Brand Guidelines for the NVIDIA Partner Network Brand Compliance Requirements and Usage Examples
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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. -
20201130 Gcdv V1.0.Pdf
DE LA RECHERCHE À L’INDUSTRIE Architecture evolutions for HPC 30 Novembre 2020 Guillaume Colin de Verdière Commissariat à l’énergie atomique et aux énergies alternatives - www.cea.fr Commissariat à l’énergie atomique et aux énergies alternatives EUROfusion G. Colin de Verdière 30/11/2020 EVOLUTION DRIVER: TECHNOLOGICAL CONSTRAINTS . Power wall . Scaling wall . Memory wall . Towards accelerated architectures . SC20 main points Commissariat à l’énergie atomique et aux énergies alternatives EUROfusion G. Colin de Verdière 30/11/2020 2 POWER WALL P: power 푷 = 풄푽ퟐ푭 V: voltage F: frequency . Reduce V o Reuse of embedded technologies . Limit frequencies . More cores . More compute, less logic o SIMD larger o GPU like structure © Karl Rupp https://software.intel.com/en-us/blogs/2009/08/25/why-p-scales-as-cv2f-is-so-obvious-pt-2-2 Commissariat à l’énergie atomique et aux énergies alternatives EUROfusion G. Colin de Verdière 30/11/2020 3 SCALING WALL FinFET . Moore’s law comes to an end o Probable limit around 3 - 5 nm o Need to design new structure for transistors Carbon nanotube . Limit of circuit size o Yield decrease with the increase of surface • Chiplets will dominate © AMD o Data movement will be the most expensive operation • 1 DFMA = 20 pJ, SRAM access= 50 pJ, DRAM access= 1 nJ (source NVIDIA) 1 nJ = 1000 pJ, Φ Si =110 pm Commissariat à l’énergie atomique et aux énergies alternatives EUROfusion G. Colin de Verdière 30/11/2020 4 MEMORY WALL . Better bandwidth with HBM o DDR5 @ 5200 MT/s 8ch = 0.33 TB/s Thread 1 Thread Thread 2 Thread Thread 3 Thread o HBM2 @ 4 stacks = 1.64 TB/s 4 Thread Skylake: SMT2 . -
NVIDIA Quadro RTX for V-Ray Next
NVIDIA QUADRO RTX V-RAY NEXT GPU Image courtesy of © Dabarti Studio, rendered with V-Ray GPU Quadro RTX Accelerates V-Ray Next GPU Rendering Solutions for V-Ray Next GPU V-Ray Next GPU taps into the power of NVIDIA® Quadro® NVIDIA Quadro® provides a wide range of RTX-enabled RTX™ to speed up production rendering with dedicated RT solutions for desktop, mobile, server-based rendering, and Cores for ray tracing and Tensor Cores for AI-accelerated virtual workstations with NVIDIA Quadro Virtual Data denoising.¹ With up to 18X faster rendering than CPU-based Center Workstation (Quadro vDWS) software.2 With up to 96 solutions and enhanced performance with NVIDIA NVLink™, gigabytes (GB) of GPU memory available,3 Quadro RTX V-Ray Next GPU with RTX support provides incredible provides the power you need for the largest professional performance improvements for your rendering workloads. graphics and rendering workloads. “ Accelerating artist productivity is always our top Benchmark: V-Ray Next GPU Rendering Performance Increase on Quadro RTX GPUs priority, so we’re quick to take advantage of the latest ray-tracing hardware breakthroughs. By Quadro RTX 6000 x2 1885 ™ Quadro RTX 6000 104 supporting NVIDIA RTX in V-Ray GPU, we’re Quadro RTX 4000 783 bringing our customers an exciting new boost in PU 1 0 2 4 6 8 10 12 14 16 18 20 their GPU production rendering speeds.” Relatve Performance – Phillip Miller, Vice President, Product Management, Chaos Group Desktop performance Tests run on 1x Xeon old 6154 3 Hz (37 Hz Turbo), 64 B DDR4 RAM Wn10x64 Drver verson 44128 Performance results may vary dependng on the scene NVIDIA Quadro professional graphics solutions are verified and recommended for the most demanding projects by Chaos Group. -
GPU-Based Deep Learning Inference
Whitepaper GPU-Based Deep Learning Inference: A Performance and Power Analysis November 2015 1 Contents Abstract ......................................................................................................................................................... 3 Introduction .................................................................................................................................................. 3 Inference versus Training .............................................................................................................................. 4 GPUs Excel at Neural Network Inference ..................................................................................................... 5 Inference Optimizations in Caffe and cuDNN 4 ........................................................................................ 5 Experimental Setup and Testing Methodology ........................................................................................ 7 Inference on Small and Large GPUs .......................................................................................................... 8 Conclusion ................................................................................................................................................... 10 References .................................................................................................................................................. 10 2 Abstract Deep learning methods are revolutionizing various areas of machine perception. On a -
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 -
NVIDIA DGX Station the First Personal AI Supercomputer 1.0 Introduction
White Paper NVIDIA DGX Station The First Personal AI Supercomputer 1.0 Introduction.........................................................................................................2 2.0 NVIDIA DGX Station Architecture ........................................................................3 2.1 NVIDIA Tesla V100 ..........................................................................................5 2.2 Second-Generation NVIDIA NVLink™ .............................................................7 2.3 Water-Cooling System for the GPUs ...............................................................7 2.4 GPU and System Memory...............................................................................8 2.5 Other Workstation Components.....................................................................9 3.0 Multi-GPU with NVLink......................................................................................10 3.1 DGX NVLink Network Topology for Efficient Application Scaling..................10 3.2 Scaling Deep Learning Training on NVLink...................................................12 4.0 DGX Station Software Stack for Deep Learning.................................................14 4.1 NVIDIA CUDA Toolkit.....................................................................................16 4.2 NVIDIA Deep Learning SDK ...........................................................................16 4.3 Docker Engine Utility for NVIDIA GPUs.........................................................17 4.4 NVIDIA Container -
MSI Afterburner V4.6.4
MSI Afterburner v4.6.4 MSI Afterburner is ultimate graphics card utility, co-developed by MSI and RivaTuner teams. Please visit https://msi.com/page/afterburner to get more information about the product and download new versions SYSTEM REQUIREMENTS: ...................................................................................................................................... 3 FEATURES: ............................................................................................................................................................. 3 KNOWN LIMITATIONS:........................................................................................................................................... 4 REVISION HISTORY: ................................................................................................................................................ 5 VERSION 4.6.4 .............................................................................................................................................................. 5 VERSION 4.6.3 (PUBLISHED ON 03.03.2021) .................................................................................................................... 5 VERSION 4.6.2 (PUBLISHED ON 29.10.2019) .................................................................................................................... 6 VERSION 4.6.1 (PUBLISHED ON 21.04.2019) .................................................................................................................... 7 VERSION 4.6.0 (PUBLISHED ON -
Salo Jouni-Junior.Pdf
Näytönohjainarkkitehtuurit Jouni-Junior Salo OPINNÄYTETYÖ Helmikuu 2019 Tieto- ja viestintätekniikan koulutus Sulautetut järjestelmät TIIVISTELMÄ Tampereen ammattikorkeakoulu Tieto- ja viestintätekniikan koulutus Sulautetut järjestelmät SALO JOUNI-JUNIOR Näytönohjainarkkitehtuurit Opinnäytetyö 39 sivua Maaliskuu 2019 Tässä opinnäytetyössä on perehdytty Yhdysvaltalaisen grafiikkasuorittimien val- mistajan Nvidian historiaan ja tuotteisiin. Nvidia on toinen maailman suurim- masta grafiikkasuorittimien valmistajasta. Tässä työssä tutustutaan tarkemmin Nvidian arkkitehtuureihin, Fermiin, Kepleriin, Maxwelliin, Pascaliin, Voltaan ja Turingiin. Opinnäytetyössä tutkittiin, mistä asioista Nvidian arkkitehtuurit koostuvat ja mi- ten eri komponentit kommunikoivat keskenään. Työssä käytiin läpi jokaisen ark- kitehtuurin julkaisuvuosi ja niiden käyttökohteet. Työssä huomattiin kuinka pal- jon Nvidian teknologia on kehittynyt vuosien varrella ja kuinka Nvidian koneop- pimiseen tarkoitettuja työkaluja on käytetty. Nvidia Fermi Kepler Maxwell Pascal Volta Turing rtx näytönohjain gpu ABSTRACT Tampere University of Applied Sciences Information and communication technologies Embedded systems SALO JOUNI-JUNIOR GPU architectures Bachelor's thesis 39 pages March 2019 This thesis focuses on the history and products of an American technology company Nvidia Corporation. Nvidia Corporation is one of the two largest graphics processing unit designers and producers. This thesis examines all of the following Nvidia architectures, Fermi, Kepler, Maxwell, Pascal, -
The NVIDIA DGX-1 Deep Learning System
NVIDIA DGX-1 ARTIFIcIAL INTELLIGENcE SYSTEM The World’s First AI Supercomputer in a Box Get faster training, larger models, and more accurate results from deep learning with the NVIDIA® DGX-1™. This is the world’s first purpose-built system for deep learning and AI-accelerated analytics, with performance equal to 250 conventional servers. It comes fully integrated with hardware, deep learning software, development tools, and accelerated analytics applications. Immediately shorten SYSTEM SPECIFICATIONS data processing time, visualize more data, accelerate deep learning frameworks, GPUs 8x Tesla P100 and design more sophisticated neural networks. TFLOPS (GPU FP16 / 170/3 CPU FP32) Iterate and Innovate Faster GPU Memory 16 GB per GPU CPU Dual 20-core Intel® Xeon® High-performance training accelerates your productivity giving you faster insight E5-2698 v4 2.2 GHz NVIDIA CUDA® Cores 28672 and time to market. System Memory 512 GB 2133 MHz DDR4 Storage 4x 1.92 TB SSD RAID 0 NVIDIA DGX-1 Delivers 58X Faster Training Network Dual 10 GbE, 4 IB EDR Software Ubuntu Server Linux OS DGX-1 Recommended GPU NVIDIA DX-1 23 Hours, less than 1 day Driver PU-Only Server 1310 Hours (5458 Days) System Weight 134 lbs 0 10X 20X 30X 40X 50X 60X System Dimensions 866 D x 444 W x 131 H (mm) Relatve Performance (Based on Tme to Tran) Packing Dimensions 1180 D x 730 W x 284 H (mm) affe benchmark wth V-D network, tranng 128M mages wth 70 epochs | PU servers uses 2x Xeon E5-2699v4 PUs Maximum Power 3200W Requirements Operating Temperature 10 - 30°C NVIDIA DGX-1 Delivers 34X More Performance Range NVIDIA DX-1 170 TFLOPS PU-Only Server 5 TFLOPS 0 10 50 100 150 170 Performance n teraFLOPS PU s dual socket Intel Xeon E5-2699v4 170TF s half precson or FP16 DGX-1 | DATA SHEET | DEc16 computing for Infinite Opportunities NVIDIA DGX-1 Software Stack The NVIDIA DGX-1 is the first system built with DEEP LEARNING FRAMEWORKS NVIDIA Pascal™-powered Tesla® P100 accelerators. -
Numerical Behavior of NVIDIA Tensor Cores
Numerical behavior of NVIDIA tensor cores Massimiliano Fasi1, Nicholas J. Higham2, Mantas Mikaitis2 and Srikara Pranesh2 1 School of Science and Technology, Örebro University, Örebro, Sweden 2 Department of Mathematics, University of Manchester, Manchester, UK ABSTRACT We explore the floating-point arithmetic implemented in the NVIDIA tensor cores, which are hardware accelerators for mixed-precision matrix multiplication available on the Volta, Turing, and Ampere microarchitectures. Using Volta V100, Turing T4, and Ampere A100 graphics cards, we determine what precision is used for the intermediate results, whether subnormal numbers are supported, what rounding mode is used, in which order the operations underlying the matrix multiplication are performed, and whether partial sums are normalized. These aspects are not documented by NVIDIA, and we gain insight by running carefully designed numerical experiments on these hardware units. Knowing the answers to these questions is important if one wishes to: (1) accurately simulate NVIDIA tensor cores on conventional hardware; (2) understand the differences between results produced by code that utilizes tensor cores and code that uses only IEEE 754-compliant arithmetic operations; and (3) build custom hardware whose behavior matches that of NVIDIA tensor cores. As part of this work we provide a test suite that can be easily adapted to test newer versions of the NVIDIA tensorcoresaswellassimilaracceleratorsfromothervendors,astheybecome available. Moreover, we identify a non-monotonicity issue -
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. -
Manycore GPU Architectures and Programming, Part 1
Lecture 19: Manycore GPU Architectures and Programming, Part 1 Concurrent and Mul=core Programming CSE 436/536, [email protected] www.secs.oakland.edu/~yan 1 Topics (Part 2) • Parallel architectures and hardware – Parallel computer architectures – Memory hierarchy and cache coherency • Manycore GPU architectures and programming – GPUs architectures – CUDA programming – Introduc?on to offloading model in OpenMP and OpenACC • Programming on large scale systems (Chapter 6) – MPI (point to point and collec=ves) – Introduc?on to PGAS languages, UPC and Chapel • Parallel algorithms (Chapter 8,9 &10) – Dense matrix, and sorng 2 Manycore GPU Architectures and Programming: Outline • Introduc?on – GPU architectures, GPGPUs, and CUDA • GPU Execuon model • CUDA Programming model • Working with Memory in CUDA – Global memory, shared and constant memory • Streams and concurrency • CUDA instruc?on intrinsic and library • Performance, profiling, debugging, and error handling • Direc?ve-based high-level programming model – OpenACC and OpenMP 3 Computer Graphics GPU: Graphics Processing Unit 4 Graphics Processing Unit (GPU) Image: h[p://www.ntu.edu.sg/home/ehchua/programming/opengl/CG_BasicsTheory.html 5 Graphics Processing Unit (GPU) • Enriching user visual experience • Delivering energy-efficient compung • Unlocking poten?als of complex apps • Enabling Deeper scien?fic discovery 6 What is GPU Today? • It is a processor op?mized for 2D/3D graphics, video, visual compu?ng, and display. • It is highly parallel, highly multhreaded mulprocessor op?mized for visual