CUDA-X (original) (raw)
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.
cuBLAS
GPU-accelerated basic linear algebra (BLAS) library.
cuFFT
GPU-accelerated library for Fast Fourier Transform implementations.
cuRAND
GPU-accelerated random number generation.
cuSOLVER
GPU-accelerated dense and sparse direct solvers.
cuSPARSE
GPU-accelerated BLAS for sparse matrices.
cuTENSOR
GPU-accelerated tensor linear algebra library.
cuDSS
GPU-accelerated direct sparse solver library.
CUDA Math API
GPU-accelerated standard mathematical function APIs.
AmgX
GPU-accelerated linear solvers for simulations and implicit unstructured methods.
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.
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.
Computational 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.
Quantum 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.
Get Started
cuPQC
SDK of optimized libraries for accelerating post-quantum cryptography (PQC) workflows.
Explore Docs
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.
Explore Docs
RAPIDS cuML
Speed up ML algorithms in scikit-learn, UMAP, and HDBSCAN with zero code changes.
Explore Docs
RAPIDS cuGraph
Scale up and speed up graph analytics with GPU-accelerated NetworkX.
Explore Docs
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.
Learn More
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.
Learn More
Morpheus
Open application framework that optimizes cybersecurity AI pipelines for analyzing large volumes of real-time data.
Learn More
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.
Learn More
Dask
Expand data science pipelines to multiple nodes with RAPIDS on Dask.
Go to GitHub
RAPIDS Accelerator for Apache Spark
Accelerate your existing Apache Spark applications with minimal code changes.
Go to GitHub
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.
Explore Docs
CV-CUDA
Open-source library for high-performance, GPU-accelerated pre- and post-processing in vision AI pipelines.
Learn More
NVIDIA DALI
Portable, open-source library for decoding and augmenting images and videos to accelerate deep learning applications.
Learn More
nvJPEG
High-performance GPU-accelerated library for JPEG decoding.
Learn More
NVIDIA Performance Primitives
GPU-accelerated image, video, and signal processing functions.
Learn More
NVIDIA Video Codec SDK
Hardware-accelerated video encode and decode on Windows and Linux.
Learn More
NVIDIA Optical Flow SDK
Exposes the latest hardware capability of NVIDIA GPUs dedicated to computing the relative motion of pixels between images.
Learn More
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 communication that maximizes bandwidth while maintaining low latency.
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.
Learn More
NVIDIA cuDNN
GPU-accelerated library of primitives for deep neural networks.
Learn More
Partner Libraries
OpenCV
GPU-accelerated open-source library for computer vision, image processing, and machine learning, now supporting real-time operation.
Learn More
FFmpeg
Open-source multimedia framework with a library of plug-ins for audio and video processing.
Learn More
ArrayFire
GPU-accelerated open-source library for matrix, signal, and image processing.
Learn More
MAGMA
GPU-accelerated linear algebra routines for heterogeneous architectures, by Magma.
Learn More
IMSL Fortran Numerical Library
GPU-accelerated open-source Fortran library with functions for math, signal and image processing, and statistics, by RogueWave.
Learn More
Gunrock
Library for graph-processing designed specifically for the GPU.
Learn More
CHOLMOD
GPU-accelerated functions for sparse direct solvers, included in the SuiteSparse linear algebra package, authored by Prof.
Learn More
Triton Ocean SDK
Real-time visual simulation of oceans, water bodies in games, simulation, and training applications, by Triton.
Learn More
CUVIlib
Primitives for accelerating imaging applications in medical, industrial, and defense domains.
Learn More
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.