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Setting up Odroid C2 – Throughtput Testing
Throughput Testing Single-Board Computer for Wireless LAN Professionals Setting up Odroid C2 As Test End-Point Inventory Project Items Unpack and knoll the following items ❑ Impact Strong 5200 mAh USB Battery o And it has a flashlight as well! ❑ USB Micro Power Cable (comes with Battery) ❑ Micro SD Card to eMCC adapter (Blueish green) ❑ eMCC Memory Card (small with Red label) ❑ Transcend USB 3.0 SD Card Reader ❑ Odroid Wi-Fi 4 NIC – (Black) ❑ USB ‘U-bend’ Adaper (Black ❑ Odroid C2 – Single Board Computer ❑ #WLPC_EU USB Drive (has the Image) ❑ Odroid Case Kit (Black) Later we will have you use as part of the testing (not in your own kit) • Small Phillips Screwdriver (to assemble your case) • Short Ethernet Cable (to test your unit when attached to a switch port) ❑ If you are running Windows copy the Win32DiskImager-0.9.5-binary executable as well. If you are running Windows follow the Windows directions, if Mac OS – move to the Mac OS directions. Mac OS Version Configure Image ❑ Copy Image File from the #WLPC_EU USB drive (in the Odroid for #WLPC_EU folder) to your Desktop… the file name is DietPi_v133_OdroidC2-arm64- (WLAN_PRO) ❑ Open Image File – Boot Drive ❑ Should have a Folder Call “Boot” ❑ Find the dietpi.txt We are changing setting so your Odroid device can join a local SSID and download more files we’ll need. ❑ Under the Wi-Fi Details section change the Wi-Fi SSID to WLPC_EU-01 with a PSK of password. #Enter your Wifi details below, if applicable (Case Sensitive). Wifi_SSID=WLPC_EU-01 Wifi_KEY=password ❑ Under WiFi Hotspot Settings, -
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. -
Download Speeds Performance and Optimized for Long Battery Life
® QUALCOMM FEATURING THE LATEST IN MOBILE TECHNOLOGY. TM SNAPDRAGON Capture sharper, higher-quality images, in challenging lighting situations Qualcomm’s Spectra 14-bit dual image signal processors tap into the enhanced performance and feature enhancements that Hexagon 680 DSP’s HVX 820MOBILE PROCESSOR performance adds with amazing features like Low Light Photo and Video and Touch-to-Track where ISP and DSP work intelligently together to enhance imaging as well as track movements and improve zoom. Enabling a more immersive, intuitive and Immersive, life-like connected experience. virtual reality The Snapdragon 820 mobile processor Experience realistic, visual and audio immersion and offers many advantages: smooth VR action enabled by Snapdragon 820’s • New X12 LTE: Industry leading Heterogeneous compute platform, designed for high connectivity with LTE download speeds performance and optimized for long battery life. of up to 600 Mbps and multi-gigabit 802.11ad Wi-Fi • New Qualcomm® Kryo CPU: Delivering Next-generation maximum performance and low power consumption Kryo is QTI’s first custom computer vision 64-bit quad-core CPU, manufactured Drive more safely with object detection and enhanced in advanced 14nm FinFET LPP process navigation and enhance your smartphone camera • New Qualcomm® Adreno 530: Up to capability with features that can track faces and 40% better graphics and compute objects for a more intelligent mobile experience. performance with the Adreno 530 GPU • Qualcomm Spectra™ 14-bit dual image signal processors (ISPs) deliver high Deeply immersive resolution DSLR-quality images using heterogeneous compute for advanced 3D gaming processing and additional power The combination of Snapdragon 820’s Adreno 530 GPU savings and Kryo CPU creates enough compute performance • New Hexagon 680 DSP includes to enable console quality games and exciting, next Hexagon Vector eXtensions and Sensor generation virtual reality applications. -
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. -
An Evolution of Mobile Graphics
AN EVOLUTION OF MOBILE GRAPHICS Michael C. Shebanow Vice President, Advanced Processor Lab Samsung Electronics July 20, 20131 DISCLAIMER • The views herein are my own • They do not represent Samsung’s vision nor product plans 2 • The Mobile Market • Review of GPU Tech • GPU Efficiency • User Experience • Tech Challenges • Summary 3 The Rise of the Mobile GPU & Connectivity A NEW WORLD COMING? 4 DISCRETE GPU MARKET Flattening 5 MOBILE GPU MARKET Smart • In 2012, an estimated 800+ Phones million mobile GPUs shipped “Phablets” • ~123M tablets • ~712M smart phones Tablets • Will easily exceed 1B in the coming years • Trend: • Discrete GPU relatively flat • Mobile is growing rapidly 6 WW INTERNET TRAFFIC • Source: Cisco VNI Mobile INET IP Traffic growth Traffic • Internet traffic growth Year (TB/sec) rate (TB/sec) rate is staggering 2005 0.9 0.00 2006 1.5 65% 0.00 • 2012 total traffic is 2007 2.5 61% 0.01 13.7 GB per person 2008 3.8 54% 0.01 per month 2009 5.6 45% 0.04 2010 7.8 40% 0.10 • 2012 smart phone 2011 10.6 36% 0.23 traffic at 2012 12.4 17% 0.34 0.342 GB per person per month • 2017 smart phone traffic expected at 2.7 GB per person per month 7 WHERE ARE WE HEADED?… • Enormous quantity of GPUs • Large amount of interconnectivity • Better I/O 8 GPU Pipelines A BRIEF REVIEW OF GPU TECH 9 MOBILE GPU PIPELINE ARCHITECTURES Tile-based immediate mode rendering IA VS CCV RS PS ROP (TBIMR) Tile-based deferred IA VS CCV scene rendering (TBDR) RS PS ROP IA = input assembler VS = vertex shader CCV = cull, clip, viewport transform RS = rasterization, -
Mali-400 MP: a Scalable GPU for Mobile Devices
Mali-400 MP: A Scalable GPU for Mobile Devices Tom Olson Director, Graphics Research, ARM Outline . ARM and Mobile Graphics . Design Constraints for Mobile GPUs . Mali Architecture Overview . Multicore Scaling in Mali-400 MP . Results 2 About ARM . World’s leading supplier of semiconductor IP . Processor Architectures and Implementations . Related IP: buses, caches, debug & trace, physical IP . Software tools and infrastructure . Business Model . License fees . Per-chip royalties . Graphics at ARM . Acquired Falanx in 2006 . ARM Mali is now the world’s most widely licensed GPU family 3 Challenges for Mobile GPUs . Size . Power . Memory Bandwidth 4 More Challenges . Graphics is going into “anything that has a screen” . Mobile . Navigation . Set Top Box/DTV . Automotive . Video telephony . Cameras . Printers . Huge range of form factors, screen sizes, power budgets, and performance requirements . In some applications, a huge difference between peak and average performance requirements 5 Solution: Scalability . Address a wide variety of performance points and applications with a single IP and a single software stack. Need static scalability to adapt to different peak requirements in different platforms / markets . Need dynamic scalability to reduce power when peak performance isn’t needed 6 Options for Scalability . Fine-grained: Multiple pipes, wide SIMD, etc . Proven approach, efficient and effective . But, adding pipes / lanes is invasive . Hard for IP licensees to do on their own . And, hard to partition to provide dynamic scalability . Coarse-grained: Multicore . Easy for licensees to select desired performance . Putting cores on separate power islands allows dynamic scaling 7 Mali 400-MP Top Level Architecture Asynch Mali-400 MP Top-Level APB Geometry Pixel Processor Pixel Processor Pixel Processor Pixel Processor Processor #1 #2 #3 #4 CLKs MaliMMUs RESETs IRQs IDLEs MaliL2 AXI . -
Intel Desktop Board DG41MJ Product Guide
Intel® Desktop Board DG41MJ Product Guide Order Number: E59138-001 Revision History Revision Revision History Date -001 First release of the Intel® Desktop Board DG41MJ Product Guide February 2009 If an FCC declaration of conformity marking is present on the board, the following statement applies: FCC Declaration of Conformity This device complies with Part 15 of the FCC Rules. Operation is subject to the following two conditions: (1) this device may not cause harmful interference, and (2) this device must accept any interference received, including interference that may cause undesired operation. For questions related to the EMC performance of this product, contact: Intel Corporation, 5200 N.E. Elam Young Parkway, Hillsboro, OR 97124 1-800-628-8686 This equipment has been tested and found to comply with the limits for a Class B digital device, pursuant to Part 15 of the FCC Rules. These limits are designed to provide reasonable protection against harmful interference in a residential installation. This equipment generates, uses, and can radiate radio frequency energy and, if not installed and used in accordance with the instructions, may cause harmful interference to radio communications. However, there is no guarantee that interference will not occur in a particular installation. If this equipment does cause harmful interference to radio or television reception, which can be determined by turning the equipment off and on, the user is encouraged to try to correct the interference by one or more of the following measures: • Reorient or relocate the receiving antenna. • Increase the separation between the equipment and the receiver. • Connect the equipment to an outlet on a circuit other than the one to which the receiver is connected. -
Improving the Beaglebone Board with Embedded Ubuntu, Enhanced GPMC Driver and Python for Communication and Graphical Prototypes
Final Master Thesis Improving the BeagleBone board with embedded Ubuntu, enhanced GPMC driver and Python for communication and graphical prototypes By RUBÉN GONZÁLEZ MUÑOZ Directed by MANUEL M. DOMINGUEZ PUMAR FINAL MASTER THESIS 30 ECTS, JULY 2015, ELECTRICAL AND ELECTRONICS ENGINEERING Abstract Abstract BeagleBone is a low price, small size Linux embedded microcomputer with a full set of I/O pins and processing power for real-time applications, also expandable with cape pluggable boards. The current work has been focused on improving the performance of this board. In this case, the BeagleBone comes with a pre-installed Angstrom OS and with a cape board using a particular software “overlay” and applications. Due to a lack of support, this pre-installed OS has been replaced by Ubuntu. As a consequence, the cape software and applications need to be adapted. Another necessity that emerges from the stated changes is to improve the communications through a GPMC interface. The depicted driver has been built for the new system as well as synchronous variants, also developed and tested. Finally, a set of applications in Python using the cape functionalities has been developed. Some extra graphical features have been included as example. Contents Contents Abstract ..................................................................................................................................................................................... 5 List of figures ......................................................................................................................................................................... -
Suzanne's Microcluster Slides
csinparallel.org Microclusters for teaching PDC Suzanne J. Matthews (West Point) 1 csinparallel.org What is a Microcluster? • A personal, highly portable Beowulf cluster • Enables highly interactive and tactile experiential learning • Notable early examples: – Ultimate Linux Lunch Box (Ron Minnich and Mitch Williams, Sandia National Labs) – LittleFe (Charlie Peck, Earlham College) – Microwulf (Joel Adams, Calvin College) 2 csinparallel.org Single Board Computers (SBCs) 3 csinparallel.org Student Pi (West Point) Suzanne J. Matthews Raspberry Pi nodes - Prototype: Raspberry Pi B nodes - Initial: Raspberry Pi B+ nodes - Current: Raspberry Pi 2 nodes - 900 Mhz quad-core CPU, 1 GB of RAM, HDMI, USB, 10/100 Ethernet - Raspbian Linux June 2014 - ~$40 p/node - Materials: - http://suzannejmatthews.com/private/cluster.html October 2014 May 2016 4 csinparallel.org Student Parallella (West Point) Suzanne J. Matthews Parallella nodes - dual-core ARM A9 CPU, 16-core Epiphany co-processor, 1 GB of RAM, μHDMI, μUSB, Gigabit Ethernet - Linaro Linux - ~$145 p/node - Materials: - http://suzannejmatthews.com/private/cluster.html - http://suzannejmatthews.github.io/ October 2014 April 2016 January 2015 5 csinparallel.org Half ShoeBox Clusters (Centre College) David Toth Cubieboard/ODROID nodes (2-node clusters) - Prototype: Cubieboard2: dual-core ARM Cortex A7, 1 GB of RAM, HDMI, USB, 10/100 Ethernet - Latest: ODROID C2: 2Ghz quad-core A53, 2 GB of RAM, HDMI, USB, Gigabit Ethernet, - Android/Ubuntu Linux - ~ $150-$200 p/cluster - Materials: Early 2014 - http://web.centre.edu/david.toth/portablecluster/index.html -
Passmark - Android Device List
PassMark - Android Device List https://www.androidbenchmark.net/device_list.php AndroidTM Benchmarks Performance Comparison of Android Devices Below is an alphabetical list of all Android device types that appear in the charts. Clicking on a specific device name will take you to the charts where it appears in and will highlight it for you. PassMark Rating CPUMark Rating PassMark Rank Android Device Type Samples (higher is better) (higher is better) (lower is better) 4G R17S 1,572 4,088 1253 1 A-gold BV9500Plus 5,052 13,068 375 1 A-gold BV9800 4,450 11,400 487 1 A-gold F1 4,237 10,869 531 7 A-gold S3_Pro 4,392 11,219 504 2 A-gold Z2_PRO 4,406 11,246 499 1 A1 Alpha 20+ 4,753 12,266 435 1 Acer A3-A40 1,982 5,269 1082 1 Acer AO722 519 1,272 1725 1 1 z 62 2020-10-14, 12:02 PassMark - Android Device List https://www.androidbenchmark.net/device_list.php PassMark Rating CPUMark Rating PassMark Rank Android Device Type Samples (higher is better) (higher is better) (lower is better) AGM A10 2,030 8,521 1066 1 ALCATEL A574BL 497 1,202 1736 1 AlcatelOneTouch Alcatel_5044R 438 1,129 1759 1 Alco CT9223W97 1,214 3,111 1384 1 ALLDOCUBE M8 2,730 7,274 882 5 ALLDOCUBE T701 1,092 4,554 1437 1 ALLDOCUBE U1006H 1,902 4,931 1125 1 ALLVIEW P7_PRO 1,691 4,543 1210 1 ALLVIEW X4_Soul 2,536 6,938 925 1 Alps Acer One 8 T4-82L 2,539 6,526 924 1 Alps Tablet18T 1,201 3,043 1394 1 Alps tb8788p1_64_bsp 2,343 5,784 983 2 Amazon KFKAWI 712 1,701 1589 4 Amazon KFMAWI 2,306 5,640 992 19 Amazon KFONWI 1,082 2,588 1442 3 Amlogic A95X-A3 1,228 3,182 1381 1 Amlogic ABOX A4 397 -
GMAI: a GPU Memory Allocation Inspection Tool for Understanding and Exploiting the Internals of GPU Resource Allocation in Critical Systems
The final publication is available at ACM via http://dx.doi.org/ 10.1145/3391896 GMAI: A GPU Memory Allocation Inspection Tool for Understanding and Exploiting the Internals of GPU Resource Allocation in Critical Systems ALEJANDRO J. CALDERÓN, Universitat Politècnica de Catalunya & Ikerlan Technology Research Centre LEONIDAS KOSMIDIS, Barcelona Supercomputing Center (BSC) CARLOS F. NICOLÁS, Ikerlan Technology Research Centre FRANCISCO J. CAZORLA, Barcelona Supercomputing Center (BSC) PEIO ONAINDIA, Ikerlan Technology Research Centre Critical real-time systems require strict resource provisioning in terms of memory and timing. The constant need for higher performance in these systems has led industry to recently include GPUs. However, GPU software ecosystems are by their nature closed source, forcing system engineers to consider them as black boxes, complicating resource provisioning. In this work we reverse engineer the internal operations of the GPU system software to increase the understanding of their observed behaviour and how resources are internally managed. We present our methodology which is incorporated in GMAI (GPU Memory Allocation Inspector), a tool which allows system engineers to accurately determine the exact amount of resources required by their critical systems, avoiding underprovisioning. We first apply our methodology on a wide range of GPU hardware from different vendors showing itsgeneralityin obtaining the properties of the GPU memory allocators. Next, we demonstrate the benefits of such knowledge in resource provisioning of two case studies from the automotive domain, where the actual memory consumption is up to 5.6× more than the memory requested by the application. ACM Reference Format: Alejandro J. Calderón, Leonidas Kosmidis, Carlos F. Nicolás, Francisco J. -
Security Monitor for Mobile Devices: Design and Applications
UNIVERSITY OF CALIFORNIA, IRVINE Security Monitor for Mobile Devices: Design and Applications DISSERTATION submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Computer Science by Saeed Mirzamohammadi Dissertation Committee: Professor Ardalan Amiri Sani, UCI, Chair Professor Gene Tsudik, UCI Professor Sharad Mehrotra, UCI Doctor Sharad Agarwal, MSR 2020 Portion of Chapter 1 c 2018 ACM, reprinted, with permission, from [148] Portion of Chapter 1 c 2017 ACM, reprinted, with permission, from [150] Portion of Chapter 1 c 2018 IEEE, reprinted, with permission, from [149] Portion of Chapter 1 c 2020 ACM, reprinted, with permission, from [151] Portion of Chapter 2 c 2018 ACM, reprinted, with permission, from [148] Portion of Chapter 2 c 2017 ACM, reprinted, with permission, from [150] Portion of Chapter 3 c 2018 ACM, reprinted, with permission, from [148] Portion of Chapter 4 c 2018 IEEE, reprinted, with permission, from [149] Portion of Chapter 5 c 2017 ACM, reprinted, with permission, from [150] Portion of Chapter 6 c 2020 ACM, reprinted, with permission, from [151] Portion of Chapter 7 c 2020 ACM, reprinted, with permission, from [151] Portion of Chapter 7 c 2017 ACM, reprinted, with permission, from [150] Portion of Chapter 7 c 2018 IEEE, reprinted, with permission, from [149] Portion of Chapter 7 c 2018 ACM, reprinted, with permission, from [148] All other materials c 2020 Saeed Mirzamohammadi TABLE OF CONTENTS Page LIST OF FIGURES v LIST OF TABLES vii ACKNOWLEDGMENTS viii VITA ix ABSTRACT OF THE DISSERTATION xi 1 Introduction 1 1.1 Applications of the security monitor .