CUDA libraries facilitate the harnessing of GPUs for various computing tasks. They provide optimized implementation of common algorithms and functions. It enables developers to write high-performance apps to leverage the parallel processing capabilities of GPUs.

The various CUDA libraries are:

cuBLAS provides optimized routines for basic linear algebra operations (matrix multiplication, vector addition and so on)

cuFFT accelerates Fast Fourier Transforms (FFTs). It is crucial for signal processing and image analysis.

cuSPARSE handles sparse matrix computations. It is useful for scientific simulations and ML.

cuDNN is designed for deep learning. It offers high-performance implementations of essential neural network primitives.

In addition to these libraries, there is CUDA-X suite. It empowers developers to create apps that run faster. It unlocks the potential in various fields such as AI, graphics and scientific computing.

Linear algebra libraries are cuBLAS, cuSOLVER and cuSPARSE. Deep learning libraries are cuDNN. It accelerates TensorFlow and PyTorch. Data science libraries are cuFFT and cuRAND for data analysis and ML. Computer vision libraries are cuFFT and cuBLAS. These accelerate image and video processing. Other domains covered are nVJPEG, NCCL and NPP.

These libraries bridge the gap between hardware capabilities of GPUs and the software applications that leverage them. There are significant performance gains.