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GPU-accelerated Libraries for Computing

NVIDIA GPU-accelerated libraries provide highly-optimized functions that perform 2x-10x faster than CPU-only alternatives. Using drop-in interfaces, you can replace CPU-only libraries such as MKL, IPP and FFTW with GPU-accelerated versions with almost no code changes. The libraries can optimally scale your application across multiple GPUs.

GPU-accelerated libraries for linear algebra, signal processing, image and video processing lay the foundation for compute-intensive applications in areas such as molecular 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 libraries use optimized precision to maximize performance for applications such as computer vision, speech processing and natural language processing. Today, NVIDIA’s libraries are running from resource constrained IoT devices, to self driving cars, to the largest supercomputers on the planet.

With NVIDIA’s libraries, you get highly efficient implementations of algorithms that are regularly extended and optimized. Whether you are building a new application or trying to speed up an existing application, NVIDIA’s libraries provide the easiest way to get started with GPUs. You can download NVIDIA libraries as part of the CUDA Toolkit.

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CUDA 10.1: What's New...

Leading Applications and Organizations Using NVIDIA Libraries

High Performance

cuFFT 10.0 - Upto 17TF performance on 16-GPUs 3D 1K FFT

(Click to Zoom)

cuBLAS 10.0 - Upto 90TF of GEMM Performance

NPP 10.0 - Upto 90x speedup on image processing routines over IPP 2018

cuSPARSE 10.0 - Upto 150x speedup over MKL 2018

cuSOLVER 10.0 - Upto 4x faster on symmetric eigensolver

All library benchmarks use NVIDIA Tesla V100 (or where specified P100) GPUs and Intel Skylake 6140 Gold 2.3 GHz processors

Drop-in Acceleration

NVIDIA’s libraries help accelerate existing applications with minimal code changes. You can use them with languages that you already know, including C, C++, Fortran, Python and MATLAB. The libraries work across all NVIDIA GPU families whether they are running in a desktop, cloud or IoT device. So while developing your application, you do not need to bother about differences between development and deployment platforms.

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

Linear Algebra and Math Libraries


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

Signal, Image and Video Libraries


GPU-accelerated library for Fast Fourier Transforms

NVIDIA Performance Primitives

GPU-accelerated library for image and signal processing


High-performance APIs and tools for hardware accelerated video encode and decode

Parallel Algorithm Libraries


Collective Communications Library for scaling apps across multiple GPUs and nodes


GPU-accelerated library for graph analytics


GPU-accelerated library of parallel algorithms and data structures

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



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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