This package provides the CUDA compiler and the CUDA run-time support libraries for NVIDIA GPUs, all of which are proprietary.

This package accepts CUDA C++ source code in character string form and creates handles that can be used to obtain the CUDA PTX, for further instrumentation with the CUDA Toolkit. It allows to shrink compilation overhead and simplify application deployment.

This package provides a command-line tool to profile CUDA kernels. It enables the collection of a timeline of CUDA-related activities on both CPU and GPU, including kernel execution, memory transfers, memory set and CUDA API calls and events or metrics for CUDA kernels.

This package provides a C++ header-only library that wraps the NVIDIA CUDA Deep Neural Network library (cuDNN) C backend API. This entry point to the same API is less verbose (without loss of control), and adds functionality on top of the backend API, such as errata filters and autotuning.

This package provides Python low-level bindings for NVIDIA CUDA toolkit.

This package provides a an interactive profiler for CUDA and NVIDIA OptiX that provides detailed performance metrics and API debugging via a user interface and command-line tool. Users can run guided analysis and compare results with a customizable and data-driven user interface, as well as post-process and analyze results in their own workflows.

This package provides a high-performance, GPU accelerated JPEG decoding functionality for image formats commonly used in deep learning and hyperscale multimedia applications. The library offers single and batched JPEG decoding capabilities which efficiently utilize the available GPU resources for optimum performance; and the flexibility for users to manage the memory allocation needed for decoding.

The nvJPEG library enables the following functions: use the JPEG image data stream as input; retrieve the width and height of the image from the data stream, and use this retrieved information to manage the GPU memory allocation and the decoding. A dedicated API is provided for retrieving the image information from the raw JPEG image data stream.

The encoding functions of the nvJPEG library perform GPU-accelerated compression of user’s image data to the JPEG bitstream. User can provide input data in a number of formats and colorspaces, and control the encoding process with parameters. Encoding functionality will allocate temporary buffers using user-provided memory allocator.

This package provides an interface for generating PTX code from both binary and text NVVM IR inputs.

This package provides facilities that focus on the simple and efficient generation of high-quality pseudorandom and quasirandom numbers. A pseudorandom sequence of numbers satisfies most of the statistical properties of a truly random sequence but is generated by a deterministic algorithm. A quasirandom sequence of -dimensional points is generated by a deterministic algorithm designed to fill an -dimensional space evenly.

This package provides cuFFT, the NVIDIA® CUDA® Fast Fourier Transform (FFT) product. It consists of two separate libraries: cuFFT and cuFFTW. The cuFFT library is designed to provide high performance on NVIDIA GPUs. The cuFFTW library is provided as a porting tool to enable users of FFTW to start using NVIDIA GPUs with a minimum amount of effort.

The FFT is a divide-and-conquer algorithm for efficiently computing discrete Fourier transforms of complex or real-valued data sets. It is one of the most important and widely used numerical algorithms in computational physics and general signal processing. The cuFFT library provides a simple interface for computing FFTs on an NVIDIA GPU, which allows users to quickly leverage the floating-point power and parallelism of the GPU in a highly optimized and tested FFT library. The cuFFTW library provides the FFTW3 API to facilitate porting of existing FFTW applications.

This package provides a library of functions for performing CUDA accelerated 2D image and signal processing.

The primary library focuses on image processing and is widely applicable for developers in these areas. NPP will evolve over time to encompass more of the compute heavy tasks in a variety of problem domains. The NPP library is written to maximize flexibility, while maintaining high performance.

This package provides a high-level library based on the cuBLAS and cuSPARSE libraries. It consists of two modules corresponding to two sets of API: the cuSolver API on a single GPU; and the cuSolverMG API on a single node multiGPU. Each of these can be used independently or in concert with other toolkit libraries. The intent of cuSolver is to provide useful LAPACK-like features, such as common matrix factorization and triangular solve routines for dense matrices, a sparse least-squares solver and an eigenvalue solver. In addition, cuSolver provides a new refactorization library useful for solving sequences of matrices with a shared sparsity pattern.

This binary extracts information from standalone cubin files and presents them in human readable format. The output of nvdisasm includes CUDA assembly code for each kernel, listing of ELF data sections and other CUDA specific sections. Output style and options are controlled through nvdisasm command-line options. nvdisasm also does control flow analysis to annotate jump/branch targets and makes the output easier to read.

This package provides a <bits/floatn.h> header to override that of glibc and disable float128 support. This is required allow the use of nvcc with CUDA 8.0 and glibc 2.26+. Otherwise, nvcc fails like this:

/gnu/store/…-glibc-2.26.105-g0890d5379c/include/bits/floatn.h(61): error: invalid argument to attribute "__mode__"

/gnu/store/…-glibc-2.26.105-g0890d5379c/include/bits/floatn.h(73): error: identifier "__float128" is undefined

See also https://devtalk.nvidia.com/default/topic/1023776/cuda-programming-and-performance/-request-add-nvcc-compatibility-with-glibc-2-26/1.

This package provides the CUDA C++ developers with building blocks that make it easier to write safe and efficient code. It unifies three essential former CUDA C++ libraries into a single repository:

• Thrust (former repo)

• CUB (former repo)

• libcudacxx (former repo)

This package provides the CUDA compiler and the CUDA run-time support libraries for NVIDIA GPUs, all of which are proprietary.

This package provides a minimal low-level profiling API for CUDA.

This package provides the CUDA compiler and the CUDA run-time support libraries for NVIDIA GPUs, all of which are proprietary.

This package provides the CUDA compiler and the CUDA run-time support libraries for NVIDIA GPUs, all of which are proprietary.

This package provides a binary that prunes host object files and libraries to only contain device code for the specified targets.

This package provides the CUDA Direct Sparse Solver library.

This package provides the proprietary cuTENSOR library for NVIDIA GPUs.

The NVIDIA Management Library Headers (NVML) is a C-based API for monitoring and managing various states of the NVIDIA GPU devices. It provides a direct access to the queries and commands exposed via nvidia-smi.

This package provides the CUDA Deep Neural Network library.