Gx Developer Programming Manual Pdf

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Maureen Quartaro

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Aug 3, 2024, 10:40:17 AM8/3/24

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Electronic versions of these documents allow you to quickly get the information you need and print only the pages you want. The Intel 64 and IA-32 architectures software developer's manuals are now available for download via one combined volume, a four-volume set, or a ten-volume set. All content is identical in each set; see details below.

The downloadable PDFs of all IA-32 Architectures Software Developer's Manual volumes are at version 084.
The downloadable PDF of the Intel 64 and IA-32 Architectures Optimization Reference Manual Volume 1 is at version 050, and Volume 2 is at version 050.
Additional specifications, application notes, and technical papers may also be downloaded.

This set contains identical information as the four-volume set. For convenience, it is separated into ten shorter PDFs: volume 1, volume 2A, volume 2B, volume 2C, volume 2D, volume 3A, volume 3B, volume 3C, volume 3D, and volume 4. This set is better suited to those with slower connection speeds.

The Intel 64 and IA-32 architectures optimization reference manual provides information on current Intel microarchitectures. These two volumes describe code optimization techniques that enable you to tune your application for highly optimized results when running on current Intel processors.

A public repository is available with open-source code samples from select chapters of this manual. These code samples are released under a 0-Clause BSD license. Intel provides additional code samples and updates to the repository as the samples are created and verified.

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.

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.

By programmer's guide I mean a manual for us, the programmers. The manual should contain how to write programs for PIC micros (e.g., how to write interrupts, what does __config do, which files to include etc).