NVIDIA CUDA-X
GPU-Accelerated Libraries
NVIDIA CUDA-X™, built on NVIDIA CUDA®, is a collection of libraries, tools, and technologies that deliver dramatically higher performance—compared to CPU-only alternatives—across application domains, including AI and high-performance computing.
NVIDIA libraries run everywhere from resource-constrained IoT devices to self-driving cars to the largest supercomputers on the planet. As a result, users receive highly optimized implementations of an ever-expanding set of algorithms. Whether building a new application or accelerating an existing application, developers can tap NVIDIA libraries for 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.
Thrust
GPU-accelerated library of C++ parallel algorithms and data structures
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Computational Lithography Library
Targeting the modern-day challenges of nanoscale computational lithography.
cuLitho
Library with optimized tools and algorithms to GPU accelerate computational lithography and the manufacturing of semiconductors
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Image and Video Libraries
GPU-accelerated libraries for image and video decoding, encoding, and processing that use CUDA and specialized hardware components of GPUs.
NVIDIA Performance Primitives
GPU-accelerated image, video, and signal processing functions
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NVIDIA Video Codec SDK
Complete set of application programming interfaces, samples, and documentation for hardware-accelerated video encode and decode on Windows and Linux
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NVIDIA Optical Flow SDK
Exposes latest hardware capability of NVIDIA GPUs dedicated to computing the relative motion of pixels between images
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Communication Libraries
Performance-optimized multi-GPU and multi-node communication primitives.
NVSHMEM
OpenSHMEM standard for GPU memory, with extensions for improved performance on GPUs.
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NCCL
Open-source library for fast multi-GPU, multi-node communication that maximizes bandwidth while maintaining low latency.
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Deep Learning Libraries
GPU-accelerated libraries for deep learning applications that use CUDA and specialized hardware components of GPUs.
NVIDIA TensorRT
High-performance deep learning inference optimizer and runtime for production deployment
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NVIDIA DeepStream SDK
Real-time streaming analytics toolkit for AI-based video understanding and multi-sensor processing
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NVIDIA DALI
Portable, open-source library for decoding and augmenting images and videos to accelerate deep learning applications
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Partner Libraries
OpenCV
GPU-accelerated open-source library for computer vision, image processing, and machine learning, now supporting real-time operation
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FFmpeg
Open-source multimedia framework with a library of plug-ins for audio and video processing
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IMSL Fortran Numerical Library
GPU-accelerated open-source Fortran library with functions for math, signal and image processing, and statistics, by RogueWave
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CHOLMOD
GPU-accelerated functions for sparse direct solvers, included in the SuiteSparse linear algebra package, authored by Prof
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Triton Ocean SDK
Real-time visual simulation of oceans, water bodies in games, simulation, and training applications, by Triton
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CUVIlib
Primitives for accelerating imaging applications in medical, industrial, and defense domains
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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.