NVIDIA CUDA-X
GPU-Accelerated Libraries
NVIDIA® CUDA-X, built on top of NVIDIA CUDA®, is a collection of libraries, tools, and technologies that deliver dramatically higher performance—compared to CPU-only alternatives— across multiple application domains, from artificial intelligence (AI) to high performance computing (HPC).
NVIDIA libraries run everywhere from resource-constrained IoT devices, to self-driving cars, to the largest supercomputers on the planet. As a result, you get highly-optimized implementations of an ever-expanding set of algorithms. Whether you’re building a new application or accelerating an existing application, NVIDIA libraries provide the easiest way to get started with GPU acceleration.
Components
Math Libraries
GPU-accelerated math libraries lay the foundation for compute-intensive applications in areas such as molecular dynamics, computational fluid dynamics, computational chemistry, medical imaging, and seismic exploration.
Parallel Algorithm Libraries
GPU-accelerated libraries of highly efficient parallel algorithms for several operations in C++ and for use with graphs when studying relationships in natural sciences, logistics, travel planning, and more.
Image and Video Libraries
GPU-accelerated libraries for image and video decoding, encoding, and processing that leverage CUDA and specialized hardware components of GPUs.
NVIDIA Performance Primitives
Provides GPU-accelerated image, video, and signal processing functions
NVIDIA Video Codec SDK
A complete set of APIs, samples, and documentation for hardware-accelerated video encode and decode on Windows and Linux
NVIDIA Optical Flow SDK
Exposes the latest hardware capability of NVIDIA Turing™ GPUs dedicated to computing the relative motion of pixels between images
Communication Libraries
Performance-optimized multi-GPU and multi-node communication primitives.
NVSHMEM
OpenSHMEM standard for GPU memory, with extensions for improved performance on GPUs.
NCCL
Open-source library for fast multi-GPU, multi-node communications that maximizes bandwidth while maintaining low latency.
Deep Learning Libraries
GPU-accelerated libraries for Deep Learning applications that leverage CUDA and specialized hardware components of GPUs.
NVIDIA TensorRT™
High-performance deep learning inference optimizer and runtime for production deployment
NVIDIA DeepStream SDK
Real-time streaming analytics toolkit for AI-based video understanding and multi-sensor processing
NVIDIA DALI
Portable, open-source library for decoding and augmenting images and videos to accelerate deep learning applications
Partner Libraries
OpenCV
GPU-accelerated open-source library for computer vision, image processing, and machine learning, now supporting real-time operation
FFmpeg
Open-source multimedia framework with a library of plugins for audio and video processing
IMSL Fortran Numerical Library
GPU-accelerated open-source Fortran library with functions for math, signal, and image processing, statistics, by RogueWave
CHOLMOD
GPU-accelerated functions for sparse direct solvers, included in the SuiteSparse linear algebra package, authored by Prof
Triton Ocean SDK
Real-time visual simulation of oceans, water bodies in games, simulation, and training applications, by Triton.
CUVIlib
Primitives for accelerating imaging applications from medical, industrial, and defense domains
Resources
Get Started
Members of the NVIDIA Developer Program get early access to all CUDA library releases and the NVIDIA online bug reporting and feature request system.