The CUDA Library Samples repository contains various examples that demonstrate the use of GPU-accelerated libraries in CUDA. These libraries enable high-performance computing in a wide range of applications, including math operations, image processing, signal processing, linear algebra, and compression. The samples included cover:
- Math and Image Processing Libraries
- cuBLAS (Basic Linear Algebra Subprograms)
- cuTENSOR (Tensor Linear Algebra)
- cuSPARSE (Sparse Matrix Operations)
- cuSOLVER (Dense and Sparse Solvers)
- cuFFT (Fast Fourier Transform)
- cuRAND (Random Number Generation)
- NPP (Image and Video Processing)
- nvJPEG (JPEG Encode/Decode)
- nvCOMP (Data Compression)
- and more...
The CUDA Library Samples are provided by NVIDIA Corporation as Open Source software, released under the 3-clause "New" BSD license. These examples showcase how to leverage GPU-accelerated libraries for efficient computation across various fields.
For more information on the available libraries and their uses, visit GPU Accelerated Libraries.
Explore the examples of each CUDA library included in this repository:
- cuBLAS - GPU-accelerated basic linear algebra (BLAS) library
- cuBLASLt - Lightweight BLAS library
- cuBLASMp - Multi-process BLAS library
- cuBLASDx - Device-side BLAS extensions
- cuDSS - GPU-accelerated linear solvers
- cuFFT - Fast Fourier Transforms
- cuFFTMp - Multi-process FFT
- cuFFTDx - Device-side FFT extensions
- cuRAND - Random number generation
- cuSOLVER - Dense and sparse direct solvers
- cuSOLVERMp - Multi-process solvers
- cuSPARSE - BLAS for sparse matrices
- cuSPARSELt - Lightweight BLAS for sparse matrices
- cuTENSOR - Tensor linear algebra library
- cuTENSORMg - Multi-GPU tensor linear algebra
- NPP - GPU-accelerated image, video, and signal processing functions
- NPP+ - C++ extensions for NPP
- nvJPEG - High-performance JPEG encode/decode
- nvJPEG2000 - JPEG2000 encoding/decoding
- nvTIFF - TIFF encoding/decoding
- nvCOMP - Data compression and decompression
Each sample provides a practical use case for how to apply these libraries in real-world scenarios, showcasing the power and flexibility of CUDA for a wide variety of computational needs.
For more information and documentation on CUDA libraries, please visit:
The CUDA Library Samples are distributed under the 3-clause "New" BSD license. For more details, refer to the license terms below:
Copyright (c) 2022-2024 NVIDIA CORPORATION AND AFFILIATES. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this list of
conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this list of
conditions and the following disclaimer in the documentation and/or other materials
provided with the distribution.
* Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
to endorse or promote products derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.