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

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 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 functional correctness checking suite included in the CUDA toolkit. This suite contains multiple tools that can perform different type of checks. The memcheck tool is capable of precisely detecting and attributing out of bounds and misaligned memory access errors in CUDA applications, and can also report hardware exceptions encountered by the GPU. The racecheck tool can report shared memory data access hazards that can cause data races. The initcheck tool can report cases where the GPU performs uninitialized accesses to global memory. The synccheck tool can report cases where the application is attempting invalid usages of synchronization primitives.

This package provides tooling to configure the NVSwitch memory fabrics to form one memory fabric among all participating GPUs, and monitors the NVLinks that support the fabric. See docs for more information.

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

This package provides a set of GPU-accelerated basic linear algebra subroutines used for handling sparse matrices that perform significantly faster than CPU-only alternatives. Depending on the specific operation, the library targets matrices with sparsity ratios in the range between 70%-99.9%.

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 CUDA binary files (both standalone and those embedded in host binaries) and presents them in human readable format. The output of cuobjdump includes CUDA assembly code for each kernel, CUDA ELF section headers, string tables, relocators and other CUDA specific sections. It also extracts embedded ptx text from host binaries.

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

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

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.

CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-matrix multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS and cuDNN.

CUTLASS decomposes these ``moving parts'' into reusable, modular software components abstracted by C++ template classes. Primitives for different levels of a conceptual parallelization hierarchy can be specialized and tuned via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies their use as building blocks within custom kernels and applications.

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

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

This package provides a GPU-accelerated library of primitives for deep neural networks, with highly tuned implementations for standard routines such as forward and backward convolution, attention, matmul, pooling, and normalization.

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 a system-wide performance analysis tool designed to visualize an application’s algorithms, identify the largest opportunities to optimize, and tune to scale efficiently across any quantity or size of CPUs and GPUs,from large servers to small systems-on-a-chip.