CPU Alternatives for Future High-Performance Systems
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Petaflops for the People
PETAFLOPS SPOTLIGHT: NERSC housands of researchers have used facilities of the Advanced T Scientific Computing Research (ASCR) program and its EXTREME-WEATHER Department of Energy (DOE) computing predecessors over the past four decades. Their studies of hurricanes, earthquakes, NUMBER-CRUNCHING green-energy technologies and many other basic and applied Certain problems lend themselves to solution by science problems have, in turn, benefited millions of people. computers. Take hurricanes, for instance: They’re They owe it mainly to the capacity provided by the National too big, too dangerous and perhaps too expensive Energy Research Scientific Computing Center (NERSC), the Oak to understand fully without a supercomputer. Ridge Leadership Computing Facility (OLCF) and the Argonne Leadership Computing Facility (ALCF). Using decades of global climate data in a grid comprised of 25-kilometer squares, researchers in These ASCR installations have helped train the advanced Berkeley Lab’s Computational Research Division scientific workforce of the future. Postdoctoral scientists, captured the formation of hurricanes and typhoons graduate students and early-career researchers have worked and the extreme waves that they generate. Those there, learning to configure the world’s most sophisticated same models, when run at resolutions of about supercomputers for their own various and wide-ranging projects. 100 kilometers, missed the tropical cyclones and Cutting-edge supercomputing, once the purview of a small resulting waves, up to 30 meters high. group of experts, has trickled down to the benefit of thousands of investigators in the broader scientific community. Their findings, published inGeophysical Research Letters, demonstrated the importance of running Today, NERSC, at Lawrence Berkeley National Laboratory; climate models at higher resolution. -
A 2000 Frames / S Programmable Binary Image Processor Chip for Real Time Machine Vision Applications
A 2000 frames / s programmable binary image processor chip for real time machine vision applications A. Loos, D. Fey Institute of Computer Science, Friedrich-Schiller-University Jena Ernst-Abbe-Platz 2, D-07743 Jena, Germany {loos,fey}@cs.uni-jena.de Abstract the inflexible and fixed instruction set. To meet that we present a so called ASIP (application specific instruction Industrial manufacturing today requires both an efficient set processor) which combines the flexibility of a GPP production process and an appropriate quality standard of (General Purpose Processor) with the speed of an ASIC. each produced unit. The number of industrial vision appli- cations, where real time vision systems are utilized, is con- tinuously rising due to the increasing automation. Assem- Embedded Vision System bly lines, where component parts are manipulated by robot 1. real scene image sensing, AD- CMOS-Imager grippers, require a fast and fault tolerant visual detection conversion and read out of objects. Standard computation hardware like PC-based 2. grey scale image image segmentation platforms with frame grabber boards are often not appro- representation priate for such hard real time vision tasks in embedded sys- 3. raw binary image image enhancement, tems. This is because they meet their limits at frame rates of representation removal of disturbance a few hundreds images per second and show comparatively ASIP FPGA long latency times of a few milliseconds. This is the result 4. improved binary image calculation of of the largely serial working and time consuming process- projections ing chain of these systems. In contrast to that we designed 5. -
United Health Group Capacity Analysis
Advanced Technical Skills (ATS) North America zPCR Capacity Sizing Lab SHARE Sessions 7774 and 7785 August 4, 2010 John Burg Brad Snyder Materials created by John Fitch and Jim Shaw IBM 1 © 2010 IBM Corporation Advanced Technical Skills Trademarks The following are trademarks of the International Business Machines Corporation in the United States and/or other countries. AlphaBlox* GDPS* RACF* Tivoli* APPN* HiperSockets Redbooks* Tivoli Storage Manager CICS* HyperSwap Resource Link TotalStorage* CICS/VSE* IBM* RETAIN* VSE/ESA Cool Blue IBM eServer REXX VTAM* DB2* IBM logo* RMF WebSphere* DFSMS IMS S/390* xSeries* DFSMShsm Language Environment* Scalable Architecture for Financial Reporting z9* DFSMSrmm Lotus* Sysplex Timer* z10 DirMaint Large System Performance Reference™ (LSPR™) Systems Director Active Energy Manager z10 BC DRDA* Multiprise* System/370 z10 EC DS6000 MVS System p* z/Architecture* DS8000 OMEGAMON* System Storage zEnterprise ECKD Parallel Sysplex* System x* z/OS* ESCON* Performance Toolkit for VM System z z/VM* FICON* PowerPC* System z9* z/VSE FlashCopy* PR/SM System z10 zSeries* * Registered trademarks of IBM Corporation Processor Resource/Systems Manager The following are trademarks or registered trademarks of other companies. Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. Cell Broadband Engine is a trademark of Sony Computer Entertainment, Inc. in the United States, other countries, or both and is used under license therefrom. Java and all Java-based trademarks are trademarks of Sun Microsystems, Inc. in the United States, other countries, or both. Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft Corporation in the United States, other countries, or both. -
Unlocking the Full Potential of the Cray XK7 Accelerator
Unlocking the Full Potential of the Cray XK7 Accelerator ∗ † Mark D. Klein , and, John E. Stone ∗National Center for Supercomputing Application, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA †Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA Abstract—The Cray XK7 includes NVIDIA GPUs for ac- [2], [3], [4], and for high fidelity movie renderings with the celeration of computing workloads, but the standard XK7 HVR volume rendering software.2 In one example, the total system software inhibits the GPUs from accelerating OpenGL turnaround time for an HVR movie rendering of a trillion- and related graphics-specific functions. We have changed the operating mode of the XK7 GPU firmware, developed a custom cell inertial confinement fusion simulation [5] was reduced X11 stack, and worked with Cray to acquire an alternate driver from an estimate of over a month for data transfer to and package from NVIDIA in order to allow users to render and rendering on a conventional visualization cluster down to post-process their data directly on Blue Waters. Users are able just one day when rendered locally using 128 XK7 nodes to use NVIDIA’s hardware OpenGL implementation which has on Blue Waters. The fully-graphics-enabled GPU state is many features not available in software rasterizers. By elim- inating the transfer of data to external visualization clusters, currently considered an unsupported mode of operation by time-to-solution for users has been improved tremendously. In Cray, and to our knowledge Blue Waters is presently the one case, XK7 OpenGL rendering has cut turnaround time only Cray system currently running in this mode. -
Design and Architectures for Signal and Image Processing
EURASIP Journal on Embedded Systems Design and Architectures for Signal and Image Processing Guest Editors: Markus Rupp, Dragomir Milojevic, and Guy Gogniat Design and Architectures for Signal and Image Processing EURASIP Journal on Embedded Systems Design and Architectures for Signal and Image Processing Guest Editors: Markus Rupp, Dragomir Milojevic, and Guy Gogniat Copyright © 2008 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in volume 2008 of “EURASIP Journal on Embedded Systems.” All articles are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editor-in-Chief Zoran Salcic, University of Auckland, New Zealand Associate Editors Sandro Bartolini, Italy Thomas Kaiser, Germany S. Ramesh, India Neil Bergmann, Australia Bart Kienhuis, The Netherlands Partha S. Roop, New Zealand Shuvra Bhattacharyya, USA Chong-Min Kyung, Korea Markus Rupp, Austria Ed Brinksma, The Netherlands Miriam Leeser, USA Asim Smailagic, USA Paul Caspi, France John McAllister, UK Leonel Sousa, Portugal Liang-Gee Chen, Taiwan Koji Nakano, Japan Jarmo Henrik Takala, Finland Dietmar Dietrich, Austria Antonio Nunez, Spain Jean-Pierre Talpin, France Stephen A. Edwards, USA Sri Parameswaran, Australia Jurgen¨ Teich, Germany Alain Girault, France Zebo Peng, Sweden Dongsheng Wang, China Rajesh K. Gupta, USA Marco Platzner, Germany Susumu Horiguchi, Japan Marc Pouzet, France Contents -
This Is Your Presentation Title
Introduction to GPU/Parallel Computing Ioannis E. Venetis University of Patras 1 Introduction to GPU/Parallel Computing www.prace-ri.eu Introduction to High Performance Systems 2 Introduction to GPU/Parallel Computing www.prace-ri.eu Wait, what? Aren’t we here to talk about GPUs? And how to program them with CUDA? Yes, but we need to understand their place and their purpose in modern High Performance Systems This will make it clear when it is beneficial to use them 3 Introduction to GPU/Parallel Computing www.prace-ri.eu Top 500 (June 2017) CPU Accel. Rmax Rpeak Power Rank Site System Cores Cores (TFlop/s) (TFlop/s) (kW) National Sunway TaihuLight - Sunway MPP, Supercomputing Center Sunway SW26010 260C 1.45GHz, 1 10.649.600 - 93.014,6 125.435,9 15.371 in Wuxi Sunway China NRCPC National Super Tianhe-2 (MilkyWay-2) - TH-IVB-FEP Computer Center in Cluster, Intel Xeon E5-2692 12C 2 Guangzhou 2.200GHz, TH Express-2, Intel Xeon 3.120.000 2.736.000 33.862,7 54.902,4 17.808 China Phi 31S1P NUDT Swiss National Piz Daint - Cray XC50, Xeon E5- Supercomputing Centre 2690v3 12C 2.6GHz, Aries interconnect 3 361.760 297.920 19.590,0 25.326,3 2.272 (CSCS) , NVIDIA Tesla P100 Cray Inc. DOE/SC/Oak Ridge Titan - Cray XK7 , Opteron 6274 16C National Laboratory 2.200GHz, Cray Gemini interconnect, 4 560.640 261.632 17.590,0 27.112,5 8.209 United States NVIDIA K20x Cray Inc. DOE/NNSA/LLNL Sequoia - BlueGene/Q, Power BQC 5 United States 16C 1.60 GHz, Custom 1.572.864 - 17.173,2 20.132,7 7.890 4 Introduction to GPU/ParallelIBM Computing www.prace-ri.eu How do -
Hardware Complexity and Software Challenges
Trends in HPC: hardware complexity and software challenges Mike Giles Oxford University Mathematical Institute Oxford e-Research Centre ACCU talk, Oxford June 30, 2015 Mike Giles (Oxford) HPC Trends June 30, 2015 1 / 29 1 { 10? 10 { 100? 100 { 1000? Answer: 4 cores in Intel Core i7 CPU + 384 cores in NVIDIA K1100M GPU! Peak power consumption: 45W for CPU + 45W for GPU Question ?! How many cores in my Dell M3800 laptop? Mike Giles (Oxford) HPC Trends June 30, 2015 2 / 29 Answer: 4 cores in Intel Core i7 CPU + 384 cores in NVIDIA K1100M GPU! Peak power consumption: 45W for CPU + 45W for GPU Question ?! How many cores in my Dell M3800 laptop? 1 { 10? 10 { 100? 100 { 1000? Mike Giles (Oxford) HPC Trends June 30, 2015 2 / 29 Question ?! How many cores in my Dell M3800 laptop? 1 { 10? 10 { 100? 100 { 1000? Answer: 4 cores in Intel Core i7 CPU + 384 cores in NVIDIA K1100M GPU! Peak power consumption: 45W for CPU + 45W for GPU Mike Giles (Oxford) HPC Trends June 30, 2015 2 / 29 Top500 supercomputers Really impressive { 300× more capability in 10 years! Mike Giles (Oxford) HPC Trends June 30, 2015 3 / 29 My personal experience 1982: CDC Cyber 205 (NASA Langley) 1985{89: Alliant FX/8 (MIT) 1989{92: Stellar (MIT) 1990: Thinking Machines CM5 (UTRC) 1987{92: Cray X-MP/Y-MP (NASA Ames, Rolls-Royce) 1993{97: IBM SP2 (Oxford) 1998{2002: SGI Origin (Oxford) 2002 { today: various x86 clusters (Oxford) 2007 { today: various GPU systems/clusters (Oxford) 2011{15: GPU cluster (Emerald @ RAL) 2008 { today: Cray XE6/XC30 (HECToR/ARCHER @ EPCC) 2013 { today: Cray XK7 with GPUs (Titan @ ORNL) Mike Giles (Oxford) HPC Trends June 30, 2015 4 / 29 Top500 supercomputers Power requirements are raising the question of whether this rate of improvement is sustainable. -
A SIMD Microprocessor for Image Processing
A SIMD microprocessor for image processing Author: Zhiqiang Qiu Student ID: 20716003 Email: [email protected] Supervisor: Prof. Dr. Thomas Bräunl Computational Intelligence - Information Processing Systems (CIIPS) School of Electrical, Electronic and Computer Engineering The University of Western Australia 1 November 2013 A SIMD microprocessor for image processing Abstract The aim of this project is to design a Single Instruction Multiple Data (SIMD) microprocessor for image processing. Image processing is an important topic in computer science. There are many interesting applications based on image processing, such as stereo matching, 3D object reconstruction and edge detection. The core of image processing is matrix manipulations on the digital image. A digital image captured by a modern digital camera is made up of millions of pixels. The common challenge for most of image processing applications is the amount of data needs to be processed. It will be very slow if each pixel is processed in a sequential order. In addition, general-purpose microprocessors are highly inefficient for image processing due to their complicated internal circuit and large instruction set. One particular solution is to process all pixels simultaneously. A SIMD microprocessor with a simple instruction set can significantly increase the overall processing speed. This project focuses on the development of a SIMD image processor using software simulation. It takes a three step approach. The first step is to improve and further develop our circuit simulation software, Retro. Retro is a powerful circuit design tool with build-in real time graphical simulation. A number of improvements have been made to Retro to fulfil our design requirements. -
Traffic Management Using Image Processing and Arm Processor
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 05 | May 2020 www.irjet.net p-ISSN: 2395-0072 TRAFFIC MANAGEMENT USING IMAGE PROCESSING AND ARM PROCESSOR Bhaskar MS1, Nandana CH1, Navya S1, Pooja P1 1Department of ECE, Sai Vidya Institute of Technology, India-560064. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In this paper, we aim to establish a smart and 2. With the advancement in computer technology, this system efficient traffic surveillance system which monitors the has an advantage of instantaneity, reliability and security. enormous movement of vehicles that cause traffic congestion. 3. It has easy maintenance. Here, we hope to develop a vision-based vehicle detection module that identifies the presence of vehicles and counts The rest of the paper is organized as follows: them. We achieve it with the help of Mali-C52, which is an ARM Section II: System overview based video processor which captures the real time video of Section III: System design and prototype the area of interest (AOI). This video is processed in various stages and is later interfaced with an ARM microcontroller Section IV: Conclusion which controls the traffic. II: SYSTEM OVERVIEW Key Words: Traffic congestion, Vehicle detection, MALI C52, Area of Interest (AOI) In the very first step, we obtain the real time video which is read and converted into images and then frames. This frame I: INTRODUCTION can be processed in a series of steps which are listed below: The rapid growth in the number of vehicles is a key feature of A. VEHICLE DETECTION BY BACKGROUND good economic development. -
Suchitra Sathyanarayana +1-858-717-2399 [email protected]
Suchitra Sathyanarayana +1-858-717-2399 [email protected] EDUCATION Doctor of Philosophy Nanyang Technological University, Singapore (Thesis submitted) M.Eng. (by research) Computer Eng., Nanyang Technological University, Singapore [2004-2006] B.Eng. (with Honors) Computer Eng., Nanyang Technological University, Singapore [1999-2003] RESEARCH EXPERIENCE Image Processing Architectures: Responsible for innovating high speed image processing cores by exploiting underlying parallelisms and image symmetries - (i) line feature extractor based on Hough Transform, which gives 1000x speedup; (ii) a novel parallel image rotation engine that achieves order of magnitude speedups especially for high-resolution images; (iii) low-cost pattern recognition system that achieves on-the-fly image rotation using line buffers and recognition using a simplified MLP neural network; (iv) a parallel EZW Wavelet encoding architecture that can be deployed for Haar based image compression systems. Hardware efficient techniques for Vision based Advanced Driver Assistance: As part of my PhD thesis, I developed computationally efficient techniques that detects and classifies various kinds of road markings, including lane markings, arrow markings of different types, zigzag markings, pedestrian markings etc. The proposed techniques are scale-invariant, hardware efficient and robust to complexities in road scenes such as shadows, lighting and weather changes. Vision-based Medical Applications: As part of my M.Eng (by research) thesis, I developed real-time techniques to assess the movements of camera during medical endoscopy. This included methods to infer camera rotations and longitudinal movements using image unrolling and corresponding matching of features. I am also currently involved with another PhD project on vision-based patient monitoring system, using multiple cues such as breathing and movements to infer abnormal patient behavior. -
Image Processor May Be Copied by Individuals and Not - for - Profit
DISTRIBUTION RELIGION THE IMAGE PROCESSOR MAY BE COPIED BY INDIVIDUALS AND NOT - FOR - PROFIT INSTITUTIONS WITHOUT CHARGE FOR - PROFIT INSTITUTIONS WILL HAVE TO NEGOTIATE FOR PERMISSION TO COPY I VIEW MY RESPONSIBILITY TO THE EVOLUTION OF NEW CONSCIOUSNESS HIGHER THAN MY RESPONSIBILITY TO MAKE PROFIT ; I THINK CULTURE HAS TO LEARN TO USE HIGH-TEK MACHINES FOR PERSONAL AESTHETIC, .RELIGIOUS, INTUITIVE, COMPREHENSIVE, EXPLORATORY GROWTH THE DEVELOPMENT OF MACHINES LIKE THE IMAGE PROCESSOR IS PART OF THIS EVOLUTION I AM PAID BY THE STATE, AT LEAST IN PART, TO DO AND DISEMINATE THIS INFORMATION ; SO I DOI As I am sure you (who are you) understand a work like developing and expanding the Image Processor requires much money and time . The 'U' does not have much money for evolutionary work and getting of grants are almost as much work as holding down a job . Therefore, I have the feeling that if considerable monies were to be made with a copy of the Image Processor, I would like some of it . So, I am asking (not telling) that if considerable money were made, by an individual with a copy of. the Image Processor, or if a copy of the Image Processor were sold (to an individual or not-for-profit institution), I would like 20% gross profit . I Things like $100 .00 honorariums should be ignored . Of course enforcing such a request is too difficult to be bothered with . But let it be known that I consider it to be morally binding Much Love . Daniel J . Sandin Department of Art University of Illinois at Chicago Circle Box 4348 Chicago, Illinois 60680 Office phone : 312-996-8689 Lab phone: 312-996-2312 Messages : 312-996-3337 (Department of Art) NOTES ON THE AESTHETICS OF 'copying-an-Ima ge Processor' : Being a 'copier' of many things, in this case the first copier of an Image Processor, .1 trust the following notes to find meaning to . -
A SIMD Programmable Vision Chip with High Speed Focal Plane Image Processing Dominique Ginhac, Jérôme Dubois, Michel Paindavoine, Barthélémy Heyrman
A SIMD Programmable Vision Chip with High Speed Focal Plane Image Processing Dominique Ginhac, Jérôme Dubois, Michel Paindavoine, Barthélémy Heyrman To cite this version: Dominique Ginhac, Jérôme Dubois, Michel Paindavoine, Barthélémy Heyrman. A SIMD Pro- grammable Vision Chip with High Speed Focal Plane Image Processing. EURASIP Journal on Em- bedded Systems, SpringerOpen, 2009, pp.13. 10.1155/2008/961315. hal-00517911 HAL Id: hal-00517911 https://hal.archives-ouvertes.fr/hal-00517911 Submitted on 15 Sep 2010 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. ACCEPTED TO EURASIP - JOURNAL OF EMBEDDED SYSTEMS 1 A SIMD Programmable Vision Chip with High Speed Focal Plane Image Processing Dominique Ginhac, Jer´ omeˆ Dubois, Michel Paindavoine, and Barthel´ emy´ Heyrman Abstract— A high speed analog VLSI image acquisition and and column-level [9]–[12]. Indeed, pixel-level processing is low-level image processing system is presented. The architecture generally dismissed because pixel sizes are often too large of the chip is based on a dynamically reconfigurable SIMD to be of practical use. However, as CMOS scales, integrating processor array. The chip features a massively parallel archi- tecture enabling the computation of programmable mask-based a processing element at each pixel or group of neighboring image processing in each pixel.