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Abstract Mixed precision methods combine the use of different numerical formats in one computational workload. This document describes the application of mixed precision to deep neural network training. There are numerous benefits to using numerical formats with lower precision than 32-bit floating point. First, they require less memory, enabling the training and deployment of larger neural networ

CUDA Toolkit 13.0 Update 1 - Release Notes 1. Overview Welcome to the release notes for NVIDIA® CUDA® Toolkit 13.0 Update 1. This release includes enhancements and fixes across the CUDA Toolkit and its libraries. This documentation is organized into two main sections: General CUDA Focuses on the core CUDA infrastructure including component versions, driver compatibility, compiler/runtime features

CUDA Installation Guide for Microsoft Windows 1. Overview The CUDA Installation Guide for Microsoft Windows provides step-by-step instructions to help developers set up NVIDIA’s CUDA Toolkit on Windows systems. It begins by introducing CUDA as NVIDIA’s powerful parallel-computing platform—designed to accelerate compute-intensive applications by leveraging GPU capabilities. The guide details essen

A newer version of this product documentation is available. If you are redirected to the main page of the user guide, then this page might have been renamed or removed. NVIDIA® Cumulus Linux is the first full-featured Debian bookworm-based, Linux operating system for the networking industry. This user guide provides in-depth documentation on the Cumulus Linux installation process, system configura

CUDA Installation Guide for Linux 1. Overview The NVIDIA CUDA Installation Guide for Linux provides comprehensive instructions for installing the CUDA Toolkit across multiple Linux distributions and architectures. CUDA® is NVIDIA’s parallel computing platform that enables dramatic performance increases by harnessing GPU power for computational workloads. This guide covers four primary installatio

NVVM IR Specification Reference guide to the NVVM compiler (intermediate representation) based on the LLVM IR. 1. Introduction 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. The NVVM compiler (

» 1. Preparing An Application For Profiling v12.9 | PDF | Archive Profiler User’s Guide The user manual for NVIDIA profiling tools for optimizing performance of CUDA applications. Profiling Overview This document describes NVIDIA profiling tools that enable you to understand and optimize the performance of your CUDA, OpenACC or OpenMP applications. The Visual Profiler is a graphical profiling tool

Parallel Thread Execution ISA Version 9.0 The programming guide to using PTX (Parallel Thread Execution) and ISA (Instruction Set Architecture). 1. Introduction This document describes PTX, a low-level parallel thread execution virtual machine and instruction set architecture (ISA). PTX exposes the GPU as a data-parallel computing device. 1.1. Scalable Data-Parallel Computing using GPUs Driven b

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Developing a Linux Kernel Module using GPUDirect RDMA The API reference guide for enabling GPUDirect RDMA connections to NVIDIA GPUs. 1. Overview GPUDirect RDMA is a technology introduced in Kepler-class GPUs and CUDA 5.0 that enables a direct path for data exchange between the GPU and a third-party peer device using standard features of PCI Express. Examples of third-party devices are: network i

CUDA C++ Best Practices Guide 1. Overview The CUDA C++ Best Practices Guide provides practical guidelines for writing high-performance CUDA applications. It covers optimization strategies across memory usage, parallel execution, and instruction-level efficiency. The guide helps developers identify performance bottlenecks, leverage GPU architecture effectively, and apply profiling tools to fine-tu

CUDA C++ Programming Guide 1. Overview CUDA is a parallel computing platform and programming model developed by NVIDIA that enables dramatic increases in computing performance by harnessing the power of the GPU. It allows developers to accelerate compute-intensive applications using C, C++, and Fortran, and is widely adopted in fields such as deep learning, scientific computing, and high-performa

1. CUDA Samples 1.1. Overview As of CUDA 11.6, all CUDA samples are now only available on the GitHub repository. They are no longer available via CUDA toolkit. 2. Notices 2.1. Notice This document is provided for information purposes only and shall not be regarded as a warranty of a certain functionality, condition, or quality of a product. NVIDIA Corporation (“NVIDIA”) makes no representation

Release Notes CUDA Installation Guides Quick Start Guide Installation Guide Linux Installation Guide Windows CUDA Architecture Guides Ada Compatibility Guide Ada Tuning Guide NVIDIA Ampere GPU Architecture Compatibility Guide NVIDIA Ampere GPU Architecture Tuning Guide Blackwell Compatibility Guide Blackwell Tuning Guide Best Practices Guide CUDA C++ Programming Guide Hopper Compatibility Guide Ho

Release Notes Installation Guides Quick Start Guide Installation Guide Linux Installation Guide Windows Programming Guides Ada Compatibility Guide Ada Tuning Guide NVIDIA Ampere GPU Architecture Compatibility Guide NVIDIA Ampere GPU Architecture Tuning Guide Blackwell Compatibility Guide Blackwell Tuning Guide Best Practices Guide Hopper Compatibility Guide Hopper Tuning Guide Inline PTX Assembly