EEMBC and the Purposes of Embedded Processor Benchmarking Markus Levy, President
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Cloud Workbench a Web-Based Framework for Benchmarking Cloud Services
Bachelor August 12, 2014 Cloud WorkBench A Web-Based Framework for Benchmarking Cloud Services Joel Scheuner of Muensterlingen, Switzerland (10-741-494) supervised by Prof. Dr. Harald C. Gall Philipp Leitner, Jürgen Cito software evolution & architecture lab Bachelor Cloud WorkBench A Web-Based Framework for Benchmarking Cloud Services Joel Scheuner software evolution & architecture lab Bachelor Author: Joel Scheuner, [email protected] Project period: 04.03.2014 - 14.08.2014 Software Evolution & Architecture Lab Department of Informatics, University of Zurich Acknowledgements This bachelor thesis constitutes the last milestone on the way to my first academic graduation. I would like to thank all the people who accompanied and supported me in the last four years of study and work in industry paving the way to this thesis. My personal thanks go to my parents supporting me in many ways. The decision to choose a complex topic in an area where I had no personal experience in advance, neither theoretically nor technologically, made the past four and a half months challenging, demanding, work-intensive, but also very educational which is what remains in good memory afterwards. Regarding this thesis, I would like to offer special thanks to Philipp Leitner who assisted me during the whole process and gave valuable advices. Moreover, I want to thank Jürgen Cito for his mainly technologically-related recommendations, Rita Erne for her time spent with me on language review sessions, and Prof. Harald Gall for giving me the opportunity to write this thesis at the Software Evolution & Architecture Lab at the University of Zurich and providing fundings and infrastructure to realize this thesis. -
Imagination Technologies Group Plc Annual Report 2006
Imagination Technologies Group plc Annual Report 2006 The data and projections shown on pages 1-24 are the product of consolidated partner data, analyst information and Imagination Technologies research. Imagination Technologies, the Imagination Technologies logo, PowerVR, the PowerVR logo, Metagence, the Metagence logo, Ensigma, the Ensigma logo, PURE Digital, IMGworks, CodeScape, META, MTX, MBX, MBX Lite, SGX, UCC, MiniEngine, PocketDAB, ReVu, the Bug, the Bug logo, Legato, EVOKE-1, TEMPUS, CHRONOS, OASIS, PURE ONE, SONUS-1XT and Élan are trademarks or registered trademarks of Imagination Technologies Limited. All other logos, products, trademarks and registered trademarks are the property of their respective owners. Copyright © 2006 Imagination Technologies Limited, an Imagination Technologies Group plc company. 4 colour print + matt lamination on 1 side, 215x280mm (h) trim size with 4mm spin, 300gsm board fold, gather and perfect bind 19/06/2006 Contact Oscar Tse / IMG Publications / 01923 260511 / [email protected] Introduction Improvements in technology mean that more and more functions can be integrated onto just one silicon chip. Whilst in the past, half a dozen companies might each have made a specialised chip to go into an advanced product like a TV or mobile phone, now it is more likely that one large semiconductor company will make a single chip that contains most, if not all, of that specialised knowledge. That’s where silicon IP, or ‘intellectual property’ comes in. Semiconductor companies can licence parts of a chip design from companies that specialise in those advanced technologies, instead of developing the technology themselves. They are especially likely to do this in new market areas. -
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. -
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. -
Introduction to Digital Signal Processors
INTRODUCTION TO Accumulator architecture DIGITAL SIGNAL PROCESSORS Memory-register architecture Prof. Brian L. Evans in collaboration with Niranjan Damera-Venkata and Magesh Valliappan Load-store architecture Embedded Signal Processing Laboratory The University of Texas at Austin Austin, TX 78712-1084 http://anchovy.ece.utexas.edu/ Outline n Signal processing applications n Conventional DSP architecture n Pipelining in DSP processors n RISC vs. DSP processor architectures n TI TMS320C6x VLIW DSP architecture n Signal and image processing applications n Signal processing on general-purpose processors n Conclusion 2 Signal Processing Applications n Low-cost embedded systems 4 Modems, cellular telephones, disk drives, printers n High-throughput applications 4 Halftoning, radar, high-resolution sonar, tomography n PC based multimedia 4 Compression/decompression of audio, graphics, video n Embedded processor requirements 4 Inexpensive with small area and volume 4 Deterministic interrupt service routine latency 4 Low power: ~50 mW (TMS320C5402/20: 0.32 mA/MIP) 3 Conventional DSP Architecture n High data throughput 4 Harvard architecture n Separate data memory/bus and program memory/bus n Three reads and one or two writes per instruction cycle 4 Short deterministic interrupt service routine latency 4 Multiply-accumulate (MAC) in a single instruction cycle 4 Special addressing modes supported in hardware n Modulo addressing for circular buffers (e.g. FIR filters) n Bit-reversed addressing (e.g. fast Fourier transforms) 4Instructions to keep the -
BOOM): an Industry- Competitive, Synthesizable, Parameterized RISC-V Processor
The Berkeley Out-of-Order Machine (BOOM): An Industry- Competitive, Synthesizable, Parameterized RISC-V Processor Christopher Celio David A. Patterson Krste Asanović Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2015-167 http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-167.html June 13, 2015 Copyright © 2015, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. The Berkeley Out-of-Order Machine (BOOM): An Industry-Competitive, Synthesizable, Parameterized RISC-V Processor Christopher Celio, David Patterson, and Krste Asanovic´ University of California, Berkeley, California 94720–1770 [email protected] BOOM is a work-in-progress. Results shown are prelimi- nary and subject to change as of 2015 June. I$ L1 D$ (32k) L2 data 1. The Berkeley Out-of-Order Machine BOOM is a synthesizable, parameterized, superscalar out- exe of-order RISC-V core designed to serve as the prototypical baseline processor for future micro-architectural studies of uncore regfile out-of-order processors. Our goal is to provide a readable, issue open-source implementation for use in education, research, exe and industry. uncore BOOM is written in roughly 9,000 lines of the hardware L2 data (256k) construction language Chisel. -
A Free, Commercially Representative Embedded Benchmark Suite
MiBench: A free, commercially representative embedded benchmark suite Matthew R. Guthaus, Jeffrey S. Ringenberg, Dan Ernst, Todd M. Austin, Trevor Mudge, Richard B. Brown {mguthaus,jringenb,ernstd,taustin,tnm,brown}@eecs.umich.edu The University of Michigan Electrical Engineering and Computer Science 1301 Beal Ave., Ann Arbor, MI 48109-2122 Abstract Among the common features of these This paper examines a set of commercially microarchitectures are deep pipelines, significant representative embedded programs and compares them instruction level parallelism, sophisticated branch to an existing benchmark suite, SPEC2000. A new prediction schemes, and large caches. version of SimpleScalar that has been adapted to the Although this class of machines has been the chief ARM instruction set is used to characterize the focus of the computer architecture community, performance of the benchmarks using configurations relatively few microprocessors are employed in this similar to current and next generation embedded market segment. The vast majority of microprocessors processors. Several characteristics distinguish the are employed in embedded applications [8]. Although representative embedded programs from the existing many are just inexpensive microcontrollers, their SPEC benchmarks including instruction distribution, combined sales account for nearly half of all memory behavior, and available parallelism. The microprocessor revenue. Furthermore, the embedded embedded benchmarks, called MiBench, are freely application domain is the fastest growing market available to all researchers. segment in the microprocessor industry. The wide range of applications makes it difficult to 1. Introduction characterize the embedded domain. In fact, an Performance based design has made benchmarking embedded benchmark suite should reflect this by a critical part of the design process [1]. -
Power Aware Tactical Computing
Power Aware Tactical Computing Song J Park1, Dale R Shires1, Brian J Henz1, James A Ross2, David A Richie3, and Jordan J Ruloff2 1U.S. Army Research Laboratory, APG, MD 2Dynamic Research Corp., Andover, MA 3Brown Deer Technology, Forest Hill, MD Abstract - Power consumption has become a chief supports and maintains high performance computing (HPC) impediment in the advancement of digital computing. To resources. The ARL DSRC provides state-of-the-art improve performance amid power limitations, accelerators computational solutions for the DoD research and are being applied to everyday systems. In particular, due to development community. Among the systems available at the mix of popularity and raw computational power, ARL is Harold system, which consists of 10,752 cores with graphics processing units (GPUs) have extended the the processing capability of 120 trillion floating-point applicability of digital computing in a multitude of sectors operations per second (TFLOPS). A predecessor, JVN from ubiquitous smartphones to environmentally responsible system, decommissioned in 2009, had 2048 cores with the supercomputing. Operating within the mobile power theoretical peak of 14.7 TFLOPS. Shifting the focus to constraint, the usefulness of a high performance graphics single-precision, the peak processing power for the Harold processor system in a tactical environment is explored in this system is 240 TFLOPS and 29.5 TFLOPS for the JVN study. A line-of-sight optimization algorithm serves as a system. Given that a single graphics card, Radeon HD 6990, compute-intensive application with characteristics relating is rated at 5.1 TFLOPS for peak single-precision arithmetic, to a tactical scenario. -
Low-Power High Performance Computing
Low-Power High Performance Computing Panagiotis Kritikakos August 16, 2011 MSc in High Performance Computing The University of Edinburgh Year of Presentation: 2011 Abstract The emerging development of computer systems to be used for HPC require a change in the architecture for processors. New design approaches and technologies need to be embraced by the HPC community for making a case for new approaches in system design for making it possible to be used for Exascale Supercomputers within the next two decades, as well as to reduce the CO2 emissions of supercomputers and scientific clusters, leading to greener computing. Power is listed as one of the most important issues and constraint for future Exascale systems. In this project we build a hybrid cluster, investigating, measuring and evaluating the performance of low-power CPUs, such as Intel Atom and ARM (Marvell 88F6281) against commodity Intel Xeon CPU that can be found within standard HPC and data-center clusters. Three main factors are considered: computational performance and efficiency, power efficiency and porting effort. Contents 1 Introduction 1 1.1 Report organisation . 2 2 Background 3 2.1 RISC versus CISC . 3 2.2 HPC Architectures . 4 2.2.1 System architectures . 4 2.2.2 Memory architectures . 5 2.3 Power issues in modern HPC systems . 9 2.4 Energy and application efficiency . 10 3 Literature review 12 3.1 Green500 . 12 3.2 Supercomputing in Small Spaces (SSS) . 12 3.3 The AppleTV Cluster . 13 3.4 Sony Playstation 3 Cluster . 13 3.5 Microsoft XBox Cluster . 14 3.6 IBM BlueGene/Q . -
Exploring Coremark™ – a Benchmark Maximizing Simplicity and Efficacy by Shay Gal-On and Markus Levy
Exploring CoreMark™ – A Benchmark Maximizing Simplicity and Efficacy By Shay Gal-On and Markus Levy There have been many attempts to provide a single number that can totally quantify the ability of a CPU. Be it MHz, MOPS, MFLOPS - all are simple to derive but misleading when looking at actual performance potential. Dhrystone was the first attempt to tie a performance indicator, namely DMIPS, to execution of real code - a good attempt, which has long served the industry, but is no longer meaningful. BogoMIPS attempts to measure how fast a CPU can “do nothing”, for what that is worth. The need still exists for a simple and standardized benchmark that provides meaningful information about the CPU core - introducing CoreMark, available for free download from www.coremark.org. CoreMark ties a performance indicator to execution of simple code, but rather than being entirely arbitrary and synthetic, the code for the benchmark uses basic data structures and algorithms that are common in practically any application. Furthermore, EEMBC carefully chose the CoreMark implementation such that all computations are driven by run-time provided values to prevent code elimination during compile time optimization. CoreMark also sets specific rules about how to run the code and report results, thereby eliminating inconsistencies. CoreMark Composition To appreciate the value of CoreMark, it’s worthwhile to dissect its composition, which in general is comprised of lists, strings, and arrays (matrixes to be exact). Lists commonly exercise pointers and are also characterized by non-serial memory access patterns. In terms of testing the core of a CPU, list processing predominantly tests how fast data can be used to scan through the list. -
CPU and FFT Benchmarks of ARM Processors
Proceedings of SAIP2014 Affordable and power efficient computing for high energy physics: CPU and FFT benchmarks of ARM processors Mitchell A Cox, Robert Reed and Bruce Mellado School of Physics, University of the Witwatersrand. 1 Jan Smuts Avenue, Braamfontein, Johannesburg, South Africa, 2000 E-mail: [email protected] Abstract. Projects such as the Large Hadron Collider at CERN generate enormous amounts of raw data which presents a serious computing challenge. After planned upgrades in 2022, the data output from the ATLAS Tile Calorimeter will increase by 200 times to over 40 Tb/s. ARM System on Chips are common in mobile devices due to their low cost, low energy consumption and high performance and may be an affordable alternative to standard x86 based servers where massive parallelism is required. High Performance Linpack and CoreMark benchmark applications are used to test ARM Cortex-A7, A9 and A15 System on Chips CPU performance while their power consumption is measured. In addition to synthetic benchmarking, the FFTW library is used to test the single precision Fast Fourier Transform (FFT) performance of the ARM processors and the results obtained are converted to theoretical data throughputs for a range of FFT lengths. These results can be used to assist with specifying ARM rather than x86-based compute farms for upgrades and upcoming scientific projects. 1. Introduction Projects such as the Large Hadron Collider (LHC) generate enormous amounts of raw data which presents a serious computing challenge. After planned upgrades in 2022, the data output from the ATLAS Tile Calorimeter will increase by 200 times to over 41 Tb/s (Terabits/s) [1]. -
IMG PPT Template
Imagination Technologies Group Overview May 2010 Company Overview . Leading Semiconductor IP Supplier . POWERVR™ graphics, video, display processing . ENSIGMA™ receivers and communications processors . META™ processors – SoC centric real-time, DSP, Linux . Licensees: Leading Semis and OEMs . #3 Silicon Design IP provider * . Innovative Consumer Product Manufacturer . PURE digital radio, internet connected audio . Established technology powerhouse . Founded:1985 . Listed:1994-London Stock Exchange:IMG . Employees: more than 650 worldwide * Source: Gartner IP Suppliers Report, March 2010 UK Headquarters R&D Sales 2.2 © Imagination Technologies Ltd. Group Revenues £m 65 60 PURE 55 Technology 50 45 40 35 30 25 20 15 10 5 0 04/05 05/06 06/07 07/08** 08/09 * 13 months to April ‘08 3 © Imagination Technologies Ltd. Everything, Everywhere The connected “four screens”: TV, PC, Mobile & In-Car The Cloud Mobile devices have to deliver the internet, PC capabilities, TV, music, navigation and more… 4 © Imagination Technologies Ltd. Our Technology Business SoC Technologies & Solutions Markets Mobile IMGworks Phone Customer’s 3rd Party IP Internal IP Customised IP Multimedia ENSIGMA META POWERVR Communications Hand-held Processor/DSP Graphics Multimedia POWERVR Display META POWERVR Audio Video SoC Home Consumer & Entertainment Display Audio Mobile Computing OpenGL MeOS Radio OpenGL-ES De-interlace Nucleus TV OpenVG Linux Frame rate In-Car Mobile TV DirectX conversion Android Electronics WiFi etc Audio Bluetooth Noise SW Stacks reduction etc MPEG1/2/4 etc H264 etc Emerging VC1 Markets etc 5 © Imagination Technologies Ltd. The Virtuous Technology Cycle… Semiconductor Partners Creates SoCs & Sells Chips SUNPLUS Makes products Creates & & sells them to Licenses IP consumers… OEMs Emerging trend with OEM partners Consumers demand more, better, cheaper…everywhere Consumers …which drives demand for more advanced & efficient IP … 6 © Imagination Technologies Ltd.