

In the context of training deep learning models, NCCL plays a crucial role in minimizing the overhead of data transfer between devices and maximizing throughput, thereby improving training efficiency. By leveraging NCCL, developers can scale their deep learning workloads across multiple GPUs seamlessly.

TensorFlow is a deep learning framework that can utilize multiple GPUs, but it relies on libraries like NCCL to handle the low-level communication. CUDA is a parallel computing platform and application programming interface (API) that allows developers to utilize the power of NVIDIA GPUs for general-purpose processing, but it does not specifically manage the distribution of model training. cuDNN is a GPU-accelerated library for deep neural networks that optimizes the performance of various neural network operations but is not responsible for managing multi-GPU distribution.