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.
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.
GPU-accelerated basic linear algebra (BLAS) library
GPU-accelerated library for Fast Fourier Transforms
CUDA Math Library
GPU-accelerated standard mathematical function library
GPU-accelerated random number generation (RNG)
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.
GPU-accelerated library of C++ parallel algorithms and data structures
Computational Lithography Library
Targeting the modern-day challenges of nanoscale computational lithography.
Library with optimized tools and algorithms to GPU-accelerate computational lithography and the manufacturing of semiconductors
Image and Video Libraries
GPU-accelerated libraries for image and video decoding, encoding, and processing that leverage CUDA and specialized hardware components of GPUs.
High performance GPU-accelerated library for JPEG decoding
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
Performance-optimized multi-GPU and multi-node communication primitives.
Deep Learning Libraries
GPU-accelerated libraries for Deep Learning applications that leverage CUDA and specialized hardware components of GPUs.
GPU-accelerated library of primitives for deep neural networks
High-performance deep learning inference optimizer and runtime for production deployment
Platform for developing engaging and contextual AI-powered conversation apps
NVIDIA DeepStream SDK
Real-time streaming analytics toolkit for AI-based video understanding and multi-sensor processing
Portable, open-source library for decoding and augmenting images and videos to accelerate deep learning applications
GPU-accelerated open-source library for computer vision, image processing, and machine learning, now supporting real-time operation
Open-source multimedia framework with a library of plugins for audio and video processing
GPU-accelerated open source library for matrix, signal, and image processing
GPU-accelerated linear algebra routines for heterogeneous architectures, by Magma
IMSL Fortran Numerical Library
GPU-accelerated open-source Fortran library with functions for math, signal, and image processing, statistics, by RogueWave
Library for graph-processing designed specifically for the GPU
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.
Primitives for accelerating imaging applications from medical, industrial, and defense domains
Members of the NVIDIA Developer Program get early access to all CUDA library releases and the NVIDIA online bug reporting and feature request system.