A Distributed Multi-GPU System for Fast Graph Processing
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http://www.physics.miami.edu/~chris/srchmrk_nws.html ● Miami-Dade/Broward/Palm Beach News ❍ Miami Herald Online Sun Sentinel Palm Beach Post ❍ Miami ABC, Ch 10 Miami NBC, Ch 6 ❍ Miami CBS, Ch 4 Miami Fox, WSVN Ch 7 ● Local Government, Schools, Universities, Culture ❍ Miami Dade County Government ❍ Village of Pinecrest ❍ Miami Dade County Public Schools ❍ ❍ University of Miami UM Arts & Sciences ❍ e-Veritas Univ of Miami Faculty and Staff "news" ❍ The Hurricane online University of Miami Student Newspaper ❍ Tropic Culture Miami ❍ Culture Shock Miami ● Local Traffic ❍ Traffic Conditions in Miami from SmartTraveler. ❍ Traffic Conditions in Florida from FHP. ❍ Traffic Conditions in Miami from MSN Autos. ❍ Yahoo Traffic for Miami. ❍ Road/Highway Construction in Florida from Florida DOT. ❍ WSVN's (Fox, local Channel 7) live Traffic conditions in Miami via RealPlayer. News, Information, Rumors, Opinions, Etc. ● Science News ❍ EurekAlert! ❍ New York Times Science/Health ❍ BBC Science/Technology ❍ Popular Science ❍ Space.com ❍ CNN Space ❍ ABC News Science/Technology ❍ CBS News Sci/Tech ❍ LA Times Science ❍ Scientific American ❍ Science News ❍ MIT Technology Review ❍ New Scientist ❍ Physorg.com ❍ PhysicsToday.org ❍ Sky and Telescope News ❍ ENN - Environmental News Network ● Technology/Computer News/Rumors/Opinions ❍ Google Tech/Sci News or Yahoo Tech News or Google Top Stories ❍ ArsTechnica Wired ❍ SlashDot Digg DoggDot.us ❍ reddit digglicious.com Technorati ❍ del.ic.ious furl.net ❍ New York Times Technology San Jose Mercury News Technology Washington -
Distributed & Cloud Computing: Lecture 1
Distributed & cloud computing: Lecture 1 Chokchai “Box” Leangsuksun SWECO Endowed Professor, Computer Science Louisiana Tech University [email protected] CTO, PB Tech International Inc. [email protected] Class Info ■" Class Hours: noon-1:50 pm T-Th ! ■" Dr. Box’s & Contact Info: ●"www.latech.edu/~box ●"Phone 318-257-3291 ●"Email: [email protected] Class Info ■" Main Text: ! ●" 1) Distributed Systems: Concepts and Design By Coulouris, Dollimore, Kindberg and Blair Edition 5, © Addison-Wesley 2012 ●" 2) related publications & online literatures Class Info ■" Objective: ! ●"the theory, design and implementation of distributed systems ●"Discussion on abstractions, Concepts and current systems/applications ●"Best practice on Research on DS & cloud computing Course Contents ! •" The Characterization of distributed computing and cloud computing. •" System Models. •" Networking and inter-process communication. •" OS supports and Virtualization. •" RAS, Performance & Reliability Modeling. Security. !! Course Contents •" Introduction to Cloud Computing ! •" Various models and applications. •" Deployment models •" Service models (SaaS, PaaS, IaaS, Xaas) •" Public Cloud: Amazon AWS •" Private Cloud: openStack. •" Cost models ( between cloud vs host your own) ! •" ! !!! Course Contents case studies! ! •" Introduction to HPC! •" Multicore & openMP! •" Manycore, GPGPU & CUDA! •" Cloud-based EKG system! •" Distributed Object & Web Services (if time allows)! •" Distributed File system (if time allows)! •" Replication & Disaster Recovery Preparedness! !!!!!! Important Dates ! ■" Apr 10: Mid Term exam ■" Apr 22: term paper & presentation due ■" May 15: Final exam Evaluations ! ■" 20% Lab Exercises/Quizzes ■" 20% Programming Assignments ■" 10% term paper ■" 20% Midterm Exam ■" 30% Final Exam Intro to Distributed Computing •" Distributed System Definitions. •" Distributed Systems Examples: –" The Internet. –" Intranets. –" Mobile Computing –" Cloud Computing •" Resource Sharing. •" The World Wide Web. •" Distributed Systems Challenges. Based on Mostafa’s lecture 10 Distributed Systems 0. -
Distributed Algorithms with Theoretic Scalability Analysis of Radial and Looped Load flows for Power Distribution Systems
Electric Power Systems Research 65 (2003) 169Á/177 www.elsevier.com/locate/epsr Distributed algorithms with theoretic scalability analysis of radial and looped load flows for power distribution systems Fangxing Li *, Robert P. Broadwater ECE Department Virginia Tech, Blacksburg, VA 24060, USA Received 15 April 2002; received in revised form 14 December 2002; accepted 16 December 2002 Abstract This paper presents distributed algorithms for both radial and looped load flows for unbalanced, multi-phase power distribution systems. The distributed algorithms are developed from tree-based sequential algorithms. Formulas of scalability for the distributed algorithms are presented. It is shown that computation time dominates communication time in the distributed computing model. This provides benefits to real-time load flow calculations, network reconfigurations, and optimization studies that rely on load flow calculations. Also, test results match the predictions of derived formulas. This shows the formulas can be used to predict the computation time when additional processors are involved. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Distributed computing; Scalability analysis; Radial load flow; Looped load flow; Power distribution systems 1. Introduction Also, the method presented in Ref. [10] was tested in radial distribution systems with no more than 528 buses. Parallel and distributed computing has been applied More recent works [11Á/14] presented distributed to many scientific and engineering computations such as implementations for power flows or power flow based weather forecasting and nuclear simulations [1,2]. It also algorithms like optimizations and contingency analysis. has been applied to power system analysis calculations These works also targeted power transmission systems. [3Á/14]. -
Technology, Media, and Telecommunications Predictions
Technology, Media, and Telecommunications Predictions 2019 Deloitte’s Technology, Media, and Telecommunications (TMT) group brings together one of the world’s largest pools of industry experts—respected for helping companies of all shapes and sizes thrive in a digital world. Deloitte’s TMT specialists can help companies take advantage of the ever- changing industry through a broad array of services designed to meet companies wherever they are, across the value chain and around the globe. Contact the authors for more information or read more on Deloitte.com. Technology, Media, and Telecommunications Predictions 2019 Contents Foreword | 2 5G: The new network arrives | 4 Artificial intelligence: From expert-only to everywhere | 14 Smart speakers: Growth at a discount | 24 Does TV sports have a future? Bet on it | 36 On your marks, get set, game! eSports and the shape of media in 2019 | 50 Radio: Revenue, reach, and resilience | 60 3D printing growth accelerates again | 70 China, by design: World-leading connectivity nurtures new digital business models | 78 China inside: Chinese semiconductors will power artificial intelligence | 86 Quantum computers: The next supercomputers, but not the next laptops | 96 Deloitte Australia key contacts | 108 1 Technology, Media, and Telecommunications Predictions 2019 Foreword Dear reader, Welcome to Deloitte Global’s Technology, Media, and Telecommunications Predictions for 2019. The theme this year is one of continuity—as evolution rather than stasis. Predictions has been published since 2001. Back in 2009 and 2010, we wrote about the launch of exciting new fourth-generation wireless networks called 4G (aka LTE). A decade later, we’re now making predic- tions about 5G networks that will be launching this year. -
PC Gamers Win Big
CASE STUDY Intel® Solid-State Drives Performance, Storage and the Customer Experience PC Gamers Win Big Intel® Solid-State Drives (SSDs) deliver the ultimate gaming experience, providing dramatic visual and runtime improvements Intel® Solid-State Drives (SSDs) represent a revolutionary breakthrough, delivering a giant leap in storage performance. Designed to satisfy the most demanding gamers, media creators, and technology enthusiasts, Intel® SSDs bring a high level of performance and reliability to notebook and desktop PC storage. Faster load times and improved graphics performance such as increased detail in textures, higher resolution geometry, smoother animation, and more characters on the screen make for a better gaming experience. Developers are now taking advantage of these features in their new game designs. SCREAMING LOAD TiMES AND SMOOTH GRAPHICS With no moving parts, high reliability, and a longer life span than traditional hard drives, Intel Solid-State Drives (SSDs) dramatically improve the computer gaming experience. Load times are substantially faster. When compared with Western Digital VelociRaptor* 10K hard disk drives (HDDs), gamers experienced up to 78 percent load time improvements using Intel SSDs. Graphics are smooth and uninterrupted, even at the highest graphics settings. To see the performance difference in a head-to- head video comparing the Intel® X25-M SATA SSD with a 10,000 RPM HDD, go to www.intelssdgaming.com. When comparing frame-to-frame coherency with the Western Digital VelociRaptor 10K HDD, the Intel X25-M responds with zero hitching while the WD VelociRaptor shows hitching seven percent of the time. This means gamers experience smoother visual transitions with Intel SSDs. -
Lecture 1: CS/ECE 3810 Introduction
Lecture 1: CS/ECE 3810 Introduction • Today’s topics: . Why computer organization is important . Logistics . Modern trends 1 Why Computer Organization 2 Image credits: uber, extremetech, anandtech Why Computer Organization 3 Image credits: gizmodo Why Computer Organization • Embarrassing if you are a BS in CS/CE and can’t make sense of the following terms: DRAM, pipelining, cache hierarchies, I/O, virtual memory, … • Embarrassing if you are a BS in CS/CE and can’t decide which processor to buy: 4.4 GHz Intel Core i9 or 4.7 GHz AMD Ryzen 9 (reason about performance/power) • Obvious first step for chip designers, compiler/OS writers • Will knowledge of the hardware help you write better and more secure programs? 4 Must a Programmer Care About Hardware? • Must know how to reason about program performance and energy and security • Memory management: if we understand how/where data is placed, we can help ensure that relevant data is nearby • Thread management: if we understand how threads interact, we can write smarter multi-threaded programs Why do we care about multi-threaded programs? 5 Example 200x speedup for matrix vector multiplication • Data level parallelism: 3.8x • Loop unrolling and out-of-order execution: 2.3x • Cache blocking: 2.5x • Thread level parallelism: 14x Further, can use accelerators to get an additional 100x. 6 Key Topics • Moore’s Law, power wall • Use of abstractions • Assembly language • Computer arithmetic • Pipelining • Using predictions • Memory hierarchies • Accelerators • Reliability and Security 7 Logistics -
Grid Computing: What Is It, and Why Do I Care?*
Grid Computing: What Is It, and Why Do I Care?* Ken MacInnis <[email protected]> * Or, “Mi caja es su caja!” (c) Ken MacInnis 2004 1 Outline Introduction and Motivation Examples Architecture, Components, Tools Lessons Learned and The Future Questions? (c) Ken MacInnis 2004 2 What is “grid computing”? Many different definitions: Utility computing Cycles for sale Distributed computing distributed.net RC5, SETI@Home High-performance resource sharing Clusters, storage, visualization, networking “We will probably see the spread of ‘computer utilities’, which, like present electric and telephone utilities, will service individual homes and offices across the country.” Len Kleinrock (1969) The word “grid” doesn’t equal Grid Computing: Sun Grid Engine is a mere scheduler! (c) Ken MacInnis 2004 3 Better definitions: Common protocols allowing large problems to be solved in a distributed multi-resource multi-user environment. “A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities.” Kesselman & Foster (1998) “…coordinated resource sharing and problem solving in dynamic, multi- institutional virtual organizations.” Kesselman, Foster, Tuecke (2000) (c) Ken MacInnis 2004 4 New Challenges for Computing Grid computing evolved out of a need to share resources Flexible, ever-changing “virtual organizations” High-energy physics, astronomy, more Differing site policies with common needs Disparate computing needs -
Survey and Benchmarking of Machine Learning Accelerators
1 Survey and Benchmarking of Machine Learning Accelerators Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, and Jeremy Kepner MIT Lincoln Laboratory Supercomputing Center Lexington, MA, USA freuther,pmichaleas,michael.jones,vijayg,sid,[email protected] Abstract—Advances in multicore processors and accelerators components play a major role in the success or failure of an have opened the flood gates to greater exploration and application AI system. of machine learning techniques to a variety of applications. These advances, along with breakdowns of several trends including Moore’s Law, have prompted an explosion of processors and accelerators that promise even greater computational and ma- chine learning capabilities. These processors and accelerators are coming in many forms, from CPUs and GPUs to ASICs, FPGAs, and dataflow accelerators. This paper surveys the current state of these processors and accelerators that have been publicly announced with performance and power consumption numbers. The performance and power values are plotted on a scatter graph and a number of dimensions and observations from the trends on this plot are discussed and analyzed. For instance, there are interesting trends in the plot regarding power consumption, numerical precision, and inference versus training. We then select and benchmark two commercially- available low size, weight, and power (SWaP) accelerators as these processors are the most interesting for embedded and Fig. 1. Canonical AI architecture consists of sensors, data conditioning, mobile machine learning inference applications that are most algorithms, modern computing, robust AI, human-machine teaming, and users (missions). Each step is critical in developing end-to-end AI applications and applicable to the DoD and other SWaP constrained users. -
Computing at Massive Scale: Scalability and Dependability Challenges
This is a repository copy of Computing at massive scale: Scalability and dependability challenges. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/105671/ Version: Accepted Version Proceedings Paper: Yang, R and Xu, J orcid.org/0000-0002-4598-167X (2016) Computing at massive scale: Scalability and dependability challenges. In: Proceedings - 2016 IEEE Symposium on Service-Oriented System Engineering, SOSE 2016. 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE), 29 Mar - 02 Apr 2016, Oxford, United Kingdom. IEEE , pp. 386-397. ISBN 9781509022533 https://doi.org/10.1109/SOSE.2016.73 (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. -
A Guide to Smartphone Astrophotography National Aeronautics and Space Administration
National Aeronautics and Space Administration A Guide to Smartphone Astrophotography National Aeronautics and Space Administration A Guide to Smartphone Astrophotography A Guide to Smartphone Astrophotography Dr. Sten Odenwald NASA Space Science Education Consortium Goddard Space Flight Center Greenbelt, Maryland Cover designs and editing by Abbey Interrante Cover illustrations Front: Aurora (Elizabeth Macdonald), moon (Spencer Collins), star trails (Donald Noor), Orion nebula (Christian Harris), solar eclipse (Christopher Jones), Milky Way (Shun-Chia Yang), satellite streaks (Stanislav Kaniansky),sunspot (Michael Seeboerger-Weichselbaum),sun dogs (Billy Heather). Back: Milky Way (Gabriel Clark) Two front cover designs are provided with this book. To conserve toner, begin document printing with the second cover. This product is supported by NASA under cooperative agreement number NNH15ZDA004C. [1] Table of Contents Introduction.................................................................................................................................................... 5 How to use this book ..................................................................................................................................... 9 1.0 Light Pollution ....................................................................................................................................... 12 2.0 Cameras ................................................................................................................................................ -
Lecture 18: Parallel and Distributed Systems
Lecture 18: Parallel and What is a distributed system? Distributed Systems n From various textbooks: u “A distributed system is a collection of independent computers n Classification of parallel/distributed that appear to the users of the system as a single computer.” architectures u “A distributed system consists of a collection of autonomous n SMPs computers linked to a computer network and equipped with n Distributed systems distributed system software.” u n Clusters “A distributed system is a collection of processors that do not share memory or a clock.” u “Distributed systems is a term used to define a wide range of computer systems from a weakly-coupled system such as wide area networks, to very strongly coupled systems such as multiprocessor systems.” 1 2 What is a distributed system?(cont.) What is a distributed system?(cont.) n Michael Flynn (1966) P1 P2 P3 u n A distributed system is Network SISD — single instruction, single data u SIMD — single instruction, multiple data a set of physically separate u processors connected by MISD — multiple instruction, single data u MIMD — multiple instruction, multiple data one or more P4 P5 communication links n More recent (Stallings, 1993) n Is every system with >2 computers a distributed system?? u Email, ftp, telnet, world-wide-web u Network printer access, network file access, network file backup u We don’t usually consider these to be distributed systems… 3 4 MIMD Classification parallel and distributed Classification of the OS of MIMD computers Architectures tightly loosely coupled -
Hybrid Computing – Co-Processors/Accelerators Power-Aware Computing – Performance of Applications Kernels
C-DAC Four Days Technology Workshop ON Hybrid Computing – Co-Processors/Accelerators Power-aware Computing – Performance of Applications Kernels hyPACK-2013 (Mode-1:Multi-Core) Lecture Topic : Multi-Core Processors : Algorithms & Applications Overview - Part-II Venue : CMSD, UoHYD ; Date : October 15-18, 2013 C-DAC hyPACK-2013 Multi-Core Processors : Alg.& Appls Overview Part-II 1 Killer Apps of Tomorrow Application-Driven Architectures: Analyzing Tera-Scale Workloads Workload convergence The basic algorithms shared by these high-end workloads Platform implications How workload analysis guides future architectures Programmer productivity Optimized architectures will ease the development of software Call to Action Benchmark suites in critical need for redress C-DAC hyPACK-2013 Multi-Core Processors : Alg.& Appls Overview Part-II 2 Emerging “Killer Apps of Tomorrow” Parallel Algorithms Design Static and Load Balancing Mapping for load balancing Minimizing Interaction Overheads in parallel algorithms design Data Sharing Overheads C-DAC hyPACK-2013 Multi-Core Processors : Alg.& Appls Overview Part-II 3 Emerging “Killer Apps of Tomorrow” Parallel Algorithms Design (Contd…) General Techniques for Choosing Right Parallel Algorithm Maximize data locality Minimize volume of data Minimize frequency of Interactions Overlapping computations with interactions. Data replication Minimize construction and Hot spots. Use highly optimized collective interaction operations. Collective data transfers and computations • Maximize Concurrency.