Mechdyne-TGX-2.1-Installation-Guide
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Reverse Engineering Power Management on NVIDIA Gpus - Anatomy of an Autonomic-Ready System Martin Peres
Reverse Engineering Power Management on NVIDIA GPUs - Anatomy of an Autonomic-ready System Martin Peres To cite this version: Martin Peres. Reverse Engineering Power Management on NVIDIA GPUs - Anatomy of an Autonomic-ready System. ECRTS, Operating Systems Platforms for Embedded Real-Time appli- cations 2013, Jul 2013, Paris, France. hal-00853849 HAL Id: hal-00853849 https://hal.archives-ouvertes.fr/hal-00853849 Submitted on 23 Aug 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Reverse engineering power management on NVIDIA GPUs - Anatomy of an autonomic-ready system Martin Peres Ph.D. student at LaBRI University of Bordeaux Hobbyist Linux/Nouveau Developer Email: [email protected] Abstract—Research in power management is currently limited supported nor documented by NVIDIA. As GPUs are leading by the fact that companies do not release enough documentation the market in terms of performance-per-Watt [3], they are or interfaces to fully exploit the potential found in modern a good candidate for a reverse engineering effort of their processors. This problem is even more present in GPUs despite power management features. The choice of reverse engineering having the highest performance-per-Watt ratio found in today’s NVIDIA’s power management features makes sense as they processors. -
Copyright by Jian He 2020 the Dissertation Committee for Jian He Certifies That This Is the Approved Version of the Following Dissertation
Copyright by Jian He 2020 The Dissertation Committee for Jian He certifies that this is the approved version of the following dissertation: Empowering Video Applications for Mobile Devices Committee: Lili Qiu, Supervisor Mohamed G. Gouda Aloysius Mok Xiaoqing Zhu Empowering Video Applications for Mobile Devices by Jian He DISSERTATION Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY THE UNIVERSITY OF TEXAS AT AUSTIN May 2020 Acknowledgments First and foremost, I want to thank my advisor Prof. Lili Qiu, for the support and guidance I have received over the past few years. I appreciate all her contributions of time, ideas and funding to make my Ph.D. experience productive and stimulating. The enthusiasm she has for her research signifi- cantly motivated to concentrate on my research especially during tough times in my Ph.D. pursuit. She taught me how to crystallize ideas into solid and fancy research works. I definitely believe that working with her will help me have a more successful career in the future. I also want to thank all the members in my dissertation committee, Prof. Mohamed G. Gouda, Prof. Aloysius Mok and Dr. Xiaoqing Zhu. I owe many thanks to them for their insightful comments on my dissertation. I was very fortunate to collaborate with Wenguang Mao, Mubashir Qureshi, Ghufran Baig, Zaiwei Zhang, Yuchen Cui, Sangki Yun, Zhaoyuan He, Chenxi Yang, Wangyang Li and Yichao Chen on many interesting works. They always had time and passion to devote to my research projects. -
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]. -
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, -
Transcoding SDK Combine Your Encoding Presets Into a Single Tool
DATASHEET | Page 1 Transcoding SDK Combine your encoding presets into a single tool MainConcept Transcoding SDK is an all-in-one production tool offering HOW DOES IT WORK? developers the ability to manage multiple codecs and parameters in one • Transcoding SDK works as an place. This streamlined SDK supports the latest encoders and decoders additional layer above MainConcept from MainConcept, including HEVC/H.265, AVC/H.264, DVCPRO, and codecs. MPEG-2. The transcoder generates compliant streams across different • The easy-to-use API replaces the devices, media types, and camcorder formats, and includes support for need to set conversion parameters MPEG-DASH and Apple HLS adaptive bitstream formats. Compliance manually by allowing you to configure ensures content is delivered that meets each unique specification. the encoders with predefined profiles, letting the transcoding engine take Transcoding SDK was created to simplify the workflow for developers care of the rest. who frequently move between codecs and output to a multitude of • If needed, manual control of the configurations. conversion process is supported, including source/target destinations, export presets, transcoding, and filter AVAILABLE PACKAGES parameters. HEVC/H.265 HEVC/H.265 encoder for creating HLS, DASH-265, and other ENCODER PACKAGE generic 8-bit/10-bit 4:2:0 and 4:2:2 streams in ES, MP4 and TS file formats. Includes hardware encoding support using Intel Quick KEY FEATURES Sync Video (IQSV) and NVIDIA NVENC (including Hybrid GPU) for Windows and Linux. • Integrated SDKs for fast deployment HEVC/H.265 SABET HEVC/H.265 encoder package plus Smart Adaptive Bitrate Encod- of transcoding tools ENCODER PACKAGE ing Technology (SABET). -
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 -
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 -
Processing Multimedia Workloads on Heterogeneous Multicore Architectures
Doctoral Dissertation Processing Multimedia Workloads on Heterogeneous Multicore Architectures H˚akon Kvale Stensland February 2015 Submitted to the Faculty of Mathematics and Natural Sciences at the University of Oslo in partial fulfilment of the requirements for the degree of Philosophiae Doctor © Håkon Kvale Stensland, 2015 Series of dissertations submitted to the Faculty of Mathematics and Natural Sciences, University of Oslo No. 1601 ISSN 1501-7710 All rights reserved. No part of this publication may be reproduced or transmitted, in any form or by any means, without permission. Cover: Hanne Baadsgaard Utigard. Printed in Norway: AIT Oslo AS. Produced in co-operation with Akademika Publishing. The thesis is produced by Akademika Publishing merely in connection with the thesis defence. Kindly direct all inquiries regarding the thesis to the copyright holder or the unit which grants the doctorate. Abstract Processor architectures have been evolving quickly since the introduction of the central processing unit. For a very long time, one of the important means of increasing per- formance was to increase the clock frequency. However, in the last decade, processor manufacturers have hit the so-called power wall, with high heat dissipation. To overcome this problem, processors were designed with reduced clock frequencies but with multiple cores and, later, heterogeneous processing elements. This shift introduced a new challenge for programmers: Legacy applications, written without parallelization in mind, gain no benefits from moving to multicore and heterogeneous architectures. Another challenge for the programmers is that heterogeneous architecture designs are very different with respect to caches, memory types, execution unit organization, and so forth and, in the worst case, a programmer must completely rewrite the application to obtain the best performance on the new architecture. -
NVIDIA RTX Virtual Workstation
NVIDIA RTX Virtual Workstation Sizing Guide NVIDIA RTX Virtual Workstation | 1 Executive Summary Document History nv-quadro-vgpu-deployment-guide-citrixonvmware-v2-082020 Version Date Authors Description of Change 01 Aug 17, 2020 AFS, JJC, EA Initial Release 02 Jan 08, 2021 CW Version 2 03 Jan 14, 2021 AFS Branding Update NVIDIA RTX Virtual Workstation | 2 Executive Summary Table of Contents Chapter 1. Executive Summary ......................................................................................................... 5 1.1 What is NVIDIA RTX vWS? ....................................................................................................... 5 1.2 Why NVIDIA vGPU? ................................................................................................................. 6 1.3 NVIDIA vGPU Architecture....................................................................................................... 6 1.4 Recommended NVIDIA GPU’s for NVIDIA RTX vWS ................................................................ 7 Chapter 2. Sizing Methodology......................................................................................................... 9 2.1 vGPU Profiles ........................................................................................................................... 9 2.2 vCPU Oversubscription .......................................................................................................... 10 Chapter 3. Tools ............................................................................................................................. -
Linux, Yocto and Fpgas
Embedded Open Source Experts Linux, Yocto and FPGAs Integrating Linux and Yocto builds into different SoCs From a Linux software perspective: ➤ Increased demand for Linux on FPGAs ➤ Many things to mange, both technical and practical ➤ FPGAs with integrated CPU cores – very similar many other SoCs Here are some experiences and observations... © Codiax 2019 ● Page 2 Why use Linux? ➤ De-facto standard ➤ Huge HW support ➤ FOSS ➤ Flexible ➤ Adaptable ➤ Stable ➤ Scalable ➤ Royalty free ➤ Vendor independent ➤ Large community ➤ Long lifetime Why not Linux? ➤ Too big ➤ Real-time requirements ➤ Certification ➤ Boot time ➤ Licensing ➤ Too open? Desktop Shells: Desktop Display server: Display BrailleDisplay Touch-Screen Mouse & Keyboard Wayland Compositor Wayland + development tools = a lot code!of source Linux system example weston, clayton,mutter,KWin evdev libinput GNOME Shell D radeon nouveau lima etna_viv freedreno tegra-re lima nouveau radeon freedreno etna_viv e libwayland-server libwayland-server s Cinnamon k t o kms p Linux kernel, Linux kernel, Plasma 2 w i (Kernel Mode Setting) Mode (Kernel d g Cairo-Dock e t s drm (Direct Rendering Manager) Rendering (Direct drm cache coherent L2-Caches L2-Caches cache coherent CPU &GPU Enlight. DR19 System libraries: System oflibraries): form (in the Toolkits Interface User µClibc Pango glibc glibc main memory possibly adaptations to Wayland/Mir libwayland / COGL libwayland Cairo Cairo (Xr) GTK+ Clutter 2D Application 2D GModule GThread GThread GLib GObject Glib GIO ATK devicedrivers other& modules System -
NVIDIA GRID™ Virtual PC and Virtual Apps
NVIDIA VIRTUAL PC AND VIRTUAL APPS GPU-ACCELERATED PERFORMANCE FOR THE VIRTUAL ENTERPRISE Desktop virtualization has been around for many Reason 1: Flexible Workers Require a years, but some organizations still struggle to Seamless Experience deliver a user experience that stands up to what As organizations form return to work plans, workers have enjoyed on physical PCs. Remote it is clear that remote work will be part of the offices, virtual collaboration, and fast access long term solution. By allowing employees to seamlessly transition between the office and to information, coupled with the rising use of home, organizations are adopting the flexible modern, graphics-intensive applications, make office model. User experience has become more GPUs more relevant than ever. important than ever. Video collaboration, as well as simple productivity applications found in Microsoft NVIDIA® Virtual PC (vPC) and Virtual Apps (vApps) Windows 10 (Win 10), Office 365, web browsers, and improve virtual desktops and applications for streaming video can benefit from GPU acceleration. every user, with proven performance built on NVIDIA GPUs for exceptional productivity, security, and IT manageability. The virtualization software divides NVIDIA GPU resources, so the GPU can be 82% of organizations shared across multiple virtual machines running have the option of any application. working remotely. Here are three powerful reasons to deploy vPC and vApps in your data center. Traditional desktop and laptop PCs boost application performance with embedded or integrated GPUs. NVIDIA VIRTUAL PC AND VIRTUAL APPS | SOlutiON OvERVIEW | SEP21 However, when making the transition from physical to Reason 3: There Are More Users to Support virtual, IT has traditionally left the computer graphics Than Ever Before burden—such as from DirectX and OpenGL workloads Today’s virtual desktops and applications require and video streaming—to be handled by a server CPU.