AI and Deep Learning on Gpus Everywhere: Impressions from NVIDIA's GPU Technology Conference 2017
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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 . -
Programming Graphics Hardware Overview of the Tutorial: Afternoon
Tutorial 5 ProgrammingProgramming GraphicsGraphics HardwareHardware Randy Fernando, Mark Harris, Matthias Wloka, Cyril Zeller Overview of the Tutorial: Morning 8:30 Introduction to the Hardware Graphics Pipeline Cyril Zeller 9:30 Controlling the GPU from the CPU: the 3D API Cyril Zeller 10:15 Break 10:45 Programming the GPU: High-level Shading Languages Randy Fernando 12:00 Lunch Tutorial 5: Programming Graphics Hardware Overview of the Tutorial: Afternoon 12:00 Lunch 14:00 Optimizing the Graphics Pipeline Matthias Wloka 14:45 Advanced Rendering Techniques Matthias Wloka 15:45 Break 16:15 General-Purpose Computation Using Graphics Hardware Mark Harris 17:30 End Tutorial 5: Programming Graphics Hardware Tutorial 5: Programming Graphics Hardware IntroductionIntroduction toto thethe HardwareHardware GraphicsGraphics PipelinePipeline Cyril Zeller Overview Concepts: Real-time rendering Hardware graphics pipeline Evolution of the PC hardware graphics pipeline: 1995-1998: Texture mapping and z-buffer 1998: Multitexturing 1999-2000: Transform and lighting 2001: Programmable vertex shader 2002-2003: Programmable pixel shader 2004: Shader model 3.0 and 64-bit color support PC graphics software architecture Performance numbers Tutorial 5: Programming Graphics Hardware Real-Time Rendering Graphics hardware enables real-time rendering Real-time means display rate at more than 10 images per second 3D Scene = Image = Collection of Array of pixels 3D primitives (triangles, lines, points) Tutorial 5: Programming Graphics Hardware Hardware Graphics Pipeline -
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 -
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. -
Computing for the Most Demanding Users
COMPUTING FOR THE MOST DEMANDING USERS NVIDIA Artificial intelligence, the dream of computer scientists for over half a century, is no longer science fiction. And in the next few years, it will transform every industry. Soon, self-driving cars will reduce congestion and improve road safety. AI travel agents will know your preferences and arrange every detail of your family vacation. And medical instruments will read and understand patient DNA to detect and treat early signs of cancer. Where engines made us stronger and powered the first industrial revolution, AI will make us smarter and power the next. What will make this intelligent industrial revolution possible? A new computing model — GPU deep learning — that enables computers to learn from data and write software that is too complex for people to code. NVIDIA — INVENTOR OF THE GPU The GPU has proven to be unbelievably effective at solving some of the most complex problems in computer science. It started out as an engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Today, NVIDIA’s GPU simulates human intelligence, running deep learning algorithms and acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. This is our life’s work — to amplify human imagination and intelligence. THE NVIDIA GPU DEFINES MODERN COMPUTER GRAPHICS Our invention of the GPU in 1999 made possible real-time programmable shading, which gives artists an infinite palette for expression. We’ve led the field of visual computing since. SIMULATING HUMAN IMAGINATION Digital artists, industrial designers, filmmakers, and broadcasters rely on NVIDIA Quadro® pro graphics to bring their imaginations to life. -
Nvidia Autonomous Driving Platform
NVIDIA AUTONOMOUS DRIVING PLATFORM Apr, 2017 Sr. Solution Architect , Marcus Oh Who is NVIDIA Deep Learning in Autonomous Driving Training Infra & Machine / DIGIT Contents DRIVE PX2 DRIVEWORKS USECASE Example Next Generation AD Platform 2 NVIDIA CONFIDENTIAL — DRIVE PX2 DEVELOPMENT PLATFORM NVIDIA Founded in 1993 | CEO & Co-founder: Jen-Hsun Huang | FY16 revenue: $5.01B | 9,500 employees | 7,300 patents | HQ in Santa Clara, CA | 3 NVIDIA — “THE AI COMPUTING COMPANY” GPU Computing Computer Graphics Artificial Intelligence 4 DEEP LEARNING IN AUTONOMOUS DRIVING 5 WHAT IS DEEP LEARNING? Input Result 6 Deep Learning Framework Forward Propagation Repeat “turtle” Tree Training Backward Propagation Cat Compute weight update to nudge Dog from “turtle” towards “dog” Trained Neural Net Model Inference “cat” 7 REINFORCEMENT LEARNING How’s it work? F A reinforcement learning agent includes: state (environment) actions (controls) reward (feedback) A value function predicts the future reward of performing actions in the current state Given the recent state, action with the maximum estimated future reward is chosen for execution For agents with complex state spaces, deep networks are used as Q-value approximator Numerical solver (gradient descent) optimizes github.com/dusty-nv/jetson-reinforcement the network on-the-fly based on reward inputs SELF-DRIVING CARS ARE AN AI CHALLENGE PERCEPTION AI PERCEPTION AI LOCALIZATION DRIVING AI DEEP LEARNING 9 NVIDIA AI SYSTEM FOR AUTONOMOUS DRIVING MAPPING KALDI LOCALIZATION DRIVENET Training on Driving with NVIDIA DGX-1 NVIDIA DRIVE PX 2 DGX-1 DriveWorks 10 TRAINING INFRA & MACHINE / DIGIT 11 170X SPEED-UP OVER COTS SERVER MICROSOFT COGNITIVE TOOLKIT SUPERCHARGED ON NVIDIA DGX-1 170x Faster (AlexNet images/sec) 13,000 78 8x Tesla P100 | 170TF FP16 | NVLink hybrid cube mesh CPU Server DGX-1 AlexNet training batch size 128, Dual Socket E5-2699v4, 44 cores CNTK 2.0b2 for CPU. -
Investigations of Various HPC Benchmarks to Determine Supercomputer Performance Efficiency and Balance
Investigations of Various HPC Benchmarks to Determine Supercomputer Performance Efficiency and Balance Wilson Lisan August 24, 2018 MSc in High Performance Computing The University of Edinburgh Year of Presentation: 2018 Abstract This dissertation project is based on participation in the Student Cluster Competition (SCC) at the International Supercomputing Conference (ISC) 2018 in Frankfurt, Germany as part of a four-member Team EPCC from The University of Edinburgh. There are two main projects which are the team-based project and a personal project. The team-based project focuses on the optimisations and tweaks of the HPL, HPCG, and HPCC benchmarks to meet the competition requirements. At the competition, Team EPCC suffered with hardware issues that shaped the cluster into an asymmetrical system with mixed hardware. Unthinkable and extreme methods were carried out to tune the performance and successfully drove the cluster back to its ideal performance. The personal project focuses on testing the SCC benchmarks to evaluate the performance efficiency and system balance at several HPC systems. HPCG fraction of peak over HPL ratio was used to determine the system performance efficiency from its peak and actual performance. It was analysed through HPCC benchmark that the fraction of peak ratio could determine the memory and network balance over the processor or GPU raw performance as well as the possibility of the memory or network bottleneck part. Contents Chapter 1 Introduction .............................................................................................. -
Compressed Modes User's Guide
Drivers for Windows Compressed Modes User’s Guide Version 2.0 NVIDIA Corporation August 27, 2002 NVIDIA Drivers Compressed Modes User’s Guide Version 2.0 Published by NVIDIA Corporation 2701 San Tomas Expressway Santa Clara, CA 95050 Copyright © 2002 NVIDIA Corporation. All rights reserved. This software may not, in whole or in part, be copied through any means, mechanical, electromechanical, or otherwise, without the express permission of NVIDIA Corporation. Information furnished is believed to be accurate and reliable. However, NVIDIA assumes no responsibility for the consequences of use of such information nor for any infringement of patents or other rights of third parties, which may result from its use. No License is granted by implication or otherwise under any patent or patent rights of NVIDIA Corporation. Specifications mentioned in the software are subject to change without notice. NVIDIA Corporation products are not authorized for use as critical components in life support devices or systems without express written approval of NVIDIA Corporation. NVIDIA, the NVIDIA logo, GeForce, GeForce2 Ultra, GeForce2 MX, GeForce2 GTS, GeForce 256, GeForce3, Quadro2, NVIDIA Quadro2, Quadro2 Pro, Quadro2 MXR, Quadro, NVIDIA Quadro, Vanta, NVIDIA Vanta, TNT2, NVIDIA TNT2, TNT, NVIDIA TNT, RIVA, NVIDIA RIVA, NVIDIA RIVA 128ZX, and NVIDIA RIVA 128 are registered trademarks or trademarks of NVIDIA Corporation in the United States and/or other countries. Intel and Pentium are registered trademarks of Intel. Microsoft, Windows, Windows NT, Direct3D, DirectDraw, and DirectX are registered trademarks of Microsoft Corporation. CDRS is a trademark and Pro/ENGINEER is a registered trademark of Parametric Technology Corporation. OpenGL is a registered trademark of Silicon Graphics Inc. -
Geforce 256 and RIVA TNT Combiners
Copyright NVIDIA Corporation, 1999. NVIDIA Proprietary. GeForce 256 and RIVA TNT Combiners How to best utilize the per-pixel operations using OpenGL on NVIDIA Graphics Processors 11/02/1999 1 Copyright NVIDIA Corporation, 1999. NVIDIA Proprietary. NVIDIA OpenGL Combiners (see Figure 3.1 of OpenGL 1.2 spec) Point Texture Rasterization Fetching From Line Primitive Rasterization Texture Assembly Environment Application Register Polygon Combiners Rasterization Texture Unit 0 General Stage 0 Texture Unit 1 DrawPixels Pixel Rectangle General Stage 1 Rasterization Final Stage Color Sum Bitmap Bitmap Rasterization Coverage Fog Application To fragment processing 11/02/1999 2 Copyright NVIDIA Corporation, Which Texture Environment or 1999. NVIDIA Proprietary. Combiner Extension to use? •Base OpenGL texture environment •supports modulate, replace, blend, decal •all OpenGL implementation support this • EXT_texture_env_add •supports add •widely supported extension, but not guaranteed •EXT_texture_env_combine •supports AB+C, AB+(1-A)C •additional scale & bias capability •user-defined constant •multi-vendor extension (NVIDIA & ATI) •NV_texture_env_combine4 •supports AB+CD, generalizes EXT_texture_env_combine •TNT & later NVIDIA graphics processors •NV_register_combiners •register model, non-linear data flow •signed math, input mappings, multiple dot products •subsumes texture environment, color sum, & fog stages •additional inputs: fog color & factor, & secondary color •number of stages independent of active textures •GeForce & future NVIDIA graphics -
Fabric Manager for NVIDIA Nvswitch Systems
Fabric Manager for NVIDIA NVSwitch Systems User Guide / Virtualization / High Availability Modes DU-09883-001_v0.7 | January 2021 Document History DU-09883-001_v0.7 Version Date Authors Description of Change 0.1 Oct 25, 2019 SB Initial Beta Release 0.2 Mar 23, 2020 SB Updated error handling and bare metal mode 0.3 May 11, 2020 YL Updated Shared NVSwitch APIs section with new API information 0.4 July 7, 2020 SB Updated MIG interoperability and high availability details. 0.5 July 17, 2020 SB Updated running as non-root instructions 0.6 Aug 03, 2020 SB Updated installation instructions based on CUDA repo and updated SXid error details 0.7 Jan 26, 2021 GT, CC Updated with vGPU multitenancy virtualization mode Fabric Ma nager fo r NVI DIA NVSwitch Sy stems DU-09883-001_v0.7 | ii Table of Contents Chapter 1. Overview ...................................................................................................... 1 1.1 Introduction .............................................................................................................1 1.2 Terminology ............................................................................................................1 1.3 NVSwitch Core Software Stack .................................................................................2 1.4 What is Fabric Manager?..........................................................................................3 Chapter 2. Getting Started With Fabric Manager ...................................................... 5 2.1 Basic Components...................................................................................................5 -
Programmability
Programmability - a New Frontier in Graphics Hardware A Revolution in Graphics Hardware The Near Future • Moving from graphics accelerators to processors • Full hardware OpenGL and DirectX pipelines ProgrammabilityProgrammability ChangesChanges thethe WorldWorld • graphics hardware pipelines are becoming massively programmable • will fundamentally change graphics • allows hyper-realistic characters, special effects, and lighting and shading 3D Graphics is about • Animated films (Bug’s Life, Toy Story, etc.) • Special Effects in live action movies (The Matrix) • Interactive Entertainment (Video games) • Computer Models of real world objects • Or, objects that haven’t been invented yet • Making reality more fantastic • Making fantasies seem real Why are Movie Special Effects Exciting and Interesting? • Suspension of Disbelief • Something amazing is happening • But, you believe it, because it is “real” • Realistic and detailed characters • Motion, and emotion • Realistic and recognizable materials • Chrome looks like chrome • Skin looks like skin • Action! Live action sfx slide The Year 2000 Graphics Pipeline vertex transform T & L and lighting setup rasterizer texture blending per-pixel texture fb anti-alias Pixar’s Geri – A Believable Old Man • Not a real person • Geri is built from Curved Surfaces • Curved surfaces are broken down into triangles • Each triangle is transformed into position • Each pixel in each triangle is shaded • Every frame • 24 (movie) or 60 (PC) times per second 3D Movie Special Effects Come to PC and Console Graphics -
NVIDIA DGX-1 System Architecture White Paper
White Paper NVIDIA DGX-1 System Architecture The Fastest Platform for Deep Learning TABLE OF CONTENTS 1.0 Introduction ........................................................................................................ 2 2.0 NVIDIA DGX-1 System Architecture .................................................................... 3 2.1 DGX-1 System Technologies ........................................................................... 5 3.0 Multi-GPU and Multi-System Scaling with NVLink and InfiniBand ..................... 6 3.1 DGX-1 NVLink Network Topology for Efficient Application Scaling ................ 7 3.2 Scaling Deep Learning Training on NVLink ................................................... 10 3.3 InfiniBand for Multi-System Scaling of DGX-1 Systems ................................ 13 4.0 DGX-1 Software................................................................................................. 16 4.1 NVIDIA CUDA Toolkit.................................................................................... 17 4.2 NVIDIA Docker.............................................................................................. 18 4.3 NVIDIA Deep Learning SDK .......................................................................... 19 4.4 NCCL............................................................................................................. 20 5.0 Deep Learning Frameworks for DGX-1.............................................................. 22 5.1 NVIDIA Caffe................................................................................................