Opengl ES Safety Critical Profile Specification
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Opengl Distilled / Paul Martz
Page left blank intently OpenGL® Distilled By Paul Martz ............................................... Publisher: Addison Wesley Professional Pub Date: February 27, 2006 Print ISBN-10: 0-321-33679-8 Print ISBN-13: 978-0-321-33679-8 Pages: 304 Table of Contents | Inde OpenGL opens the door to the world of high-quality, high-performance 3D computer graphics. The preferred application programming interface for developing 3D applications, OpenGL is widely used in video game development, visuali,ation and simulation, CAD, virtual reality, modeling, and computer-generated animation. OpenGL® Distilled provides the fundamental information you need to start programming 3D graphics, from setting up an OpenGL development environment to creating realistic te tures and shadows. .ritten in an engaging, easy-to-follow style, this boo/ ma/es it easy to find the information you0re loo/ing for. 1ou0ll quic/ly learn the essential and most-often-used features of OpenGL 2.0, along with the best coding practices and troubleshooting tips. Topics include Drawing and rendering geometric data such as points, lines, and polygons Controlling color and lighting to create elegant graphics Creating and orienting views Increasing image realism with te ture mapping and shadows Improving rendering performance Preserving graphics integrity across platforms A companion .eb site includes complete source code e amples, color versions of special effects described in the boo/, and additional resources. Page left blank intently Table of contents: Chapter 6. Texture Mapping Copyright ............................................................... 4 Section 6.1. Using Texture Maps ........................... 138 Foreword ............................................................... 6 Section 6.2. Lighting and Shadows with Texture .. 155 Preface ................................................................... 7 Section 6.3. Debugging .......................................... 169 About the Book .................................................... -
Nvidia Equity Report
APRIL 2020 NVIDIA EQUITY REPORT By Sanjay Gospodinov Col'23 EQUITY REPORT // APRIL 2020 0 2 NVIDIA REVENUE STREAM NVidia Corp is a technology company based in Santa Clara, Ca with operations worldwide. It is primarily a graphics processing chip manufacturer that makes most of its revenue from the sales of graphics processing units (GPUs), which are used for gaming, professional visualization, and cryptocurrency mining. The Nvidia graphics card, Geforce, is a particularly popular gaming graphics card and as of right now, Nvidia controls 18% of the overall graphics card market and 73% of the discrete GPU market (where the GPU and CPU are separate). Nvidia also produces Quadro, a GPU aimed at professional graphics content designers. They additionally make Tesla (no relation to the car maker), which is a GPU accelerator that runs simulations, deep learning algorithms, and is primarily marketed towards AI data scientists and big data research. Lastly, this segment of NVidia also makes GRID, a product designed for cloud-based streaming. This reportable segment, which almost entirely sells GPUs, makes up 86.7% of Nvidia's revenue. TEGRA SEGMENT AND OPPORTUNITY FOR GROWTH The other reportable segment of Nvidia is Tegra, which combines a GPU and CPU onto one chip. This product is made to support online gaming, entertainment devices, drones, and self-driving cars. This segment of Nvidia's business is relatively new and only produced about 13% of the company's revenue in 2019. However, the opportunity for growth is tremendous here as, in early 2015, Nvidia partnered with Uber to expand in the self-driving car sector. -
Programmable Shading in Opengl
Computer Graphics - Programmable Shading in OpenGL - Arsène Pérard-Gayot History • Pre-GPU graphics acceleration – SGI, Evans & Sutherland – Introduced concepts like vertex transformation and texture mapping • First-generation GPUs (-1998) – NVIDIA TNT2, ATI Rage, Voodoo3 – Vertex transformation on CPU, limited set of math operations • Second-generation GPUs (1999-2000) – GeForce 256, GeForce2, Radeon 7500, Savage3D – Transformation & lighting, more configurable, still not programmable • Third-generation GPUs (2001) – GeForce3, GeForce4 Ti, Xbox, Radeon 8500 – Vertex programmability, pixel-level configurability • Fourth-generation GPUs (2002) – GeForce FX series, Radeon 9700 and on – Vertex-level and pixel-level programmability (limited) • Eighth-generation GPUs (2007) – Geometry shaders, feedback, unified shaders, … • Ninth-generation GPUs (2009/10) – OpenCL/DirectCompute, hull & tesselation shaders Graphics Hardware Gener Year Product Process Transistors Antialiasing Polygon ation fill rate rate 1st 1998 RIVA TNT 0.25μ 7 M 50 M 6 M 1st 1999 RIVA TNT2 0.22μ 9 M 75 M 9 M 2nd 1999 GeForce 256 0.22μ 23 M 120 M 15 M 2nd 2000 GeForce2 0.18μ 25 M 200 M 25 M 3rd 2001 GeForce3 0.15μ 57 M 800 M 30 M 3rd 2002 GeForce4 Ti 0.15μ 63 M 1,200 M 60 M 4th 2003 GeForce FX 0.13μ 125 M 2,000 M 200 M 8th 2007 GeForce 8800 0.09μ 681 M 36,800 M 13,800 M (GT100) 8th 2008 GeForce 280 0.065μ 1,400 M 48,200 M ?? (GT200) 9th 2009 GeForce 480 0.04μ 3,000 M 42,000 M ?? (GF100) Shading Languages • Small program fragments (plug-ins) – Compute certain aspects of the -
Deconstructing Hardware Usage for General Purpose Computation on Gpus
Deconstructing Hardware Usage for General Purpose Computation on GPUs Budyanto Himawan Manish Vachharajani Dept. of Computer Science Dept. of Electrical and Computer Engineering University of Colorado University of Colorado Boulder, CO 80309 Boulder, CO 80309 E-mail: {Budyanto.Himawan,manishv}@colorado.edu Abstract performance, in 2001, NVidia revolutionized the GPU by making it highly programmable [3]. Since then, the programmability of The high-programmability and numerous compute resources GPUs has steadily increased, although they are still not fully gen- on Graphics Processing Units (GPUs) have allowed researchers eral purpose. Since this time, there has been much research and ef- to dramatically accelerate many non-graphics applications. This fort in porting both graphics and non-graphics applications to use initial success has generated great interest in mapping applica- the parallelism inherent in GPUs. Much of this work has focused tions to GPUs. Accordingly, several works have focused on help- on presenting application developers with information on how to ing application developers rewrite their application kernels for the perform the non-trivial mapping of general purpose concepts to explicitly parallel but restricted GPU programming model. How- GPU hardware so that there is a good fit between the algorithm ever, there has been far less work that examines how these appli- and the GPU pipeline. cations actually utilize the underlying hardware. Less attention has been given to deconstructing how these gen- This paper focuses on deconstructing how General Purpose ap- eral purpose application use the graphics hardware itself. Nor has plications on GPUs (GPGPU applications) utilize the underlying much attention been given to examining how GPUs (or GPU-like GPU pipeline. -
AMD Radeon E8860
Components for AMD’s Embedded Radeon™ E8860 GPU INTRODUCTION The E8860 Embedded Radeon GPU available from CoreAVI is comprised of temperature screened GPUs, safety certi- fiable OpenGL®-based drivers, and safety certifiable GPU tools which have been pre-integrated and validated together to significantly de-risk the challenges typically faced when integrating hardware and software components. The plat- form is an off-the-shelf foundation upon which safety certifiable applications can be built with confidence. Figure 1: CoreAVI Support for E8860 GPU EXTENDED TEMPERATURE RANGE CoreAVI provides extended temperature versions of the E8860 GPU to facilitate its use in rugged embedded applications. CoreAVI functionally tests the E8860 over -40C Tj to +105 Tj, increasing the manufacturing yield for hardware suppliers while reducing supply delays to end customers. coreavi.com [email protected] Revision - 13Nov2020 1 E8860 GPU LONG TERM SUPPLY AND SUPPORT CoreAVI has provided consistent and dedicated support for the supply and use of the AMD embedded GPUs within the rugged Mil/Aero/Avionics market segment for over a decade. With the E8860, CoreAVI will continue that focused support to ensure that the software, hardware and long-life support are provided to meet the needs of customers’ system life cy- cles. CoreAVI has extensive environmentally controlled storage facilities which are used to store the GPUs supplied to the Mil/ Aero/Avionics marketplace, ensuring that a ready supply is available for the duration of any program. CoreAVI also provides the post Last Time Buy storage of GPUs and is often able to provide additional quantities of com- ponents when COTS hardware partners receive increased volume for existing products / systems requiring additional inventory. -
The Opengl Framebuffer Object Extension
TheThe OpenGLOpenGL FramebufferFramebuffer ObjectObject ExtensionExtension SimonSimon GreenGreen NVIDIANVIDIA CorporationCorporation OverviewOverview •• WhyWhy renderrender toto texture?texture? •• PP--bufferbuffer // ARBARB renderrender texturetexture reviewreview •• FramebufferFramebuffer objectobject extensionextension •• ExamplesExamples •• FutureFuture directionsdirections WhyWhy RenderRender ToTo Texture?Texture? • Allows results of rendering to framebuffer to be directly read as texture • Better performance – avoids copy from framebuffer to texture (glCopyTexSubImage2D) – uses less memory – only one copy of image – but driver may sometimes have to do copy internally • some hardware has separate texture and FB memory • different internal representations • Applications – dynamic textures – procedurals, reflections – multi-pass techniques – anti-aliasing, motion blur, depth of field – image processing effects (blurs etc.) – GPGPU – provides feedback loop WGL_ARB_pbufferWGL_ARB_pbuffer •• PixelPixel buffersbuffers •• DesignedDesigned forfor offoff--screenscreen renderingrendering – Similar to windows, but non-visible •• WindowWindow systemsystem specificspecific extensionextension •• SelectSelect fromfrom anan enumeratedenumerated listlist ofof availableavailable pixelpixel formatsformats usingusing – ChoosePixelFormat() – DescribePixelFormat() ProblemsProblems withwith PBuffersPBuffers • Each pbuffer usually has its own OpenGL context – (Assuming they have different pixel formats) – Can share texture objects, display lists between -
Troubleshooting Guide Table of Contents -1- General Information
Troubleshooting Guide This troubleshooting guide will provide you with information about Star Wars®: Episode I Battle for Naboo™. You will find solutions to problems that were encountered while running this program in the Windows 95, 98, 2000 and Millennium Edition (ME) Operating Systems. Table of Contents 1. General Information 2. General Troubleshooting 3. Installation 4. Performance 5. Video Issues 6. Sound Issues 7. CD-ROM Drive Issues 8. Controller Device Issues 9. DirectX Setup 10. How to Contact LucasArts 11. Web Sites -1- General Information DISCLAIMER This troubleshooting guide reflects LucasArts’ best efforts to account for and attempt to solve 6 problems that you may encounter while playing the Battle for Naboo computer video game. LucasArts makes no representation or warranty about the accuracy of the information provided in this troubleshooting guide, what may result or not result from following the suggestions contained in this troubleshooting guide or your success in solving the problems that are causing you to consult this troubleshooting guide. Your decision to follow the suggestions contained in this troubleshooting guide is entirely at your own risk and subject to the specific terms and legal disclaimers stated below and set forth in the Software License and Limited Warranty to which you previously agreed to be bound. This troubleshooting guide also contains reference to third parties and/or third party web sites. The third party web sites are not under the control of LucasArts and LucasArts is not responsible for the contents of any third party web site referenced in this troubleshooting guide or in any other materials provided by LucasArts with the Battle for Naboo computer video game, including without limitation any link contained in a third party web site, or any changes or updates to a third party web site. -
Graphics Pipeline and Rasterization
Graphics Pipeline & Rasterization Image removed due to copyright restrictions. MIT EECS 6.837 – Matusik 1 How Do We Render Interactively? • Use graphics hardware, via OpenGL or DirectX – OpenGL is multi-platform, DirectX is MS only OpenGL rendering Our ray tracer © Khronos Group. All rights reserved. This content is excluded from our Creative Commons license. For more information, see http://ocw.mit.edu/help/faq-fair-use/. 2 How Do We Render Interactively? • Use graphics hardware, via OpenGL or DirectX – OpenGL is multi-platform, DirectX is MS only OpenGL rendering Our ray tracer © Khronos Group. All rights reserved. This content is excluded from our Creative Commons license. For more information, see http://ocw.mit.edu/help/faq-fair-use/. • Most global effects available in ray tracing will be sacrificed for speed, but some can be approximated 3 Ray Casting vs. GPUs for Triangles Ray Casting For each pixel (ray) For each triangle Does ray hit triangle? Keep closest hit Scene primitives Pixel raster 4 Ray Casting vs. GPUs for Triangles Ray Casting GPU For each pixel (ray) For each triangle For each triangle For each pixel Does ray hit triangle? Does triangle cover pixel? Keep closest hit Keep closest hit Scene primitives Pixel raster Scene primitives Pixel raster 5 Ray Casting vs. GPUs for Triangles Ray Casting GPU For each pixel (ray) For each triangle For each triangle For each pixel Does ray hit triangle? Does triangle cover pixel? Keep closest hit Keep closest hit Scene primitives It’s just a different orderPixel raster of the loops! -
Xengt: a Software Based Intel Graphics Virtualization Solution
XenGT: a Software Based Intel Graphics Virtualization Solution Oct 22, 2013 Haitao Shan, [email protected] Kevin Tian, [email protected] Eddie Dong, [email protected] David Cowperthwaite, [email protected] Agenda • Background • Existing Arts • XenGT Architecture • Performance • Summary 2 Background Graphics Computing • Entertainment applications • Gaming, video playback, browser, etc. • General purpose windowing • Windows Aero, Compiz Fusion, etc • High performance computing • Computer aided designs, weather broadcast, etc. Same capability required, when above tasks are moved into VM 4 Graphics Virtualization • Performance vs. multiplexing • Consistent and rich user experience in all VMs • Share a single GPU among multiple VMs Client Rich Virtual Client Server VDI, transcoder, GPGPU Embedded Smartphone, tablet, IVI 5 Existing Arts Device Emulation • Only for legacy VGA cards • E.g. Cirrus logic VGA card • Limited graphics capability • 2D only • Optimizations on frame buffer operations • E.g. PV framebuffer • Impossible to emulate a modern GPU • Complexity • Poor performance 7 Split Driver Model • Frontend/Backend drivers • Forward OpenGL/DirectX API calls • Implementation specific for the level of forwarding • E.g. VMGL, VMware vGPU, Virgil • Hardware agnostic • Challenges on forwarding between host/guest graphics stacks • API compatibility • CPU overhead 8 Direct Pass-Through/SR-IOV • Best performance with direct pass-through • However no multiplexing 9 XenGT Architecture XenGT • A mediated pass-through solution -
Module: Introduction
CDP 2015 Climate Change 2015 Information Request CDP NVIDIA Corporation Module: Introduction Page: Introduction CC0.1 Introduction Please give a general description and introduction to your organization. NVIDIA is the world leader in visual computing. The GPU, which we invented in 1999, serves as the visual cortex of modern computers and is at the heart of our products and services. Our work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions like self-learning machines and self-driving cars. NVIDIA focuses on serving large markets where visual computing is essential. For each, we offer a platform of processors, software, systems and services. We innovate across PC, data center and mobile technologies. And our inventions power the products of OEMs across industries. GeForce GTX, our GPU brand for PC gamers, is the world’s largest gaming platform, with 200 million users. In conjunction with GeForce Experience, an application that configures games to run optimally and tunes a PC’s performance continually, GeForce GPUs transform everyday PCs into powerful gaming machines. SHIELD, NVIDIA’s first living-room entertainment device, will change the way people enjoy entertainment at home. The world’s first 4K Android TV, it delivers video, music, apps and amazing games. With the ability to easily connect to a store full of apps, SHIELD will do for smart TVs what smartphones did for cell phones. The SHIELD tablet and SHIELD portable round out our family of mobile devices. The NVIDIA GRID game-streaming service, dubbed “a Netflix of games,” allows gamers to connect their SHIELD devices to a GeForce supercomputer in the cloud. -
Investor Presentation Q3 Fy2021
INVESTOR PRESENTATION Q3 FY2021 November 23, 2020 Except for the historical information contained herein, certain matters in this presentation including, but not limited to, statements as to: our financial position; our markets; the performance, benefits, abilities and impact of our products and technology; the availability of our products and technology; our partnerships and customers; our use of cash; the acquisition of Arm and its impacts; NVIDIA’s financial outlook for the fourth quarter of fiscal 2021; our growth and growth drivers; our financial policy; future revenue growth; our opportunities in existing and new markets; the TAM for our products; and performance in our financial metrics are forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. These forward-looking statements and any other forward-looking statements that go beyond historical facts that are made in this presentation are subject to risks and uncertainties that may cause actual results to differ materially. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences and demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems and other factors. NVIDIA has based these forward-looking statements largely on its current expectations and projections about future events and trends that it believes may affect its financial condition, results of operations, business strategy, short-term and long-term business operations and objectives, and financial needs. -
PACKET 7 BOOKSTORE 433 Lecture 5 Dr W IBM OVERVIEW
“PROCESSORS” and multi-processors Excerpt from Hennessey Computer Architecture book; edits by JT Wunderlich PhD Plus Dr W’s IBM Research & Development: JT Wunderlich PhD “PROCESSORS” Excerpt from Hennessey Computer Architecture book; edits by JT Wunderlich PhD Historical Perspective and Further 7.14 Reading There is a tremendous amount of history in multiprocessors; in this section we divide our discussion by both time period and architecture. We start with the SIMD SIMD=SinGle approach and the Illiac IV. We then turn to a short discussion of some other early experimental multiprocessors and progress to a discussion of some of the great Instruction, debates in parallel processing. Next we discuss the historical roots of the present multiprocessors and conclude by discussing recent advances. Multiple Data SIMD Computers: Attractive Idea, Many Attempts, No Lasting Successes The cost of a general multiprocessor is, however, very high and further design options were considered which would decrease the cost without seriously degrading the power or efficiency of the system. The options consist of recentralizing one of the three major components. Centralizing the [control unit] gives rise to the basic organization of [an] . array processor such as the Illiac IV. Bouknight, et al.[1972] The SIMD model was one of the earliest models of parallel computing, dating back to the first large-scale multiprocessor, the Illiac IV. The key idea in that multiprocessor, as in more recent SIMD multiprocessors, is to have a single instruc- tion that operates on many data items at once, using many functional units (see Figure 7.14.1). Although successful in pushing several technologies that proved useful in later projects, it failed as a computer.