The Growing Importance of Ray Tracing Due to Gpus
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Physx As a Middleware for Dynamic Simulations in the Container Loading Problem
Proceedings of the 2018 Winter Simulation Conference M. Rabe, A.A. Juan, N. Mustafee, A. Skoogh, S. Jain, and B. Johansson, eds. PHYSX AS A MIDDLEWARE FOR DYNAMIC SIMULATIONS IN THE CONTAINER LOADING PROBLEM Juan C. Martínez-Franco David Álvarez-Martínez Department of Mechanical Engineering Department of Industrial Engineering Universidad de Los Andes Universidad de Los Andes Carrera 1 Este No. 19A – 40 Carrera 1 Este No. 19A – 40 Bogotá, 11711, COLOMBIA Bogotá, 11711, COLOMBIA ABSTRACT The Container Loading Problem (CLP) is an optimization challenge where the constraint of dynamic stability plays a significant role. The evaluation of dynamic stability requires the use of dynamic simulations that are carried out either with dedicated simulation software that produces very small errors at the expense of simulation speed, or real-time physics engines that complete simulations in a very short time at the cost of repeatability. One such engine, PhysX, is evaluated to determine the feasibility of its integration with the open source application PackageCargo. A simulation tool based on PhysX is proposed and compared with the dynamic simulation environment of Autodesk Inventor to verify its reliability. The simulation tool presents a dynamically accurate representation of the physical phenomena experienced by cargo during transportation, making it a viable option for the evaluation of dynamic stability in solutions to the CLP. 1 INTRODUCTION The Container Loading Problem (CLP) consists of the geometric arrangement of small rectangular items (cargo) into a larger rectangular space (container), but it is not a simple geometry problem. The arrangements of cargo must maximize volume utilization while complying with certain constraints such as cargo fragility, delivery order, etc. -
GPU Developments 2018
GPU Developments 2018 2018 GPU Developments 2018 © Copyright Jon Peddie Research 2019. All rights reserved. Reproduction in whole or in part is prohibited without written permission from Jon Peddie Research. This report is the property of Jon Peddie Research (JPR) and made available to a restricted number of clients only upon these terms and conditions. Agreement not to copy or disclose. This report and all future reports or other materials provided by JPR pursuant to this subscription (collectively, “Reports”) are protected by: (i) federal copyright, pursuant to the Copyright Act of 1976; and (ii) the nondisclosure provisions set forth immediately following. License, exclusive use, and agreement not to disclose. Reports are the trade secret property exclusively of JPR and are made available to a restricted number of clients, for their exclusive use and only upon the following terms and conditions. JPR grants site-wide license to read and utilize the information in the Reports, exclusively to the initial subscriber to the Reports, its subsidiaries, divisions, and employees (collectively, “Subscriber”). The Reports shall, at all times, be treated by Subscriber as proprietary and confidential documents, for internal use only. Subscriber agrees that it will not reproduce for or share any of the material in the Reports (“Material”) with any entity or individual other than Subscriber (“Shared Third Party”) (collectively, “Share” or “Sharing”), without the advance written permission of JPR. Subscriber shall be liable for any breach of this agreement and shall be subject to cancellation of its subscription to Reports. Without limiting this liability, Subscriber shall be liable for any damages suffered by JPR as a result of any Sharing of any Material, without advance written permission of JPR. -
Realityserver Installation Guide
RealityServer Êc Web Services 3.1 Installation Guide Document version 1.28 December 8, 2010 Installation Guide Copyright Information c 1986, 2011 NVIDIA Corporation. All rights reserved. This document is protected under copyright law. The contents of this document may not be translated, copied or duplicated in any form, in whole or in part, without the express written permission of NVIDIA Corporation. The information contained in this document is subject to change without notice. NVIDIA Corporation and its employees shall not be responsible for incidental or consequential damages resulting from the use of this material or liable for technical or editorial omissions made herein. NVIDIA, the NVIDIA logo, and DiCE, imatter, iray, mental cloud, mental images, mental matter, mental mesh, mental mill, mental queue, mental ray, Metanode, MetaSL, neuray, Phenomenon, RealityDesigner, RealityPlayer, RealityServer, rendering imagination visible, Shape-By-Shading, and SPM, are trademarks and/or registered trademarks of NVIDIA Corporation. Other product names mentioned in this document may be trademarks or registered trademarks of their respective companies and are hereby acknowledged. Installation, doc. 1.28 c 1986, 2011 NVIDIA Corporation. Table of Contents Table of Contents System Requirements 1 Memory 1 Processors 1 Graphics Card 1 Operating System 4 Network 4 RealityServer Web Services Installation 5 Firewalls 5 Network Buffers 5 Software Protection Manager (SPM) 5 RealityServer Web Services Networking Setup 6 Network Setup 6 c 1986,2011NVIDIACorporation. Installation,doc. 1.28 i Table of Contents ii Installation, doc. 1.28 c 1986, 2011 NVIDIA Corporation. System Requirements— Graphics Card System Requirements Memory Memory requirements greatly depend on the size of the 3D scene, and the number of different scenes concurrently loaded. -
Adding RTX Acceleration to Iray with Optix 7
Adding RTX acceleration to Iray with OptiX 7 Lutz Kettner Director Advanced Rendering and Materials July 30th, SIGGRAPH 2019 What is Iray? Production Rendering on CUDA In Production since > 10 Years Bring ray tracing based production / simulation quality rendering to GPUs New paradigm: Push Button rendering (open up new markets) Plugins for 3ds Max Maya Rhino SketchUp … 2 SIMULATION QUALITY 3 iray legacy ARTISTIC FREEDOM 4 How Does it Work? 99% physically based Path Tracing To guarantee simulation quality and Push Button • Limit shortcuts and good enough hacks to minimum • Brute force (spectral) simulation no intermediate filtering scale over multiple GPUs and hosts even in interactive use GTC 2014 19 VCA * 8 Q6000 GPUs 5 How Does it Work? 99% physically based Path Tracing To guarantee simulation quality and Push Button • Limit shortcuts and good enough hacks to minimum • Brute force (spectral) simulation no intermediate filtering scale over multiple GPUs and hosts even in interactive use • Two-way path tracing from camera and (opt.) lights 6 How Does it Work? 99% physically based Path Tracing To guarantee simulation quality and Push Button • Limit shortcuts and good enough hacks to minimum • Brute force (spectral) simulation no intermediate filtering scale over multiple GPUs and hosts even in interactive use • Two-way path tracing from camera and (opt.) lights • Use NVIDIA Material Definition Language (MDL) 7 How Does it Work? 99% physically based Path Tracing To guarantee simulation quality and Push Button • Limit shortcuts and good -
NVIDIA Physx SDK EULA
NVIDIA CORPORATION NVIDIA® PHYSX® SDK END USER LICENSE AGREEMENT Welcome to the new world of reality gaming brought to you by PhysX® acceleration from NVIDIA®. NVIDIA Corporation (“NVIDIA”) is willing to license the PHYSX SDK and the accompanying documentation to you only on the condition that you accept all the terms in this License Agreement (“Agreement”). IMPORTANT: READ THE FOLLOWING TERMS AND CONDITIONS BEFORE USING THE ACCOMPANYING NVIDIA PHYSX SDK. IF YOU DO NOT AGREE TO THE TERMS OF THIS AGREEMENT, NVIDIA IS NOT WILLING TO LICENSE THE PHYSX SDK TO YOU. IF YOU DO NOT AGREE TO THESE TERMS, YOU SHALL DESTROY THIS ENTIRE PRODUCT AND PROVIDE EMAIL VERIFICATION TO [email protected] OF DELETION OF ALL COPIES OF THE ENTIRE PRODUCT. NVIDIA MAY MODIFY THE TERMS OF THIS AGREEMENT FROM TIME TO TIME. ANY USE OF THE PHYSX SDK WILL BE SUBJECT TO SUCH UPDATED TERMS. A CURRENT VERSION OF THIS AGREEMENT IS POSTED ON NVIDIA’S DEVELOPER WEBSITE: www.developer.nvidia.com/object/physx_eula.html 1. Definitions. “Licensed Platforms” means the following: - Any PC or Apple Mac computer with a NVIDIA CUDA-enabled processor executing NVIDIA PhysX; - Any PC or Apple Mac computer running NVIDIA PhysX software executing on the primary central processing unit of the PC only; - Any PC utilizing an AGEIA PhysX processor executing NVIDIA PhysX code; - Microsoft XBOX 360; - Nintendo Wii; and/or - Sony Playstation 3 “Physics Application” means a software application designed for use and fully compatible with the PhysX SDK and/or NVIDIA Graphics processor products, including but not limited to, a video game, visual simulation, movie, or other product. -
RTX Beyond Ray Tracing
RTX Beyond Ray Tracing Exploring the Use of Hardware Ray Tracing Cores for Tet-Mesh Point Location -Now, let’s run a lot of experiments … I Wald (NVIDIA), W Usher, N Morrical, L Lediaev, V Pascucci (University of Utah) Motivation – What this is about - In this paper: We accelerate Unstructured-Data (Tet Mesh) Volume Ray Casting… NVIDIA Confidential Motivation – What this is about - In this paper: We accelerate Unstructured-Data (Tet Mesh) Volume Ray Casting… - But: This is not what this is (primarily) about - Volume rendering is just a “proof of concept”. - Original question: “What else” can you do with RTX? - Remember the early 2000’s (e.g., “register combiners”): Lots of innovation around “using graphics hardware for non- graphics problems”. - Since CUDA: Much of that has been subsumed through CUDA - Today: Now that we have new hardware units (RTX, Tensor Cores), what else could we (ab-)use those for? (“(ab-)use” as in “use for something that it wasn’t intended for”) NVIDIA Confidential Motivation – What this is about - In this paper: We accelerate Unstructured-Data (Tet Mesh) Volume Ray Casting… - But: This is not what this is (primarily) about - Volume rendering is just a “proof of concept”. - Original question: “What else” can you do with RTX? - Remember the early 2000’s (e.g., “register combiners”): Lots of innovation around “using graphics hardware for non- graphics →problems”.Two main goal(s) of this paper: -a)SinceGet CUDA: readers Much ofto that think has beenabout subsumed the “what through else”s CUDA… - Today: Nowb) Showthat -
RTX-Accelerated Hair Brought to Life with NVIDIA Iray (GTC 2020 S22494)
RTX-accelerated Hair brought to Life with NVIDIA Iray (GTC 2020 S22494) Carsten Waechter, March 2020 What is Iray? Production Rendering on CUDA In Production since > 10 Years Bring ray tracing based production / simulation quality rendering to GPUs New paradigm: Push Button rendering (open up new markets) Plugins for 3ds Max Maya Rhino SketchUp … … … 2 What is Iray? NVIDIA testbed and inspiration for new tech NVIDIA Material Definition Language (MDL) evolved from internal material representation into public SDK NVIDIA OptiX 7 co-development, verification and guinea pig NVIDIA RTX / RT Cores scene- and ray-dumps to drive hardware requirements NVIDIA Maxwell…NVIDIA Turing (& future) enhancements profiling/experiments resulting in new features/improvements Design and test/verify NVIDIA’s new Headquarter (in VR) close cooperation with Gensler 3 Simulation Quality 4 iray legacy Artistic Freedom 5 How Does it Work? 99% physically based Path Tracing To guarantee simulation quality and Push Button • Limit shortcuts and good enough hacks to minimum • Brute force (spectral) simulation no intermediate filtering scale over multiple GPUs and hosts even in interactive use • Two-way path tracing from camera and (opt.) lights • Use NVIDIA Material Definition Language (MDL) • NVIDIA AI Denoiser to clean up remaining noise 6 How Does it Work? 99% physically based Path Tracing To guarantee simulation quality and Push Button • Limit shortcuts and good enough hacks to minimum • Brute force (spectral) simulation no intermediate filtering scale over multiple -
Jen-Hsun Huang, Co-Founder & Ceo, Nvidia | Gtc China 2016
THE DEEP LEARNING AI REVOLUTION JEN-HSUN HUANG, CO-FOUNDER & CEO, NVIDIA | GTC CHINA 2016 GPU DEEP LEARNING BIG BANG ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky Ilya Sutskever Geoffrey e. Hinton University of Toronto University of Toronto University of Toronto NIPS (2012) Deep Learning NVIDIA GPU GPU DEEP LEARNING ACHIEVES “SUPERHUMAN” RESULTS ImageNet — Accuracy % 96% Human Microsoft, Google 3.5% error rate DL 74% Hand-coded CV 2010 2011 2012 2013 2014 2015 2012: Deep Learning researchers 2015: DNN achieves superhuman 2015: Deep Speech 2 achieves worldwide discover GPUs image recognition superhuman voice recognition NVIDIA — “THE AI COMPUTING COMPANY” GPU Computing Computer Graphics Artificial Intelligence ANNOUNCING NEW GRAPHICS SDKS Ansel Volumetric OptiX 4.0 Mental Ray MDL 1.0 In-game Photography Physical Light Models Multi-GPU Ray-Tracing Now GPU-Accelerated! Physically Based Materials Funhouse VR 360 Video 1.0 Iray VR Remote Rendering GVDB Open Source Real-Time Panoramic VR Photorealistic VR Ray Tracing Video Compositing Sparse Volumes for Special Effects NVIDIA VR FUNHOUSE NVIDIA SILICON VALLEY HEADQUARTERS GTC — 25X GROWTH IN GPU DL DEVELOPERS s 16,000 400,000 55,000 • Higher Ed 35% • Australia • Software 19% • China • Internet 15% • Europe • Auto 10% • India 120,000 • Government 5% • Japan • Medical 4% • Japan 3,700 • Korea 2,200 • Finance 4% • United States • United States • Manufacturing 4% (Silicon Valley, D.C.) 2014 2016 2014 2016 2014 2016 4X Attendees 3X GPU Developers 25x Deep Learning -
GPU Based Cloud Computing
GPU based cloud computing Dairsie Latimer, Petapath, UK Petapath © NVIDIA Corporation 2010 About Petapath Petapath ! " Founded in 2008 to focus on delivering innovative hardware and software solutions into the high performance computing (HPC) markets ! " Partnered with HP and SGI to deliverer two Petascale prototype systems as part of the PRACE WP8 programme ! " The system is a testbed for new ideas in usability, scalability and efficiency of large computer installations ! " Active in exploiting emerging standards for acceleration technologies and are members of Khronos group and sit on the OpenCL working committee ! " We also provide consulting expertise for companies wishing to explore the advantages offered by heterogeneous systems © NVIDIA Corporation 2010 What is Heterogeneous or GPU Computing? x86 PCIe bus GPU Computing with CPU + GPU Heterogeneous Computing © NVIDIA Corporation 2010 Low Latency or High Throughput? CPU GPU ! " Optimised for low-latency ! " Optimised for data-parallel, access to cached data sets throughput computation ! " Control logic for out-of-order ! " Architecture tolerant of and speculative execution memory latency ! " More transistors dedicated to computation © NVIDIA Corporation 2010 NVIDIA GPU Computing Ecosystem ISV CUDA CUDA TPP / OEM Training Development Company Specialist Hardware GPU Architecture Architect VAR CUDA SDK & Tools Customer Application Customer NVIDIA Hardware Requirements Solutions Hardware Architecture © NVIDIA Corporation 2010 Deployment Science is Desperate for Throughput Gigaflops 1,000,000,000 -
Nvidia Corporation 2016 Annual Report
2017 NVIDIA CORPORATION ANNUAL REVIEW NOTICE OF ANNUAL MEETING PROXY STATEMENT FORM 10-K THE AGE OF THE GPU IS UPON US THE NEXT PLATFORM A decade ago, we set out to transform the GPU into a powerful computing platform—a specialized tool for the da Vincis and Einsteins of our time. GPU computing has since opened a floodgate of innovation. From gaming and VR to AI and self-driving cars, we’re at the center of the most promising trends in our lifetime. The world has taken notice. Jensen Huang CEO and Founder, NVIDIA NVIDIA GEFORCE HAS MOVED FROM GRAPHICS CARD TO GAMING PLATFORM FORBES The PC drives the growth of computer gaming, the largest entertainment industry in the world. The mass popularity of eSports, the evolution of gaming into a social medium, and the advance of new technologies like 4K, HDR, and VR will fuel its further growth. Today, 200 million gamers play on GeForce, the world’s largest gaming platform. Our breakthrough NVIDIA Pascal architecture delighted gamers, and we built on its foundation with new capabilities, like NVIDIA Ansel, the most advanced in-game photography system ever built. To serve the 1 billion new or infrequent gamers whose PCs are not ready for today’s games, we brought the GeForce NOW game streaming service to Macs and PCs. Mass Effect: Andromeda. Courtesy of Electronic Arts. THE BEST ANDROID TV DEVICE JUST GOT BETTER ENGADGET NVIDIA SHIELD TV, controller, and remote. NVIDIA SHIELD is taking NVIDIA gaming and AI into living rooms around the world. The most advanced streamer now boasts 1,000 games, has the largest open catalog of media in 4K, and can serve as the brain of the AI home. -
NVIDIA Ampere GA102 GPU Architecture Whitepaper
NVIDIA AMPERE GA102 GPU ARCHITECTURE Second-Generation RTX Updated with NVIDIA RTX A6000 and NVIDIA A40 Information V2.0 Table of Contents Introduction 5 GA102 Key Features 7 2x FP32 Processing 7 Second-Generation RT Core 7 Third-Generation Tensor Cores 8 GDDR6X and GDDR6 Memory 8 Third-Generation NVLink® 8 PCIe Gen 4 9 Ampere GPU Architecture In-Depth 10 GPC, TPC, and SM High-Level Architecture 10 ROP Optimizations 11 GA10x SM Architecture 11 2x FP32 Throughput 12 Larger and Faster Unified Shared Memory and L1 Data Cache 13 Performance Per Watt 16 Second-Generation Ray Tracing Engine in GA10x GPUs 17 Ampere Architecture RTX Processors in Action 19 GA10x GPU Hardware Acceleration for Ray-Traced Motion Blur 20 Third-Generation Tensor Cores in GA10x GPUs 24 Comparison of Turing vs GA10x GPU Tensor Cores 24 NVIDIA Ampere Architecture Tensor Cores Support New DL Data Types 26 Fine-Grained Structured Sparsity 26 NVIDIA DLSS 8K 28 GDDR6X Memory 30 RTX IO 32 Introducing NVIDIA RTX IO 33 How NVIDIA RTX IO Works 34 Display and Video Engine 38 DisplayPort 1.4a with DSC 1.2a 38 HDMI 2.1 with DSC 1.2a 38 Fifth Generation NVDEC - Hardware-Accelerated Video Decoding 39 AV1 Hardware Decode 40 Seventh Generation NVENC - Hardware-Accelerated Video Encoding 40 NVIDIA Ampere GA102 GPU Architecture ii Conclusion 42 Appendix A - Additional GeForce GA10x GPU Specifications 44 GeForce RTX 3090 44 GeForce RTX 3070 46 Appendix B - New Memory Error Detection and Replay (EDR) Technology 49 Appendix C - RTX A6000 GPU Perf ormance 50 List of Figures Figure 1. -
Nx Witness User Manual Contents
User Manual Still need help? Visit us at http://support.networkoptix.com Nx Witness User Manual Contents Table of Contents Working with Nx Witness 1 Opening................................................................................................................................... and Closing Nx Witness Client 1 Connecting................................................................................................................................... to Nx Witness via Web-Client 3 Connecting................................................................................................................................... to Enterprise Controller and Working Offline 4 Launching................................................................................................................................... Nx Witness in Compatibility Mode 7 Introducing................................................................................................................................... User Roles 8 Nx Witness................................................................................................................................... User Interface Overview 9 Main Menu .......................................................................................................................................................... 10 Show ing and ..........................................................................................................................................................Hiding Side Panels 11 Tabs and Layouts.........................................................................................................................................................