Release Notes The Release Notes for the CUDA Toolkit. EULA 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. Unit 4 Assignment 1 1/11/17 OSX Scavenger Hunt 1. What are five MAC OSX interfaces? Finder, Apple Menu, System Preferences, The Dock, and File management 2. How do I turn on AirPort? From the Apple menu, choose System Preferences. From the View menu, choose Network. In the list of available network connections, select AirPort. Click the '-' button to remove the AirPort interface. Programming Assignment 1: Percolation. Write a program to estimate the value of the percolation threshold via Monte Carlo simulation. Install a Java programming environment. Install a Java programming environment on your computer by following these step-by-step instructions for your operating system Mac OS X Windows Linux.After following these instructions you will have.

• Egl Assignment 1 Mac Os 11
• Egl Assignment 1 Mac Os X
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: Research

At first I had decided to build Firefox using Visual Studio 2005 on Windows XP. No knowing where to start I did what I usually do when I don’t have a clue about something. I googled it. Using the following search query “build Firefox using visual studio” in google I was directed to [1].

I followed the instructions on that page up to the point where the buildsetup.bat needed to be executed. It did not work. I was unable to execute the script. I knew this because it wasn’t setting up the correct environmental variables for CVS to work properly. After an hour of struggle with this problem I decided to give up on Windows and decided to try my luck with OS X.

I chose to build Firefox using the release 1.5.0.6 source which was available online on the mozilla release website. I downloaded firefox-1.5.0.6-source.tar.bz2 and then extracted the content of the archive into /Users/Moe/proj/.

My .mozconfig settings

I searched google with the following query “mac osx build firefox” and this page [2] was returned as one of the search results. I then followed the instructions on the build documentation page and at the end was able to get Firefox built and running in no time.

: Hardware and Software Requirements

Hardware

I didn’t care much about the hardware requirements listed on the Build Documentation page because I knew that my laptop was a fairly recent model [2 years old] and that the only reason why they usually require high system requirements is mostly to decrease the time that it takes to compile code.

My hardware configuration

Software

Xcode: I already had Xcode installed since I had used it before in my BTP course. Xcode provided me with the SDKs that were required for building Firefox as well as the build tools like the gcc 3.3 compiler. [Get Xcode]

DarwinPorts: The Mozilla prerequisites page for building Firefox on OS X recommends the installation of DarwinPorts, a package management system, needed in order to install libIDL, Glib and autoconf. I also had DarwinPorts installed on my computer.

[Installation Document] [Get DarwinPorts]

My software configuration

: Problems encountered & Resolutions

When compiling Firefox I realized that I was missing the QuickTime, CoreAudio and the OpenGL SDK. To resolve this issue I re-downloaded Xcode and installed the missing files.

Missing QuickTime SDK

Missing CoreAudio SDK

Missing OpenGL SDK

After I added the missing SDKs I was still unable to compile and I would get the same build error message over and over again.

After many build attempts I realized that the old object files that were compiled before, when I was missing the SDKs, were causing the build process to fail. The removal of the object files with in the objdir resulted it a successful build of Firefox.

: Resources Used

: Screenshot

: Final Thoughts

My first experience with building Firefox has been quite uneventful. I was expecting hours of painful struggle with this assignment but luckily the entire process went without a hitch at least compared to the other horror stories I’ve heard. I however consider myself lucky to have all the tools needed already installed and ready to go.

Release Notes

The Release Notes for the CUDA Toolkit.

EULA

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.

Installation Guides

Quick Start Guide

This guide provides the minimal first-steps instructions for installation and verifying CUDA on a standard system.

Installation Guide Windows

This guide discusses how to install and check for correct operation of the CUDA Development Tools on Microsoft Windows systems.

Installation Guide Mac OS X

This guide discusses how to install and check for correct operation of the CUDA Development Tools on Mac OS X systems.

Installation Guide Linux

This guide discusses how to install and check for correct operation of the CUDA Development Tools on GNU/Linux systems.

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Programming Guides

Programming Guide

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.

Best Practices Guide

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.

Maxwell Compatibility Guide

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.

Pascal Compatibility Guide

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.

Volta Compatibility Guide

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.

Turing Compatibility Guide

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.

NVIDIA Ampere GPU Architecture Compatibility Guide

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.

Kepler Tuning Guide

Kepler is NVIDIA's 3rd-generation architecture for CUDA compute applications. Applications that follow the best practices for the Fermi architecture should typically see speedups on the Kepler architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging Kepler architectural features.

Maxwell Tuning Guide

Maxwell is NVIDIA's 4th-generation architecture for CUDA compute applications. Applications that follow the best practices for the Kepler architecture should typically see speedups on the Maxwell architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging Maxwell architectural features.

Pascal Tuning Guide

Pascal is NVIDIA's 5th-generation architecture for CUDA compute applications. Applications that follow the best practices for the Maxwell architecture should typically see speedups on the Pascal architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging Pascal architectural features.

Volta Tuning Guide

Volta is NVIDIA's 6th-generation architecture for CUDA compute applications. Applications that follow the best practices for the Pascal architecture should typically see speedups on the Volta architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging Volta architectural features.

Turing Tuning Guide

Turing is NVIDIA's 7th-generation architecture for CUDA compute applications. Applications that follow the best practices for the Pascal architecture should typically see speedups on the Turing architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging Turing architectural features.

NVIDIA Ampere GPU Architecture Tuning Guide

NVIDIA Ampere GPU Architecture is NVIDIA's 8th-generation architecture for CUDA compute applications. Applications that follow the best practices for the NVIDIA Volta architecture should typically see speedups on the NVIDIA Ampere GPU Architecture without any code changes. This guide summarizes the ways that applications can be fine-tuned to gain additional speedups by leveraging NVIDIA Ampere GPU Architecture's features.

PTX ISA

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.

Developer Guide for Optimus

This document explains how CUDA APIs can be used to query for GPU capabilities in NVIDIA Optimus systems.

Video Decoder

NVIDIA Video Decoder (NVCUVID) is deprecated. Instead, use the NVIDIA Video Codec SDK (https://developer.nvidia.com/nvidia-video-codec-sdk).

PTX Interoperability

This document shows how to write PTX that is ABI-compliant and interoperable with other CUDA code.

Inline PTX Assembly

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.

CUDA Occupancy Calculator

The CUDA Occupancy Calculator allows you to compute the multiprocessor occupancy of a GPU by a given CUDA kernel.

CUDA API References

CUDA Runtime API

The CUDA runtime API.

CUDA Driver API

The CUDA driver API.

CUDA Math API

The CUDA math API.

cuBLAS

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.

NVBLAS

The NVBLAS library is a multi-GPUs accelerated drop-in BLAS (Basic Linear Algebra Subprograms) built on top of the NVIDIA cuBLAS Library.

nvJPEG

The nvJPEG Library provides high-performance GPU accelerated JPEG decoding functionality for image formats commonly used in deep learning and hyperscale multimedia applications.

cuFFT

The cuFFT library user guide.

cuRAND

The cuRAND library user guide.

cuSPARSE

The cuSPARSE library user guide.

cuSPARSELt

The cuSPARSELt library user guide.

NPP

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 (Runtime Compilation)

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.

Thrust

The Thrust getting started guide.

cuSOLVER

The cuSOLVER library user guide.

Miscellaneous

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CUDA Samples

This document contains a complete listing of the code samples that are included with the NVIDIA CUDA Toolkit. It describes each code sample, lists the minimum GPU specification, and provides links to the source code and white papers if available.

CUDA Demo Suite

This document describes the demo applications shipped with the CUDA Demo Suite.

CUDA on WSL

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.

Multi-Instance GPU (MIG)

This edition of the user guide describes the Multi-Instance GPU feature of the NVIDIA® A100 GPU.

CUPTI

The CUPTI-API. The CUDA Profiling Tools Interface (CUPTI) enables the creation of profiling and tracing tools that target CUDA applications.

Debugger API

The CUDA debugger API.

GPUDirect RDMA

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.

vGPU

vGPUs that support CUDA.

Tools

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NVCC

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.

CUDA-GDB

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.

CUDA-MEMCHECK

CUDA-MEMCHECK is a suite of run time tools capable of precisely detecting out of bounds and misaligned memory access errors, checking device allocation leaks, reporting hardware errors and identifying shared memory data access hazards.

Compute Sanitizer

The user guide for Compute Sanitizer.

Nsight Eclipse Plugins Installation Guide

Nsight Eclipse Plugins Installation Guide

Nsight Eclipse Plugins Edition

Nsight Eclipse Plugins Edition getting started guide

Nsight Compute

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.

Profiler

This is the guide to the Profiler.

CUDA Binary Utilities

The application notes for cuobjdump, nvdisasm, and nvprune.

White Papers

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Floating Point and IEEE 754

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.

Incomplete-LU and Cholesky Preconditioned Iterative Methods

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.

Application Notes

CUDA for Tegra

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.

Compiler SDK

libNVVM API

The libNVVM API.

libdevice User's Guide

The libdevice library is an LLVM bitcode library that implements common functions for GPU kernels.

NVVM IR

NVVM IR is a compiler IR (internal 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.