White Paper: Next Generation Graphics GPU Shader and Compute Libraries
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Introduction to the Vulkan Computer Graphics API
1 Introduction to the Vulkan Computer Graphics API Mike Bailey mjb – July 24, 2020 2 Computer Graphics Introduction to the Vulkan Computer Graphics API Mike Bailey [email protected] SIGGRAPH 2020 Abridged Version This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License http://cs.oregonstate.edu/~mjb/vulkan ABRIDGED.pptx mjb – July 24, 2020 3 Course Goals • Give a sense of how Vulkan is different from OpenGL • Show how to do basic drawing in Vulkan • Leave you with working, documented, understandable sample code http://cs.oregonstate.edu/~mjb/vulkan mjb – July 24, 2020 4 Mike Bailey • Professor of Computer Science, Oregon State University • Has been in computer graphics for over 30 years • Has had over 8,000 students in his university classes • [email protected] Welcome! I’m happy to be here. I hope you are too ! http://cs.oregonstate.edu/~mjb/vulkan mjb – July 24, 2020 5 Sections 13.Swap Chain 1. Introduction 14.Push Constants 2. Sample Code 15.Physical Devices 3. Drawing 16.Logical Devices 4. Shaders and SPIR-V 17.Dynamic State Variables 5. Data Buffers 18.Getting Information Back 6. GLFW 19.Compute Shaders 7. GLM 20.Specialization Constants 8. Instancing 21.Synchronization 9. Graphics Pipeline Data Structure 22.Pipeline Barriers 10.Descriptor Sets 23.Multisampling 11.Textures 24.Multipass 12.Queues and Command Buffers 25.Ray Tracing Section titles that have been greyed-out have not been included in the ABRIDGED noteset, i.e., the one that has been made to fit in SIGGRAPH’s reduced time slot. -
GLSL 4.50 Spec
The OpenGL® Shading Language Language Version: 4.50 Document Revision: 7 09-May-2017 Editor: John Kessenich, Google Version 1.1 Authors: John Kessenich, Dave Baldwin, Randi Rost Copyright (c) 2008-2017 The Khronos Group Inc. All Rights Reserved. This specification is protected by copyright laws and contains material proprietary to the Khronos Group, Inc. It or any components may not be reproduced, republished, distributed, transmitted, displayed, broadcast, or otherwise exploited in any manner without the express prior written permission of Khronos Group. You may use this specification for implementing the functionality therein, without altering or removing any trademark, copyright or other notice from the specification, but the receipt or possession of this specification does not convey any rights to reproduce, disclose, or distribute its contents, or to manufacture, use, or sell anything that it may describe, in whole or in part. Khronos Group grants express permission to any current Promoter, Contributor or Adopter member of Khronos to copy and redistribute UNMODIFIED versions of this specification in any fashion, provided that NO CHARGE is made for the specification and the latest available update of the specification for any version of the API is used whenever possible. Such distributed specification may be reformatted AS LONG AS the contents of the specification are not changed in any way. The specification may be incorporated into a product that is sold as long as such product includes significant independent work developed by the seller. A link to the current version of this specification on the Khronos Group website should be included whenever possible with specification distributions. -
Overview: Graphics Processing Units
Overview: Graphics Processing Units l advent of GPUs l GPU architecture n the NVIDIA Fermi processor l the CUDA programming model n simple example, threads organization, memory model n case study: matrix multiply using shared memory n memories, thread synchronization, scheduling n case study: reductions n performance considerations: bandwidth, scheduling, resource conflicts, instruction mix u host-device data transfer: multiple GPUs, NVLink, Unified Memory, APUs l the OpenCL programming model l directive-based programming models refs: Lin & Snyder Ch 10, CUDA Toolkit Documentation, An Even Easier Introduction to CUDA (tutorial); NCI NF GPU page, Programming Massively Parallel Processors, Kirk & Hwu, Morgan-Kaufman, 2010; Cuda By Example, by Sanders and Kandrot; OpenCL web page, OpenCL in Action, by Matthew Scarpino COMP4300/8300 L18,19: Graphics Processing Units 2021 JJJ • III × 1 Advent of General-purpose Graphics Processing Units l many applications have massive amounts of mostly independent calculations n e.g. ray tracing, image rendering, matrix computations, molecular simulations, HDTV n can be largely expressed in terms of SIMD operations u implementable with minimal control logic & caches, simple instruction sets l design point: maximize number of ALUs & FPUs and memory bandwidth to take advantage of Moore’s’ Law (shown here) n put this on a co-processor (GPU); have a normal CPU to co-ordinate, run the operating system, launch applications, etc l architecture/infrastructure development requires a massive economic base for its development (the gaming industry!) n pre 2006: only specialized graphics operations (integer & float data) n 2006: ‘General Purpose’ (GPGPU): general computations but only through a graphics library (e.g. -
Opencl on the GPU San Jose, CA | September 30, 2009
OpenCL on the GPU San Jose, CA | September 30, 2009 Neil Trevett and Cyril Zeller, NVIDIA Welcome to the OpenCL Tutorial! • Khronos and industry perspective on OpenCL – Neil Trevett Khronos Group President OpenCL Working Group Chair NVIDIA Vice President Mobile Content • NVIDIA and OpenCL – Cyril Zeller NVIDIA Manager of Compute Developer Technology Khronos and the OpenCL Standard Neil Trevett OpenCL Working Group Chair, Khronos President NVIDIA Vice President Mobile Content Copyright Khronos 2009 Who is the Khronos Group? • Consortium creating open API standards ‘by the industry, for the industry’ – Non-profit founded nine years ago – over 100 members - any company welcome • Enabling software to leverage silicon acceleration – Low-level graphics, media and compute acceleration APIs • Strong commercial focus – Enabling members and the wider industry to grow markets • Commitment to royalty-free standards – Industry makes money through enabled products – not from standards themselves Silicon Community Software Community Copyright Khronos 2009 Apple Over 100 companies creating authoring and acceleration standards Board of Promoters Processor Parallelism CPUs GPUs Multiple cores driving Emerging Increasingly general purpose performance increases Intersection data-parallel computing Improving numerical precision Multi-processor Graphics APIs programming – Heterogeneous and Shading e.g. OpenMP Computing Languages Copyright Khronos 2009 OpenCL Commercial Objectives • Grow the market for parallel computing • Create a foundation layer for a parallel -
History and Evolution of the Android OS
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Springer - Publisher Connector CHAPTER 1 History and Evolution of the Android OS I’m going to destroy Android, because it’s a stolen product. I’m willing to go thermonuclear war on this. —Steve Jobs, Apple Inc. Android, Inc. started with a clear mission by its creators. According to Andy Rubin, one of Android’s founders, Android Inc. was to develop “smarter mobile devices that are more aware of its owner’s location and preferences.” Rubin further stated, “If people are smart, that information starts getting aggregated into consumer products.” The year was 2003 and the location was Palo Alto, California. This was the year Android was born. While Android, Inc. started operations secretly, today the entire world knows about Android. It is no secret that Android is an operating system (OS) for modern day smartphones, tablets, and soon-to-be laptops, but what exactly does that mean? What did Android used to look like? How has it gotten where it is today? All of these questions and more will be answered in this brief chapter. Origins Android first appeared on the technology radar in 2005 when Google, the multibillion- dollar technology company, purchased Android, Inc. At the time, not much was known about Android and what Google intended on doing with it. Information was sparse until 2007, when Google announced the world’s first truly open platform for mobile devices. The First Distribution of Android On November 5, 2007, a press release from the Open Handset Alliance set the stage for the future of the Android platform. -
Standards for Vision Processing and Neural Networks
Standards for Vision Processing and Neural Networks Radhakrishna Giduthuri, AMD [email protected] © Copyright Khronos Group 2017 - Page 1 Agenda • Why we need a standard? • Khronos NNEF • Khronos OpenVX dog Network Architecture Pre-trained Network Model (weights, …) © Copyright Khronos Group 2017 - Page 2 Neural Network End-to-End Workflow Neural Network Third Vision/AI Party Applications Training Frameworks Tools Datasets Trained Vision and Neural Network Network Inferencing Runtime Network Model Architecture Desktop and Cloud Hardware Embedded/Mobile Embedded/MobileEmbedded/Mobile Embedded/Mobile/Desktop/CloudVision/InferencingVision/Inferencing Hardware Hardware cuDNN MIOpen MKL-DNN Vision/InferencingVision/Inferencing Hardware Hardware GPU DSP CPU Custom FPGA © Copyright Khronos Group 2017 - Page 3 Problem: Neural Network Fragmentation Neural Network Training and Inferencing Fragmentation NN Authoring Framework 1 Inference Engine 1 NN Authoring Framework 2 Inference Engine 2 NN Authoring Framework 3 Inference Engine 3 Every Tool Needs an Exporter to Every Accelerator Neural Network Inferencing Fragmentation toll on Applications Inference Engine 1 Hardware 1 Vision/AI Inference Engine 2 Hardware 2 Application Inference Engine 3 Hardware 3 Every Application Needs know about Every Accelerator API © Copyright Khronos Group 2017 - Page 4 Khronos APIs Connect Software to Silicon Software Silicon Khronos is an International Industry Consortium of over 100 companies creating royalty-free, open standard APIs to enable software to access -
Comparison of Technologies for General-Purpose Computing on Graphics Processing Units
Master of Science Thesis in Information Coding Department of Electrical Engineering, Linköping University, 2016 Comparison of Technologies for General-Purpose Computing on Graphics Processing Units Torbjörn Sörman Master of Science Thesis in Information Coding Comparison of Technologies for General-Purpose Computing on Graphics Processing Units Torbjörn Sörman LiTH-ISY-EX–16/4923–SE Supervisor: Robert Forchheimer isy, Linköpings universitet Åsa Detterfelt MindRoad AB Examiner: Ingemar Ragnemalm isy, Linköpings universitet Organisatorisk avdelning Department of Electrical Engineering Linköping University SE-581 83 Linköping, Sweden Copyright © 2016 Torbjörn Sörman Abstract The computational capacity of graphics cards for general-purpose computing have progressed fast over the last decade. A major reason is computational heavy computer games, where standard of performance and high quality graphics con- stantly rise. Another reason is better suitable technologies for programming the graphics cards. Combined, the product is high raw performance devices and means to access that performance. This thesis investigates some of the current technologies for general-purpose computing on graphics processing units. Tech- nologies are primarily compared by means of benchmarking performance and secondarily by factors concerning programming and implementation. The choice of technology can have a large impact on performance. The benchmark applica- tion found the difference in execution time of the fastest technology, CUDA, com- pared to the slowest, OpenCL, to be twice a factor of two. The benchmark applica- tion also found out that the older technologies, OpenGL and DirectX, are compet- itive with CUDA and OpenCL in terms of resulting raw performance. iii Acknowledgments I would like to thank Åsa Detterfelt for the opportunity to make this thesis work at MindRoad AB. -
Gscale: Scaling up GPU Virtualization with Dynamic Sharing of Graphics
gScale: Scaling up GPU Virtualization with Dynamic Sharing of Graphics Memory Space Mochi Xue, Shanghai Jiao Tong University and Intel Corporation; Kun Tian, Intel Corporation; Yaozu Dong, Shanghai Jiao Tong University and Intel Corporation; Jiacheng Ma, Jiajun Wang, and Zhengwei Qi, Shanghai Jiao Tong University; Bingsheng He, National University of Singapore; Haibing Guan, Shanghai Jiao Tong University https://www.usenix.org/conference/atc16/technical-sessions/presentation/xue This paper is included in the Proceedings of the 2016 USENIX Annual Technical Conference (USENIX ATC ’16). June 22–24, 2016 • Denver, CO, USA 978-1-931971-30-0 Open access to the Proceedings of the 2016 USENIX Annual Technical Conference (USENIX ATC ’16) is sponsored by USENIX. gScale: Scaling up GPU Virtualization with Dynamic Sharing of Graphics Memory Space Mochi Xue1,2, Kun Tian2, Yaozu Dong1,2, Jiacheng Ma1, Jiajun Wang1, Zhengwei Qi1, Bingsheng He3, Haibing Guan1 {xuemochi, mjc0608, jiajunwang, qizhenwei, hbguan}@sjtu.edu.cn {kevin.tian, eddie.dong}@intel.com [email protected] 1Shanghai Jiao Tong University, 2Intel Corporation, 3National University of Singapore Abstract As one of the key enabling technologies of GPU cloud, GPU virtualization is intended to provide flexible and With increasing GPU-intensive workloads deployed on scalable GPU resources for multiple instances with high cloud, the cloud service providers are seeking for practi- performance. To achieve such a challenging goal, sev- cal and efficient GPU virtualization solutions. However, eral GPU virtualization solutions were introduced, i.e., the cutting-edge GPU virtualization techniques such as GPUvm [28] and gVirt [30]. gVirt, also known as GVT- gVirt still suffer from the restriction of scalability, which g, is a full virtualization solution with mediated pass- constrains the number of guest virtual GPU instances. -
Slang: Language Mechanisms for Extensible Real-Time Shading Systems
Slang: language mechanisms for extensible real-time shading systems YONG HE, Carnegie Mellon University KAYVON FATAHALIAN, Stanford University TIM FOLEY, NVIDIA Designers of real-time rendering engines must balance the conicting goals and GPU eciently, and minimizing CPU overhead using the new of maintaining clear, extensible shading systems and achieving high render- parameter binding model oered by the modern Direct3D 12 and ing performance. In response, engine architects have established eective de- Vulkan graphics APIs. sign patterns for authoring shading systems, and developed engine-specic To help navigate the tension between performance and maintain- code synthesis tools, ranging from preprocessor hacking to domain-specic able/extensible code, engine architects have established eective shading languages, to productively implement these patterns. The problem is design patterns for authoring shading systems, and developed code that proprietary tools add signicant complexity to modern engines, lack ad- vanced language features, and create additional challenges for learning and synthesis tools, ranging from preprocessor hacking, to metapro- adoption. We argue that the advantages of engine-specic code generation gramming, to engine-proprietary domain-specic languages (DSLs) tools can be achieved using the underlying GPU shading language directly, [Tatarchuk and Tchou 2017], for implementing these patterns. For provided the shading language is extended with a small number of best- example, the idea of shader components [He et al. 2017] was recently practice principles from modern, well-established programming languages. presented as a pattern for achieving both high rendering perfor- We identify that adding generics with interface constraints, associated types, mance and maintainable code structure when specializing shader and interface/structure extensions to existing C-like GPU shading languages code to coarse-grained features such as a surface material pattern or enables real-time renderer developers to build shading systems that are a tessellation eect. -
Webgl™ Optimizations for Mobile
WebGL™ Optimizations for Mobile Lorenzo Dal Col Senior Software Engineer, ARM 1 Agenda 1. Introduction to WebGL™ on mobile . Rendering Pipeline . Locate the bottleneck 2. Performance analysis and debugging tools for WebGL . Generic optimization tips 3. PlayCanvas experience . WebGL Inspector 4. Use case: PlayCanvas Swooop . ARM® DS-5 Streamline . ARM Mali™ Graphics Debugger 5. Q & A 2 Bring the Power of OpenGL® ES to Mobile Browsers What is WebGL™? Why WebGL? . A cross-platform, royalty free web . It brings plug-in free 3D to the web, standard implemented right into the browser. Low-level 3D graphics API . Major browser vendors are members of . Based on OpenGL® ES 2.0 the WebGL Working Group: . A shader based API using GLSL . Apple (Safari® browser) . Mozilla (Firefox® browser) (OpenGL Shading Language) . Google (Chrome™ browser) . Opera (Opera™ browser) . Some concessions made to JavaScript™ (memory management) 3 Introduction to WebGL™ . How does it fit in a web browser? . You use JavaScript™ to control it. Your JavaScript is embedded in HTML5 and uses its Canvas element to draw on. What do you need to start creating graphics? . Obtain WebGLrenderingContext object for a given HTMLCanvasElement. It creates a drawing buffer into which the API calls are rendered. For example: var canvas = document.getElementById('canvas1'); var gl = canvas.getContext('webgl'); canvas.width = newWidth; canvas.height = newHeight; gl.viewport(0, 0, canvas.width, canvas.height); 4 WebGL™ Stack What is happening when a WebGL page is loaded . User enters URL . HTTP stack requests the HTML page Browser . Additional requests will be necessary to get Space User JavaScript™ code and other resources WebKit JavaScript Engine . -
Rowpro Graphics Tester Instructions
RowPro Graphics Tester Instructions What is the RowPro Graphics Tester? The RowPro Graphics Tester is a handy utility to quickly check and confirm RowPro 3D graphics and live water will run in your PC. Do I need to test my PC graphics? If any of the following are true you should test your PC graphics before installing or upgrading to RowPro 3: If your PC shipped new with Windows XP. If you are about to upgrade from RowPro version 2. If you have any doubts or concerns about your PC graphics system. How to download and install the RowPro Graphics Tester Click the link above to download the tester file RowProGraphicsTest.exe. In the download dialog box that appears, click Save or Save this program to disk, navigate to the folder where you want to save the download, and click OK to start the download. IMPORTANT NOTE: The RowPro Graphics Tester only tests if your PC has the required graphics components installed, it is not a graphics performance test. Passing the RowPro Graphics Test is not a guarantee that your PC will run RowPro at a frame rate that is fast enough to be useful. It is however an important test to confirm your PC is at least equipped with the necessary graphics components. How to run the RowPro Graphics Tester 1. Run RowProGraphicsTest.exe to run the test. The test normally completes in less than a second. 2. If any of the results show 'No', check the solutions below. 3. Click the x at the top right of the test panel to close the test. -
Opengl Shading Languag 2Nd Edition (Orange Book)
OpenGL® Shading Language, Second Edition By Randi J. Rost ............................................... Publisher: Addison Wesley Professional Pub Date: January 25, 2006 Print ISBN-10: 0-321-33489-2 Print ISBN-13: 978-0-321-33489-3 Pages: 800 Table of Contents | Index "As the 'Red Book' is known to be the gold standard for OpenGL, the 'Orange Book' is considered to be the gold standard for the OpenGL Shading Language. With Randi's extensive knowledge of OpenGL and GLSL, you can be assured you will be learning from a graphics industry veteran. Within the pages of the second edition you can find topics from beginning shader development to advanced topics such as the spherical harmonic lighting model and more." David Tommeraasen, CEO/Programmer, Plasma Software "This will be the definitive guide for OpenGL shaders; no other book goes into this detail. Rost has done an excellent job at setting the stage for shader development, what the purpose is, how to do it, and how it all fits together. The book includes great examples and details, and good additional coverage of 2.0 changes!" Jeffery Galinovsky, Director of Emerging Market Platform Development, Intel Corporation "The coverage in this new edition of the book is pitched just right to help many new shader- writers get started, but with enough deep information for the 'old hands.'" Marc Olano, Assistant Professor, University of Maryland "This is a really great book on GLSLwell written and organized, very accessible, and with good real-world examples and sample code. The topics flow naturally and easily, explanatory code fragments are inserted in very logical places to illustrate concepts, and all in all, this book makes an excellent tutorial as well as a reference." John Carey, Chief Technology Officer, C.O.R.E.