GPU-Accelerated Libraries for AI and HPC
NVIDIA CUDA-X, built on top of 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 to high performance computing.
GPU-accelerated math and image and video processing libraries lay the foundation for compute-intensive applications in areas such as molecular dynamics, computational fluid dynamics, computational chemistry, medical imaging and seismic exploration. For deep learning, NVIDIA provides specialized libraries that are integrated with all the leading deep learning frameworks. NVIDIA’s libraries run everywhere from resource constrained IoT devices, to self driving cars, to the largest supercomputers on the planet.
With NVIDIA’s libraries, you get highly optimized implementations of an ever-expanding set of algorithms. Whether you are building a new application or accelerating an existing application, NVIDIA’s libraries provide the easiest way to get started with GPUs.Download Now CUDA 10.1: What's New...
GPU-accelerated standard BLAS library
CUDA Math Library
GPU-accelerated standard mathematical function library
GPU-accelerated BLAS for sparse matrices
GPU-accelerated random number generation (RNG)
Dense and sparse direct solvers for Computer Vision, CFD, Computational Chemistry, and Linear Optimization applications
GPU accelerated linear solvers for simulations and implicit unstructured methods
Parallel Algorithm Libraries
GPU-accelerated library for graph analytics
GPU-accelerated library of parallel algorithms and data structures
Image and Video Processing Libraries
GPU-accelerated library for JPEG encoding and decoding
NVIDIA Performance Primitives
GPU-accelerated library for image and signal processing
NVIDIA Codec SDK
High-performance APIs and tools for hardware accelerated video encode and decode
OpenSHMEM standard for GPU memory, with extensions for improved performance on GPUs
Collective Communications Library for scaling apps across multiple GPUs and nodes
Deep Learning Libraries
GPU-accelerated open-source library for computer vision, image processing and machine learning, now supporting real-time operation
GPU-accelerated open-source Fortran library with functions for math, signal and image processing, statistics, by RogueWave
GPU-accelerated functions for sparse direct solvers, included in SuiteSparse linear algebra package authored by Prof.
GPU-accelerated computational geometry engine for advanced GIS, EDA, computer vision, and motion planning, by Fixstars
Leading Applications and Organizations Using NVIDIA Libraries
Members of the NVIDIA Developer Program get early access to the next CUDA Library release, and access to NVIDIA’s online bug reporting and feature request system.Download Now