The NVIDIA CUDA Toolkit provides a development environment for creating high performance GPU-acceleratedapplications. With the CUDA Toolkit, you can develop, optimize, and deploy your applications on GPU-acceleratedembedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers.The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtimelibrary to deploy your application.
Using built-in capabilities for distributing computations across multi-GPU configurations, scientists and researcherscan develop applications that scale from single GPU workstations to cloud installations with thousands of GPUs.
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The CUDA Toolkit End User License Agreement applies to the NVIDIA CUDA Toolkit, the NVIDIA CUDA Samples, the NVIDIA Display Driver, NVIDIA Nsight tools (Visual Studio Edition), and the associated documentation on CUDA APIs, programming model and development tools. If you do not agree with the terms and conditions of the license agreement, then do not download or use the software.
This guide provides a detailed discussion of the CUDA programming model and programming interface. It then describes the hardware implementation, and provides guidance on how to achieve maximum performance. The appendices include a list of all CUDA-enabled devices, detailed description of all extensions to the C++ language, listings of supported mathematical functions, C++ features supported in host and device code, details on texture fetching, technical specifications of various devices, and concludes by introducing the low-level driver API.
This guide presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. The intent is to provide guidelines for obtaining the best performance from NVIDIA GPUs using the CUDA Toolkit.
This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on GPUs based on the NVIDIA Maxwell Architecture. This document provides guidance to ensure that your software applications are compatible with Maxwell.
This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on GPUs based on the NVIDIA Pascal Architecture. This document provides guidance to ensure that your software applications are compatible with Pascal.
This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on GPUs based on the NVIDIA Volta Architecture. This document provides guidance to ensure that your software applications are compatible with Volta.
This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on GPUs based on the NVIDIA Turing Architecture. This document provides guidance to ensure that your software applications are compatible with Turing.
This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on GPUs based on the NVIDIA Ampere GPU Architecture. This document provides guidance to ensure that your software applications are compatible with NVIDIA Ampere GPU architecture.
This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on the Hopper GPUs. This document provides guidance to ensure that your software applications are compatible with Hopper architecture.
This application note is intended to help developers ensure that their NVIDIA CUDA applications will run properly on the Ada GPUs. This document provides guidance to ensure that your software applications are compatible with Ada architecture.
This guide provides detailed instructions on the use of PTX, a low-level parallel thread execution virtual machine and instruction set architecture (ISA). PTX exposes the GPU as a data-parallel computing device.
This document shows how to inline PTX (parallel thread execution) assembly language statements into CUDA code. It describes available assembler statement parameters and constraints, and the document also provides a list of some pitfalls that you may encounter.
The cuBLAS library is an implementation of BLAS (Basic Linear Algebra Subprograms) on top of the NVIDIA CUDA runtime. It allows the user to access the computational resources of NVIDIA Graphical Processing Unit (GPU), but does not auto-parallelize across multiple GPUs.
The NVIDIA GPUDirect Storage cuFile API Reference Guide provides information about the preliminary version of the cuFile API reference guide that is used in applications and frameworks to leverage GDS technology and describes the intent, context, and operation of those APIs, which are part of the GDS technology.
NVIDIA NPP is a library of functions for performing CUDA accelerated processing. The initial set of functionality in the library focuses on imaging and video 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.
NVRTC is a runtime compilation library for CUDA C++. It accepts CUDA C++ source code in character string form and creates handles that can be used to obtain the PTX. The PTX string generated by NVRTC can be loaded by cuModuleLoadData and cuModuleLoadDataEx, and linked with other modules by cuLinkAddData of the CUDA Driver API. This facility can often provide optimizations and performance not possible in a purely offline static compilation.
This guide is intended to help users get started with using NVIDIA CUDA on Windows Subsystem for Linux (WSL 2). The guide covers installation and running CUDA applications and containers in this environment.
A technology introduced in Kepler-class GPUs and CUDA 5.0, enabling a direct path for communication between the GPU and a third-party peer device on the PCI Express bus when the devices share the same upstream root complex using standard features of PCI Express. This document introduces the technology and describes the steps necessary to enable a GPUDirect RDMA connection to NVIDIA GPUs within the Linux device driver model.
This is a reference document for nvcc, the CUDA compiler driver. nvcc accepts a range of conventional compiler options, such as for defining macros and include/library paths, and for steering the compilation process.
The NVIDIA tool for debugging CUDA applications running on Linux and QNX, providing developers with a mechanism for debugging CUDA applications running on actual hardware. CUDA-GDB is an extension to the x86-64 port of GDB, the GNU Project debugger.
The NVIDIA Nsight Compute is the next-generation interactive kernel profiler for CUDA applications. It provides detailed performance metrics and API debugging via a user interface and command line tool.
A number of issues related to floating point accuracy and compliance are a frequent source of confusion on both CPUs and GPUs. The purpose of this white paper is to discuss the most common issues related to NVIDIA GPUs and to supplement the documentation in the CUDA C++ Programming Guide.
In this white paper we show how to use the cuSPARSE and cuBLAS libraries to achieve a 2x speedup over CPU in the incomplete-LU and Cholesky preconditioned iterative methods. We focus on the Bi-Conjugate Gradient Stabilized and Conjugate Gradient iterative methods, that can be used to solve large sparse nonsymmetric and symmetric positive definite linear systems, respectively. Also, we comment on the parallel sparse triangular solve, which is an essential building block in these algorithms.
This application note provides an overview of NVIDIA Tegra memory architecture and considerations for porting code from a discrete GPU (dGPU) attached to an x86 system to the Tegra integrated GPU (iGPU). It also discusses EGL interoperability.
NVVM IR is a compiler IR (intermediate representation) based on the LLVM IR. The NVVM IR is designed to represent GPU compute kernels (for example, CUDA kernels). High-level language front-ends, like the CUDA C compiler front-end, can generate NVVM IR.
I pretty much followed the advice above and got it installed. Drivers only first. Then stepped through it piece at a time till I hit NSight Compute. I got the install file for that from -compute then finished out anything left, but did G-force Experience Last.
Do you already have an Nsight compute installed and try to install again from cuda toolkit ?
Anyway, a workaround for this is to download Nsight compute standalone installer from -overview/nsight-compute/get-started#latest which I think you already know.
I have the same issue here.
At first, the CUDA installer asked me to install Visual Studio, then I installed VSCode and Visual Studio Community, but the CUDA installer finally failed to install Nsight Compute.
So, I continued to install the standalone version of Nsight Compute and rebooted my computer, but the CUDA installer showed it failed to install Nsight Compute again, which made no sense because I had installed that application already.
Is there a specific reason why this is split into three repos? For me as a user I have to say it would be a lot easier to just set up one repo from Nvidia and then install the according packages. Might be simpler, smaller, more robust, also, since I think there is some overlap between the HPC SDK and toolkit repos?
Part two is how to set up a docker container with the HPC SDK compilers.
Is it even a good idea to use the 11.7.1-devel-ubuntu20.04 docker container from Nvidia as a base and add the nvhpc-22-7 package from the according repo, since that also installs a cuda toolkit? Or could I just take a stock Ubuntu 20.04 docker, do the same and end up with a smaller image?
Section 1.2 of the Install Guide is primarily for those installing from the tarball. This can be skipped for the other methods since the installation is implicit in these cases. Would adding a line indicating that this section can be skipped for the yum, zypper, and apt-get installations be sufficient to help clarify this? 152ee80cbc
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