NVIDIA CUDA-X Libraries
NVIDIA CUDA-X? Libraries, built on CUDA?, is a collection of libraries 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
CUDA 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.
NVIDIA Math Libraries in Python
Enabling GPU-accelerated math operations for the Python ecosystem.
nvmath-python
nvmath-python (Beta) is an open source library that provides high-performance access to the core mathematical operations in the NVIDIA math libraries.
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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.
Learn MoreComputational Lithography Library
Targeting the modern-day challenges of nanoscale computational lithography.
cuLitho
Library with optimized tools and algorithms to accelerate computational lithography and the manufacturing of semiconductors using GPUs.
Learn MoreQuantum Libraries
Enabling simulation, HPC integration and AI for quantum computing.
cuQuantum
NVIDIA cuQuantum is a set of highly optimized libraries for accelerating quantum computing simulations.
cuPQC
SDK of optimized libraries for accelerating post-quantum cryptography (PQC) workflows.
Data Processing Libraries
GPU-accelerated libraries to accelerate data processing workflows for tabular, text, and image data.
RAPIDS cuDF
Accelerate tabular data, including pandas and Polars, with zero code changes.
RAPIDS cuML
Speed up ML algorithms in scikit-learn, UMAP, and HDBSCAN with zero code changes.
RAPIDS cuGraph
Scale up and speed up graph analytics with GPU-accelerated NetworkX.
NVIDIA cuVS
Apply cuVS algorithms to accelerate vector search for data mining and semantic search applications – including world-class performance from the GPU-native nearest neighbors algorithm CAGRA.
NeMo Curator
NVIDIA NeMo Curator improves generative AI model accuracy by processing text, image, and video data at scale for training and customization. It also provides pre-built pipelines for generating synthetic data to customize and evaluate generative AI systems.
Morpheus
Open application framework that optimizes cybersecurity AI pipelines for analyzing large volumes of real-time data.
GPU Direct Storage
NVIDIA GPUDirect? Storage creates a direct data path between local or remote storage, such as NVMe or NVMe over Fabrics (NVMe-oF), and GPU memory.
Dask
Expand data science pipelines to multiple nodes with RAPIDS on Dask.
RAPIDS Accelerator for Apache Spark
Accelerate your existing Apache Spark applications with minimal code changes.
Image and Video Libraries
GPU-accelerated libraries for image and video decoding, encoding, and processing that use CUDA and specialized hardware components of GPUs.
RAPIDS cuCIM
Accelerate input/output (IO), computer vision, and image processing of n-dimensional, especially biomedical images.
CV-CUDA
Open-source library for high-performance, GPU-accelerated pre- and post-processing in vision AI pipelines.
NVIDIA DALI
Portable, open-source library for decoding and augmenting images and videos to accelerate deep learning applications.
nvJPEG
High-performance GPU-accelerated library for JPEG decoding.
NVIDIA Performance Primitives
GPU-accelerated image, video, and signal processing functions.
NVIDIA Video Codec SDK
Hardware-accelerated video encode and decode on Windows and Linux.
NVIDIA Optical Flow SDK
Exposes the latest hardware capability of NVIDIA 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.
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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 Core 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.
NVIDIA cuDNN
GPU-accelerated library of primitives for deep neural networks.
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 plug-ins for audio and video processing.
ArrayFire
GPU-accelerated open-source library for matrix, signal, and image processing.
MAGMA
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, and statistics, by RogueWave.
Gunrock
Library for graph-processing designed specifically for the GPU.
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 in medical, industrial, and defense domains.
Resources
Documentation
Training
Community
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.