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NVIDIA CUDA-X

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.

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CUDA 10.1: What's New...

Math Libraries

cuBLAS

GPU-accelerated standard BLAS library

CUDA Math Library

GPU-accelerated standard mathematical function library

cuSPARSE

GPU-accelerated BLAS for sparse matrices

cuRAND

GPU-accelerated random number generation (RNG)

cuSOLVER

Dense and sparse direct solvers for Computer Vision, CFD, Computational Chemistry, and Linear Optimization applications

AmgX

GPU accelerated linear solvers for simulations and implicit unstructured methods


Parallel Algorithm Libraries

nvGRAPH

GPU-accelerated library for graph analytics

Thrust

GPU-accelerated library of parallel algorithms and data structures


Image and Video Processing Libraries

nvJPEG

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


Communication Libraries

NVSHMEM

OpenSHMEM standard for GPU memory, with extensions for improved performance on GPUs

NCCL

Collective Communications Library for scaling apps across multiple GPUs and nodes


Deep Learning Libraries

GPU-accelerated library of primitives for deep neural networks

GPU-accelerated neural network inference library for building deep learning applications

Advanced GPU-accelerated video inference library


Partner Libraries

GPU-accelerated open-source library for computer vision, image processing and machine learning, now supporting real-time operation

Open-source multi-media 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

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 SuiteSparse linear algebra package authored by Prof.

GPU-accelerated linear algebra library by EM Photonics

GPU-accelerated linear algebra (LA) routines for the R platform for statistical computing supporting heterogeneous

GPU-accelerated computational geometry engine for advanced GIS, EDA, computer vision, and motion planning, by Fixstars

Real-time visual simulation of oceans, water bodies in games, simulation, and training applications, by Triton


Leading Applications and Organizations Using NVIDIA Libraries


Resources


Availability

NVIDIA Accelerated Computing Libraries are freely available as part of the CUDA Toolkit and OpenACC Toolkit. Deep Learning libraries are available separately.

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.

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