NVIDIA Jetson Linux Driver Package
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High Speed Visualization in the Jetos Aviation Operating System Using Hardware Acceleration*
High Speed Visualization in the JetOS Aviation Operating System Using Hardware Acceleration* Boris Barladian[0000-0002-2391-2067], Nikolay Deryabin[0000-0003-1248-6047], Alexey Voloboy[0000-0003-1252-8294], Vladimir Galaktionov[0000-0001-6460-7539], and Lev Shapiro[0000-0002-6350-851X] The Keldysh Institute of the Applied Mathematics of RAS, Moscow, Russia [email protected],{voloboy, vlgal, pls}@gin.keldysh.ru Abstract. The paper discusses details of the pilot display visualization that uses the hardware acceleration capabilities of the Vivante graphics processor in the JetOS aviation operating system. Previously the OpenGL Safety Critical library was implemented without hardware acceleration. This was done in such a way because software library is easier to certify in accordance with the avionics re- quirements. But usage of the software OpenGL does not provide acceptable visualization speed for modern Flight Display and 3D relief applications. So more complex visualization approach utilized the GPU acceleration capabilities was elaborated. Although the OpenGL library was implemented for a specific GPU and took into account its specificity, the described approach to adapt the MESA open source library can be used for other GPUs. An effective algorithm for multi-window visualization using the implemented library with hardware acceleration is present. The described approach allows you to achieve the visu- alization speed acceptable for the pilot display of the aircraft. Keywords: Pilot Display, Embedded Systems, Real-time Operating System, OpenGL Safety Critical, Multi-windowing. 1 Introduction In [1] requirements were formulated for a real-time operating system (RTOS) de- signed to work with integrated modular avionics. In particular, the RTOS should comply with the ARINC 653 standard [2]. -
Porting Tizen-IVI 3.0 to an ARM Based Soc Platform
Porting Tizen-IVI 3.0 to an ARM based SoC Platform Damian Hobson-Garcia Automotive Linux Summit July 1-2, 2014 Tokyo, Japan Tizen IVI support Until recently… Intel architecture (x86) system – Tizen IVI 2.0alpha, Tizen IVI 3.0 ARM architecture based system – Tizen IVI 2.0alpha (ivi-panda) Need to port Tizen IVI 3.0 to ARM ourselves Current State of Affairs Intel architecture (x86) system – Tizen IVI 2.0alpha, Tizen IVI 3.0 – Tizen Common NEW ARM architecture based system – Tizen IVI 2.0alpha (ivi-panda) – Tizen Common NEW Tizen IVI now based on Tizen Common – Lots of reuse Target Platform Renesas R-Car Gen2 series platform R-Car M2 – ARM Cortex A15 x2 R-Car H2 – ARM Cortex A15 x4, + ARM Cortex A7 x4 (option) 3D Graphics System – Imagination Technologies PowerVR series On board IP blocks – H/W video decode/encode – image processing Agenda Objective Methodology Porting Tasks – Weston/Wayland Integration – WebKit Integration – GStreamer Integration Objective Tizen IVI 3.0 on R-Car M2/H2 1. Standard Native Applications – Terminal program – Open GL/ES applications 2. Web – Browser and web applications 3. Multimedia – Video playback (1080p @ 30fps) Local Build Methodology Tizen IVI 3.0 milestone releases we used: – M2-Sep (released Oct 11, 2013) – M2-EOY (released Jan 15, 2014) – M2-March2014 (released April 11, 2014) Non-hardware dependant packages – Rebuild for ARM instruction set Hardware dependant packages – Replace/update with R-Car M2/H2 support Tizen Common/IVI Rebase Methodology Reuse Tizen Common ARM support for Tizen -
Nvidia Video Codec Sdk - Encoder
NVIDIA VIDEO CODEC SDK - ENCODER Application Note vNVENC_DA-6209-001_v14 | July 2021 Table of Contents Chapter 1. NVIDIA Hardware Video Encoder.......................................................................1 1.1. Introduction............................................................................................................................... 1 1.2. NVENC Capabilities...................................................................................................................1 1.3. NVENC Licensing Policy...........................................................................................................3 1.4. NVENC Performance................................................................................................................ 3 1.5. Programming NVENC...............................................................................................................5 1.6. FFmpeg Support....................................................................................................................... 5 NVIDIA VIDEO CODEC SDK - ENCODER vNVENC_DA-6209-001_v14 | ii Chapter 1. NVIDIA Hardware Video Encoder 1.1. Introduction NVIDIA GPUs - beginning with the Kepler generation - contain a hardware-based encoder (referred to as NVENC in this document) which provides fully accelerated hardware-based video encoding and is independent of graphics/CUDA cores. With end-to-end encoding offloaded to NVENC, the graphics/CUDA cores and the CPU cores are free for other operations. For example, in a game recording scenario, -
Lightdm Lightdm
02/10/2021 20:51 1/8 LightDM LightDM Objet : installation et configuration du gestionnaire de connexion Lightdm. Niveau requis : débutant, avisé . Commentaires : LighDM est une alternative à GDM ou KDM. Débutant, à savoir : Utiliser GNU/Linux en ligne de commande, tout commence là ! Suivi : à-tester Création par black_sun_2012 le 18/07/2012. Mise-à-jour par paskal le 29/10/2013. Mise à jour par Freddec le 07/12/2015, rajout du paragraphe. Verrouiller le pavé numérique dés le lancement de LightDM. Testé par <…> le <…> Commentaires sur le forum : Lien vers le forum concernant ce tuto 1) Présentation LightDM est un gestionnaire d'affichage multi-desktop. Il a été conçu pour être une alternative relativement légère et très personnalisable à GDM. LightDM a été introduit depuis Wheezy, il est suffisamment stable pour être aussi utilisé dans Stretch et Sid. Installation Installer2) le paquet lightdm : apt-get update && apt-get install lightdm Depuis stretch on peut aussi utiliser la commande apt ainsi : apt update && apt install lightdm Configuration LightDM est configurable en éditant3) le fichier : /etc/lightdm/lightdm.conf Il est recommandé d'effectuer une sauvegarde du fichier de configuration avant d'essayer de le Documentation - Wiki - http://debian-facile.org/ Last update: 31/08/2021 11:24 doc:environnements:x11:lightdm http://debian-facile.org/doc:environnements:x11:lightdm configurer. Pour changer le gestionnaire d'affichage par défaut courant, exécutez : dpkg-reconfigure lightdm puis sélectionnez : lightdm Si vous débutez avec LightDM, mieux vaut avoir GDM, SLiM ou un autre gestionnaire d'affichage installé en sauvegarde. Pour connaître les différentes clés je vous renvoie sur la doc : ubuntu Et aussi quelques explications sur différents paramètres et comment les modifier : aller plus loin Modifier le fond de l'écran d'accueil La configuration de l'écran d'accueil GTK de LightDM avec Debian se trouve dans le fichier /etc/lightdm/lightdm-gtk-greeter.conf. -
Performance Analysis and Optimization Opportunities for NVIDIA Automotive Gpus
Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs Hamid Tabani∗, Fabio Mazzocchetti∗y, Pedro Benedicte∗y, Jaume Abella∗ and Francisco J. Cazorla∗ ∗ Barcelona Supercomputing Center y Universitat Politecnica` de Catalunya Abstract—Advanced Driver Assistance Systems (ADAS) and products such as Renesas R-Car H3 [1], NVIDIA Jetson Autonomous Driving (AD) bring unprecedented performance TX2 [20] and NVIDIA Jetson Xavier [35], [27] have already requirements for automotive systems. Graphic Processing Unit reached the market building upon GPU technology inherited (GPU) based platforms have been deployed with the aim of meeting these requirements, being NVIDIA Jetson TX2 and from the high-performance domain. Automotive GPUs have its high-performance successor, NVIDIA AGX Xavier, relevant inherited designs devised for the high-performance domain representatives. However, to what extent high-performance GPU with the aim of reducing costs in the design, verification and configurations are appropriate for ADAS and AD workloads validation process for chip manufacturers. remains as an open question. Unfortunately, reusability of high-performance hardware This paper analyzes this concern and provides valuable does not consider GPUs efficiency in the automotive domain insights on this question by modeling two recent automotive NVIDIA GPU-based platforms, namely TX2 and AGX Xavier. In and, to the best of our knowledge, the design space for GPUs, particular, our work assesses their microarchitectural parameters where resources are sized with the aim of optimizing specific against relevant benchmarks, identifying GPU setups delivering goals such as performance, has not been yet thoroughly increased performance within a similar cost envelope, or decreas- performed for the automotive domain. ing hardware costs while preserving original performance levels. -
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 -
Msi Nvidia Drivers Download Update MSI Graphics Card Driver on Windows 10 & 7
msi nvidia drivers download Update MSI Graphics Card Driver on Windows 10 & 7. Easily! Updating MSI graphics card drivers provides you with high gaming performance. So it’s recommended you keep your graphics card driver up-to- date. In this post, you’ll learn two ways to download and install the latest MSI graphics card driver. Download the MSI graphics card driver manually Download and install the MSI graphics card driver automatically. Way 1: Download the MSI graphics card driver manually. MSI provides the graphics driver on their website, for instance, the NVIDIA graphics card driver and the AMD graphics card driver. So you can check for and download the latest driver you need for your graphics card from MSI’s website. The driver always can be downloaded on the SUPPORT section. Go to MSI website and enter the name of your graphics card and perform a quick search. Then follow the on-screen instructions to download the driver you need. MSI always uploads new drivers to their website. So it’s recommended you to check for the driver release often in order to get the latest driver in time. If you don’t have time and patience to download the driver manually, Way 2 may be a better option for you. Way 2 : Download and install the MSI graphics card driver automatically. If you don’t have the time, patience or computer skills to update the MSI graphics driver manually, you can do it automatically with Driver Easy . Driver Easy will automatically recognize your system and find the correct drivers for it. -
A Low-Cost Deep Neural Network-Based Autonomous Car
DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car Michael G. Bechtely, Elise McEllhineyy, Minje Kim?, Heechul Yuny y University of Kansas, USA. fmbechtel, elisemmc, [email protected] ? Indiana University, USA. [email protected] Abstract—We present DeepPicar, a low-cost deep neural net- task may be directly linked to the safety of the vehicle. This work based autonomous car platform. DeepPicar is a small scale requires a high computing capacity as well as the means to replication of a real self-driving car called DAVE-2 by NVIDIA. guaranteeing the timings. On the other hand, the computing DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces hardware platform must also satisfy cost, size, weight, and car steering angles as output. DeepPicar uses the same net- power constraints, which require a highly efficient computing work architecture—9 layers, 27 million connections and 250K platform. These two conflicting requirements complicate the parameters—and can drive itself in real-time using a web camera platform selection process as observed in [25]. and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we To understand what kind of computing hardware is needed analyze the Pi 3’s computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. for AI workloads, we need a testbed and realistic workloads. We also systematically compare other contemporary embedded While using a real car-based testbed would be most ideal, it computing platforms using the DeepPicar’s CNN-based real-time is not only highly expensive, but also poses serious safety control workload. -
Tegra Linux Driver Package
TEGRA LINUX DRIVER PACKAGE RN_05071-R32 | March 18, 2019 Subject to Change 32.1 Release Notes RN_05071-R32 Table of Contents 1.0 About this Release ................................................................................... 3 1.1 Login Credentials ............................................................................................... 4 2.0 Known Issues .......................................................................................... 5 2.1 General System Usability ...................................................................................... 5 2.2 Boot .............................................................................................................. 6 2.3 Camera ........................................................................................................... 6 2.4 CUDA Samples .................................................................................................. 7 2.5 Multimedia ....................................................................................................... 7 3.0 Top Fixed Issues ...................................................................................... 9 3.1 General System Usability ...................................................................................... 9 3.2 Camera ........................................................................................................... 9 4.0 Documentation Corrections ..................................................................... 10 4.1 Adaptation and Bring-Up Guide ............................................................................ -
The Linux Graphics Stack Attributions
I - Hardware : Anatomy of a GPU II - Host : The Linux graphics stack Attributions Introduction to GPUs and to the Linux Graphics Stack Martin Peres CC By-SA 3.0 Nouveau developer Ph.D. student at LaBRI November 26, 2012 1 / 36 I - Hardware : Anatomy of a GPU II - Host : The Linux graphics stack Attributions General overview Outline 1 I - Hardware : Anatomy of a GPU General overview Driving screens Host < − > GPU communication 2 II - Host : The Linux graphics stack General overview DRM and libdrm Mesa X11 Wayland X11 vs Wayland 3 Attributions Attributions 2 / 36 I - Hardware : Anatomy of a GPU II - Host : The Linux graphics stack Attributions General overview General overview of a modern GPU's functions Display content on a screen Accelerate 2D operations Accelerate 3D operations Decode videos Accelerate scientific calculations 3 / 36 I - Hardware : Anatomy of a GPU II - Host : The Linux graphics stack Attributions General overview CPU Clock Front-side Graphics Generator bus card slot Chipset Memory Slots High-speed graphics bus (AGP or PCI Northbridge Memory Express) bus (memory controller hub) Internal Bus PCI Bus Onboard Southbridge graphics PCI (I/O controller controller Bus hub) IDE SATA USB Cables and Ethernet ports leading Audio Codec CMOS Memory off-board PCI Slots LPC Bus Super I/O Serial Port Parallel Port Flash ROM Floppy Disk Keyboard (BIOS) Mouse 4 / 36 I - Hardware : Anatomy of a GPU II - Host : The Linux graphics stack Attributions General overview Hardware architecture GPU: Where all the calculations are made VRAM: Stores -
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 -
Getting Started with Ubuntu 12.04
Getting Started withUbuntu 12.04 Second Edition The Ubuntu Manual Team Copyright © – by e Ubuntu Manual Team. Some rights reserved. cba is work is licensed under the Creative Commons Aribution–Share Alike . License. To view a copy of this license, see Appendix A, visit http://creativecommons.org/licenses/by-sa/./, or send a leer to Creative Commons, Second Street, Suite , San Francisco, California, , USA. Geing Started with Ubuntu . can be downloaded for free from http:// ubuntu-manual.org/ or purchased from http://ubuntu-manual.org/buy/ gswue/en_US. A printed copy of this book can be ordered for the price of printing and delivery. We permit and even encourage you to dis- tribute a copy of this book to colleagues, friends, family, and anyone else who might be interested. http://ubuntu-manual.org Second Edition Revision number: Revision date: -- :: + Contents Prologue Welcome Ubuntu Philosophy A brief history of Ubuntu Is Ubuntu right for you? Contact details About the team Conventions used in this book Installation Geing Ubuntu Trying out Ubuntu Installing Ubuntu—Geing started Finishing Installation Ubuntu installer for Windows e Ubuntu Desktop Understanding the Ubuntu desktop Unity Using Launcher e Dash Workspaces Managing windows Browsing files on your computer Nautilus file manager Searching for files and folders on your computer Customizing your desktop Accessibility Session options Geing help Working with Ubuntu All the applications you need Geing online Browsing the web Reading and composing email Using instant messaging Microblogging Viewing and editing photos Watching videos and movies Listening to audio and music Burning CDs and DVDs Working with documents, spreadsheets, and presentations Ubuntu One Hardware Using your devices Hardware identification .