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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, -
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 ......................................................................................................................................................................... -
Automatic Benchmark Profiling Through Advanced Trace Analysis Alexis Martin, Vania Marangozova-Martin
Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin, Vania Marangozova-Martin To cite this version: Alexis Martin, Vania Marangozova-Martin. Automatic Benchmark Profiling through Advanced Trace Analysis. [Research Report] RR-8889, Inria - Research Centre Grenoble – Rhône-Alpes; Université Grenoble Alpes; CNRS. 2016. hal-01292618 HAL Id: hal-01292618 https://hal.inria.fr/hal-01292618 Submitted on 24 Mar 2016 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. Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin , Vania Marangozova-Martin RESEARCH REPORT N° 8889 March 23, 2016 Project-Team Polaris ISSN 0249-6399 ISRN INRIA/RR--8889--FR+ENG Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin ∗ † ‡, Vania Marangozova-Martin ∗ † ‡ Project-Team Polaris Research Report n° 8889 — March 23, 2016 — 15 pages Abstract: Benchmarking has proven to be crucial for the investigation of the behavior and performances of a system. However, the choice of relevant benchmarks still remains a challenge. To help the process of comparing and choosing among benchmarks, we propose a solution for automatic benchmark profiling. It computes unified benchmark profiles reflecting benchmarks’ duration, function repartition, stability, CPU efficiency, parallelization and memory usage. -
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 -
Automated Performance Testing for Virtualization with Mmtests
Automated Performance Testing For Virtualization with MMTests Dario Faggioli <[email protected]> Software Engineer - Virtualization Specialist, SUSE GPG: 4B9B 2C3A 3DD5 86BD 163E 738B 1642 7889 A5B8 73EE https://about.me/dario.faggioli https://www.linkedin.com/in/dfaggioli/ https://twitter.com/DarioFaggioli (@DarioFaggioli) Testing / Benchmarking / CI Tools & Suites • OpenQA • Jenkins • Kernel CI • Autotest / Avocado-framework / Avocado-vt • Phoronix Test Suite • Fuego • Linux Test Project • Xen-Project’s OSSTests • … • ... SRSLY THINKING I’ll TALK ABOUT & SUGGEST USING ANOTHER ONE ? REALLY ? Benchmarking on Baremetal What’s the performance impact of kernel code change “X” ? Baremetal Baremetal Kernel Kernel (no X) VS. (with X) CPU MEM CPU MEM bench bench bench bench I/O I/O bench bench Benchmarking in Virtualization What’s the performance impact of kernel code change “X” ? Baremetal Baremetal Baremetal Baremetal Kernel Kernel Kernel (no X) Kernel (no X) (with X) (with X) VM VS. VM VS. VM VS. VM Kernel Kernel Kernel (no X) Kernel (no X) (with X) (with X) CPU MEM CPU MEM CPU MEM CPU MEM bench bench bench bench bench bench bench bench I/O I/O I/O I/O bench bench bench bench Benchmarking in Virtualization What’s the performance impact of kernel code change “X” ? Baremetal Baremetal Baremetal Baremetal Kernel Kernel Kernel (no X) Kernel (no X) (with X) (with X) VM VS. VM VS. VM VS. VM Kernel Kernel Kernel (no X) Kernel (no X) (with X) (with X) We want to run the benchmarks inside VMs CPU MEM CPU MEM CPU MEM CPU MEM bench bench bench bench bench bench bench bench I/O I/O I/O I/O bench bench bench bench Benchmarking in Virtualization What’s the performance impact of kernel code change “X” ? Baremetal Baremetal Baremetal Baremetal Kernel Kernel Kernel (no X) Kernel (no X) (with X) (with X) VM VS. -
Zhodnocení Použitelnosti Raspberry Pi Pro Distribuované Výpočty
Univerzita Karlova v Praze Matematicko-fyzikální fakulta BAKALÁŘSKÁ PRÁCE Petr Stefan Zhodnocení použitelnosti Raspberry Pi pro distribuované výpoèty Katedra softwarového inženýrství Vedoucí bakaláøské práce: RNDr. Martin Kruli¹, Ph.D. Studijní program: Informatika Studijní obor: Obecná informatika Praha 2015 Prohla¹uji, že jsem tuto bakaláøskou práci vypracoval(a) samostatně a výhradně s použitím citovaných pramenù, literatury a dalších odborných zdrojù. Beru na vědomí, že se na moji práci vztahují práva a povinnosti vyplývající ze zákona è. 121/2000 Sb., autorského zákona v platném znění, zejména skuteènost, že Univerzita Karlova v Praze má právo na uzavření licenční smlouvy o užití této práce jako ¹kolního díla podle x60 odst. 1 autorského zákona. V Praze dne 29. èervence 2015 Petr Stefan i ii Název práce: Zhodnocení použitelnosti Raspberry Pi pro distribuované výpoèty Autor: Petr Stefan Katedra: Katedra softwarového inženýrství Vedoucí bakaláøské práce: RNDr. Martin Kruli¹, Ph.D., Katedra softwarového inženýrství Abstrakt: Cílem práce bylo zhodnotit výkonnostní vlastnosti mikropočítače Rasp- berry Pi, a to především z hlediska možnosti sestavení výpoèetního klastru z více těchto jednotek. Součástí práce bylo vytvoření sady testù pro měření výpočetního výkonu procesoru, propustnosti operační paměti, rychlosti zápisu a čtení z per- sistentního úložiště (paměťové karty) a propustnosti síťového rozhraní Ethernet. Na základě porovnání výsledků získaných měřením na Raspberry Pi a na vhod- ném reprezentativním vzorku dalších běžně dostupných počítačů bylo provedeno doporučení, zda je konstrukce Raspberry Pi klastru opodstatněná. Tyto výsledky rovněž poskytly přibližné ekonomické a výkonnostní projekce takového řešení. Klíčová slova: Raspberry Pi výkon distribuovaný výpoèet benchmark Title: Assessing Usability of Raspberry Pi for Distributed Computing Author: Petr Stefan Department: Department of Software Engineering Supervisor: RNDr. -
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 -
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 . -
Cubieboard Cubieboard2 Cubietruck Beaglebone Black
Raspberry Pi (Model B rev.2) Cubieboard Cubieboard2 Cubietruck Beaglebone Black 1 Ghz (OC) ARM® Cortex-A6 1 Ghz ARM® Cortex-A8 1 Ghz ARM® Cortex-A7 Dual Core 1 Ghz ARM® Cortex-A7 Dual Core 1 Ghz ARM® Cortex-A8 CPU ARM1176JZF-F Allwinner A10 C8096CA Allwinner A20 Allwinner A20 AM335x GPU/FPU VideoCore IV Mali-400 (CedarX, OpenGL) Mali-400MP2 (CedarX, OpenGL) Mali-400MP2 (CedarX, OpenGL) SGX350 3D / NEON FPU accelerator RAM 512 MB 1 GB DDR3 2 GB 2 GB 512 MB DDR3 Storage micro SD/SDHC 4 GB NAND Flash, micro SD/SDHC, SATA 4 GB NAND Flash, micro SD/SDHC, SATA 4 GB NAND Flash, micro SD/SDHC, SATA 2.0 2GB eMMC Power micro USB (5V/1A) 3.5 W DC 5v/2A DC 5v/2A DC 5v/2.5A DC 5V/500mA Video RCA Composite Video, HDMI 1.4 HDMI HDMI HDMI/VGA microHDMI Audio 3.5 mm Headphone Jack 3.5 mm Headphone Jack / Line In 3.5 mm Headphone Jack 3.5 mm Headphone Jack, SPDIF Network 10/100 Mbps 10/100 Mbps 10/100 Mbps 10/100/1000 Mbps, Wifi, Bluetooth 10/100 Mbps 2x46 PIN GPIO I/O ports 26 PIN GPIO, 2x Ribon 2x48 PIN GPIO, 4PIN Serial, 1IR 2x48 PIN GPIO, 4PIN Serial, 1IR 1x 54 PIN GPIO (Arduino Shield Compatible) USB ports 2x USB 2.0 2x USB 2.0 2x USB 2.0, 1 mini USB OTG 2x USB 2.0, 1 mini USB OTG 1x USB 2.0 Linux (Raspbian, Debian, Fedora, Arch, Gentoo, Kali), Andoid, Angstrom, Ubuntu, Fedora, Gentoo. -
Automatic Benchmark Profiling Through Advanced Trace Analysis
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Hal - Université Grenoble Alpes Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin, Vania Marangozova-Martin To cite this version: Alexis Martin, Vania Marangozova-Martin. Automatic Benchmark Profiling through Advanced Trace Analysis. [Research Report] RR-8889, Inria - Research Centre Grenoble { Rh^one-Alpes; Universit´eGrenoble Alpes; CNRS. 2016. <hal-01292618> HAL Id: hal-01292618 https://hal.inria.fr/hal-01292618 Submitted on 24 Mar 2016 HAL is a multi-disciplinary open access L'archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destin´eeau d´ep^otet `ala diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publi´esou non, lished or not. The documents may come from ´emanant des ´etablissements d'enseignement et de teaching and research institutions in France or recherche fran¸caisou ´etrangers,des laboratoires abroad, or from public or private research centers. publics ou priv´es. Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin , Vania Marangozova-Martin RESEARCH REPORT N° 8889 March 23, 2016 Project-Team Polaris ISSN 0249-6399 ISRN INRIA/RR--8889--FR+ENG Automatic Benchmark Profiling through Advanced Trace Analysis Alexis Martin ∗ † ‡, Vania Marangozova-Martin ∗ † ‡ Project-Team Polaris Research Report n° 8889 — March 23, 2016 — 15 pages Abstract: Benchmarking has proven to be crucial for the investigation of the behavior and performances of a system. However, the choice of relevant benchmarks still remains a challenge. To help the process of comparing and choosing among benchmarks, we propose a solution for automatic benchmark profiling. -
Passmark Android Benchmark Charts - CPU Rating
PassMark Android Benchmark Charts - CPU Rating http://www.androidbenchmark.net/cpumark_chart.html Home Software Hardware Benchmarks Services Store Support Forums About Us Home » Android Benchmarks » Device Charts CPU Benchmarks Video Card Benchmarks Hard Drive Benchmarks RAM PC Systems Android iOS / iPhone Android TM Benchmarks ----Select A Page ---- Performance Comparison of Android Devices Android Devices - CPUMark Rating How does your device compare? Add your device to our benchmark chart This chart compares the CPUMark Rating made using PerformanceTest Mobile benchmark with PerformanceTest Mobile ! results and is updated daily. Submitted baselines ratings are averaged to determine the CPU rating seen on the charts. This chart shows the CPUMark for various phones, smartphones and other Android devices. The higher the rating the better the performance. Find out which Android device is best for your hand held needs! Android CPU Mark Rating Updated 14th of July 2016 Samsung SM-N920V 166,976 Samsung SM-N920P 166,588 Samsung SM-G890A 166,237 Samsung SM-G928V 164,894 Samsung Galaxy S6 Edge (Various Models) 164,146 Samsung SM-G930F 162,994 Samsung SM-N920T 162,504 Lemobile Le X620 159,530 Samsung SM-N920W8 159,160 Samsung SM-G930T 157,472 Samsung SM-G930V 157,097 Samsung SM-G935P 156,823 Samsung SM-G930A 155,820 Samsung SM-G935F 153,636 Samsung SM-G935T 152,845 Xiaomi MI 5 150,923 LG H850 150,642 Samsung Galaxy S6 (Various Models) 150,316 Samsung SM-G935A 147,826 Samsung SM-G891A 145,095 HTC HTC_M10h 144,729 Samsung SM-G928F 144,576 Samsung -
Xeon Platinum 8280 Vs. AMD EPYC 7742 - Ubuntu Linux
www.phoronix-test-suite.com Xeon Platinum 8280 vs. AMD EPYC 7742 - Ubuntu Linux AMD EPYC and Intel Xeon benchmarks by Michael Larabel... Mix of single and multi-threaded workloads. Admittedly somewhat random tests with the intent of just being to doing some validation on Phoronix Test Suite 9.4 and checking out the graphing code, PDF generation, etc. Take the actual results as you wish. Automated Executive Summary 2 x EPYC 7742 had the most wins, coming in first place for 61% of the tests. Based on the geometric mean of all complete results, the fastest (2 x EPYC 7742) was 1.2x the speed of the slowest (2 x Xeon Platinum 8280). The results with the greatest spread from best to worst included: Tungsten Renderer (Scene: Volumetric Caustic) at 3.53x - MBW (Test: Memory Copy - Array Size: 4096 MiB) at 3.22x - Parboil (Test: OpenMP MRI Gridding) at 3.02x - Hackbench (Count: 32 - Type: Process) at 3.01x - Tachyon (Total Time) at 2.62x - C-Ray (Total Time - 4K, 16 Rays Per Pixel) at 2.37x - dav1d (Video Input: Chimera 1080p) at 2.33x Xeon Platinum 8280 vs. AMD EPYC 7742 - Ubuntu Linux - Coremark (CoreMark Size 666 - Iterations Per Second) at 2.3x - dav1d (Video Input: Summer Nature 1080p) at 2.17x - GraphicsMagick (Operation: Sharpen) at 2.17x. Test Systems: 2 x Xeon Platinum 8280 Processor: 2 x Intel Xeon Platinum 8280 @ 4.00GHz (56 Cores / 112 Threads), Motherboard: GIGABYTE MD61-SC2-00 v01000100 (T15 BIOS), Chipset: Intel Sky Lake-E DMI3 Registers, Memory: 12 x 32 GB DDR4-2933MT/s HMA84GR7CJR4N-WM, Disk: 280GB INTEL SSDPED1D280GA, Graphics: