The Micro-Cluster Showcase: 7 Inexpensive Beowulf Clusters for Teaching PDC
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Multicomputer Cluster
Multicomputer • Multiple (full) computers connected by network. • Distributed memory each have special address space. • Access to data another processor is explicit in program, express by call function for sending or receiving message. • Don’t need special operating System, enough libraries with function for sub sending message. • Good scalability. In this section we discuss network computing, in which the nodes are stand- alone computers that could be connected via a switch, local area network, or the Internet. The main idea is to divide the application into semi-independent parts according to the kind of processing needed. Different nodes on the network can be assigned different parts of the application. This form of network computing takes advantage of the unique capabilities of diverse system architectures. It also maximally leverages potentially idle resources within a large organization. Therefore, unused CPU cycles may be utilized during short periods of time resulting in bursts of activity followed by periods of inactivity. In what follows, we discuss the utilization of network technology in order to create a computing infrastructure using commodity computers. Cluster • In 1990 shifted from expensive and specialized parallel machines to the more cost-effective clusters of PCs and workstations. • A cluster is a collection of stand-alone computers connected using some interconnection network. • Each node in a cluster could be a workstation. • Important for it to have fast processors and fast network to enable it to use for distributed system. • Cluster workstation component: 1. Fast processor/memory and complete HW for PC. 2. Free access SW. 3. High execute, low latency. The 1990s have witnessed a significant shift from expensive and specialized parallel machines to the more cost-effective clusters of PCs and workstations. -
Cluster Computing: Architectures, Operating Systems, Parallel Processing & Programming Languages
Cluster Computing Architectures, Operating Systems, Parallel Processing & Programming Languages Author Name: Richard S. Morrison Revision Version 2.4, Monday, 28 April 2003 Copyright © Richard S. Morrison 1998 – 2003 This document is distributed under the GNU General Public Licence [39] Print date: Tuesday, 28 April 2003 Document owner: Richard S. Morrison, [email protected] ✈ +612-9928-6881 Document name: CLUSTER_COMPUTING_THEORY Stored: (\\RSM\FURTHER_RESEARCH\CLUSTER_COMPUTING) Revision Version 2.4 Copyright © 2003 Synopsis & Acknolegdements My interest in Supercomputing through the use of clusters has been long standing and was initially sparked by an article in Electronic Design [33] in August 1998 on the Avalon Beowulf Cluster [24]. Between August 1998 and August 1999 I gathered information from websites and parallel research groups. This culminated in September 1999 when I organised the collected material and wove a common thread through the subject matter producing two handbooks for my own use on cluster computing. Each handbook is of considerable length, which was governed by the wealth of information and research conducted in this area over the last 5 years. The cover the handbooks are shown in Figure 1-1 below. Figure 1-1 – Author Compiled Beowulf Class 1 Handbooks Through my experimentation using the Linux Operating system and the undertaking of the University of Technology, Sydney (UTS) undergraduate subject Operating Systems in Autumn Semester 1999 with Noel Carmody, a systems level focus was developed and is the core element of this material contained in this document. This led to my membership to the IEEE and the IEEE Technical Committee on Parallel Processing, where I am able to gather and contribute information and be kept up to date on the latest issues. -
A 1024-Core 70GFLOPS/W Floating Point Manycore Microprocessor
A 1024-core 70GFLOPS/W Floating Point Manycore Microprocessor Andreas Olofsson, Roman Trogan, Oleg Raikhman Adapteva, Lexington, MA The Past, Present, & Future of Computing SIMD MIMD PE PE PE PE MINI MINI MINI CPU CPU CPU PE PE PE PE MINI MINI MINI CPU CPU CPU PE PE PE PE MINI MINI MINI CPU CPU CPU MINI MINI MINI BIG BIG CPU CPU CPU CPU CPU BIG BIG BIG BIG CPU CPU CPU CPU PAST PRESENT FUTURE 2 Adapteva’s Manycore Architecture C/C++ Programmable Incredibly Scalable 70 GFLOPS/W 3 Routing Architecture 4 E64G400 Specifications (Jan-2012) • 64-Core Microprocessor • 100 GFLOPS performance • 800 MHz Operation • 8GB/sec IO bandwidth • 1.6 TB/sec on chip memory BW • 0.8 TB/sec network on chip BW • 64 Billion Messages/sec IO Pads Core • 2 Watt total chip power • 2MB on chip memory Link Logic • 10 mm2 total silicon area • 324 ball 15x15mm plastic BGA 5 Lab Measurements 80 Energy Efficiency 70 60 50 GFLOPS/W 40 30 20 10 0 0 200 400 600 800 1000 1200 MHz ENERGY EFFICIENCY ENERGY EFFICIENCY (28nm) 6 Epiphany Performance Scaling 16,384 G 4,096 F 1,024 L 256 O 64 4096 P 1024 S 16 256 64 4 16 1 # Cores On‐demand scaling from 0.25W to 64 Watt 7 Hold on...the title said 1024 cores! • We can build it any time! • Waiting for customer • LEGO approach to design • No global timinga paths • Guaranteed by design • Generate any array in 1 day • ~130 mm2 silicon area 1024 Cores 1Core 8 What about 64-bit Floating Point? Single Precision Double Precision 2 FLOPS/CYCLE 2 FLOPS/CYCLE 64KB SRAM 64KB SRAM 0.215mm^2 0.237mm^2 700MHz 600MHz 9 Epiphany Latency Specifications -
Comparison of 116 Open Spec, Hacker Friendly Single Board Computers -- June 2018
Comparison of 116 Open Spec, Hacker Friendly Single Board Computers -- June 2018 Click on the product names to get more product information. In most cases these links go to LinuxGizmos.com articles with detailed product descriptions plus market analysis. HDMI or DP- USB Product Price ($) Vendor Processor Cores 3D GPU MCU RAM Storage LAN Wireless out ports Expansion OSes 86Duino Zero / Zero Plus 39, 54 DMP Vortex86EX 1x x86 @ 300MHz no no2 128MB no3 Fast no4 no5 1 headers Linux Opt. 4GB eMMC; A20-OLinuXino-Lime2 53 or 65 Olimex Allwinner A20 2x A7 @ 1GHz Mali-400 no 1GB Fast no yes 3 other Linux, Android SATA A20-OLinuXino-Micro 65 or 77 Olimex Allwinner A20 2x A7 @ 1GHz Mali-400 no 1GB opt. 4GB NAND Fast no yes 3 other Linux, Android Debian Linux A33-OLinuXino 42 or 52 Olimex Allwinner A33 4x A7 @ 1.2GHz Mali-400 no 1GB opt. 4GB NAND no no no 1 dual 40-pin 3.4.39, Android 4.4 4GB (opt. 16GB A64-OLinuXino 47 to 88 Olimex Allwinner A64 4x A53 @ 1.2GHz Mali-400 MP2 no 1GB GbE WiFi, BT yes 1 40-pin custom Linux eMMC) Banana Pi BPI-M2 Berry 36 SinoVoip Allwinner V40 4x A7 Mali-400 MP2 no 1GB SATA GbE WiFi, BT yes 4 Pi 40 Linux, Android 8GB eMMC (opt. up Banana Pi BPI-M2 Magic 21 SinoVoip Allwinner A33 4x A7 Mali-400 MP2 no 512MB no Wifi, BT no 2 Pi 40 Linux, Android to 64GB) 8GB to 64GB eMMC; Banana Pi BPI-M2 Ultra 56 SinoVoip Allwinner R40 4x A7 Mali-400 MP2 no 2GB GbE WiFi, BT yes 4 Pi 40 Linux, Android SATA Banana Pi BPI-M2 Zero 21 SinoVoip Allwinner H2+ 4x A7 @ 1.2GHz Mali-400 MP2 no 512MB no no WiFi, BT yes 1 Pi 40 Linux, Android Banana -
Building a Beowulf Cluster
Building a Beowulf cluster Åsmund Ødegård April 4, 2001 1 Introduction The main part of the introduction is only contained in the slides for this session. Some of the acronyms and names in this paper may be unknown. In Appendix B we includ short descriptions for some of them. Most of this is taken from “whatis” [6] 2 Outline of the installation ² Install linux on a PC ² Configure the PC to act as a install–server for the cluster ² Wire up the network if that isn’t done already ² Install linux on the rest of the nodes ² Configure one PC, e.g the install–server, to be a server for your cluster. These are the main steps required to build a linux cluster, but each step can be done in many different ways. How you prefer to do it, depends mainly on personal taste, though. Therefor, I will translate the given outline into this list: ² Install Debian GNU/Linux on a PC ² Install and configure “FAI” on the PC ² Build the “FAI” boot–floppy ² Assemble hardware information, and finalize the “FAI” configuration ² Boot each node with the boot–floppy ² Install and configure a queue system and software for running parallel jobs on your cluster 3 Debian The choice of Linux distribution is most of all a matter of personal taste. I prefer the Debian distri- bution for various reasons. So, the first step in the cluster–building process is to pick one of the PCs as a install–server, and install Debian onto it, as follows: ² Make sure that the computer can boot from cdrom. -
Beowulf Clusters Make Supercomputing Accessible
Nor-Tech Contributes to NASA Article: Beowulf Clusters Make Supercomputing Accessible Original article available at NASA Spinoff: https://spinoff.nasa.gov/Spinoff2020/it_1.html NASA Technology In the Old English epic Beowulf, the warrior Unferth, jealous of the eponymous hero’s bravery, openly doubts Beowulf’s odds of slaying the monster Grendel that has tormented the Danes for 12 years, promising a “grim grappling” if he dares confront the dreaded march-stepper. A thousand years later, many in the supercomputing world were similarly skeptical of a team of NASA engineers trying achieve supercomputer-class processing on a cluster of standard desktop computers running a relatively untested open source operating system. “Not only did nobody care, but there were even a number of people hostile to this project,” says Thomas Sterling, who led the small team at NASA’s Goddard Space Flight Center in the early 1990s. “Because it was different. Because it was completely outside the scope of the Thomas Sterling, who co-invented the Beowulf supercomputing cluster at Goddard Space Flight Center, poses with the Naegling cluster at California supercomputing community at that time.” Technical Institute in 1997. Consisting of 120 Pentium Pro processors, The technology, now known as the Naegling was the first cluster to hit 10 gigaflops of sustained performance. Beowulf cluster, would ultimately succeed beyond its inventors’ imaginations. In 1993, however, its odds may indeed have seemed long. The U.S. Government, nervous about Japan’s high- performance computing effort, had already been pouring money into computer architecture research at NASA and other Federal agencies for more than a decade, and results were frustrating. -
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. -
SPORK: a Summarization Pipeline for Online Repositories of Knowledge
SPORK: A SUMMARIZATION PIPELINE FOR ONLINE REPOSITORIES OF KNOWLEDGE A Thesis presented to the Faculty of California Polytechnic State University San Luis Obispo In Partial Fulfillment of the Requirements for the Degree Master of Science in Computer Science by Steffen Lyngbaek June 2013 c 2013 Steffen Lyngbaek ALL RIGHTS RESERVED ii COMMITTEE MEMBERSHIP TITLE: SPORK: A Summarization Pipeline for Online Repositories of Knowledge AUTHOR: Steffen Lyngbaek DATE SUBMITTED: June 2013 COMMITTEE CHAIR: Professor Alexander Dekhtyar, Ph.D., De- parment of Computer Science COMMITTEE MEMBER: Professor Franz Kurfess, Ph.D., Depar- ment of Computer Science COMMITTEE MEMBER: Professor Foaad Khosmood, Ph.D., Depar- ment of Computer Science iii Abstract SPORK: A Summarization Pipeline for Online Repositories of Knowledge Steffen Lyngbaek The web 2.0 era has ushered an unprecedented amount of interactivity on the Internet resulting in a flood of user-generated content. This content is of- ten unstructured and comes in the form of blog posts and comment discussions. Users can no longer keep up with the amount of content available, which causes developers to start relying on natural language techniques to help mitigate the problem. Although many natural language processing techniques have been em- ployed for years, automatic text summarization, in particular, has recently gained traction. This research proposes a graph-based, extractive text summarization system called SPORK (Summarization Pipeline for Online Repositories of Knowl- edge). The goal of SPORK is to be able to identify important key topics presented in multi-document texts, such as online comment threads. While most other automatic summarization systems simply focus on finding the top sentences rep- resented in the text, SPORK separates the text into clusters, and identifies dif- ferent topics and opinions presented in the text. -
Master's Thesis: Adaptive Core Assignment for Adapteva Epiphany
Adaptive core assignment for Adapteva Epiphany Master of Science Thesis in Embedded Electronic System Design Erik Alveflo Chalmers University of Technology Department of Computer Science and Engineering G¨oteborg, Sweden 2015 The Author grants to Chalmers University of Technology and University of Gothenburg the non-exclusive right to publish the Work electronically and in a non-commercial pur- pose make it accessible on the Internet. The Author warrants that he/she is the author to the Work, and warrants that the Work does not contain text, pictures or other mate- rial that violates copyright law. The Author shall, when transferring the rights of the Work to a third party (for example a publisher or a company), acknowledge the third party about this agreement. If the Author has signed a copyright agreement with a third party regarding the Work, the Author warrants hereby that he/she has obtained any necessary permission from this third party to let Chalmers University of Technology and University of Gothenburg store the Work electronically and make it accessible on the Internet. Adaptive core assignment for Adapteva Epiphany Erik Alveflo, c Erik Alveflo, 2015. Examiner: Per Larsson-Edefors Chalmers University of Technology Department of Computer Science and Engineering SE-412 96 G¨oteborg Sweden Telephone + 46 (0)31-772 1000 Department of Computer Science and Engineering G¨oteborg, Sweden 2015 Adaptive core assignment for Adapteva Epiphany Erik Alveflo Department of Computer Science and Engineering Chalmers University of Technology Abstract The number of cores in many-core processors is ever increasing, and so is the number of defects due to manufacturing variations and wear-out mechanisms. -
Spark on Hadoop Vs MPI/Openmp on Beowulf
Procedia Computer Science Volume 53, 2015, Pages 121–130 2015 INNS Conference on Big Data Big Data Analytics in the Cloud: Spark on Hadoop vs MPI/OpenMP on Beowulf Jorge L. Reyes-Ortiz1, Luca Oneto2, and Davide Anguita1 1 DIBRIS, University of Genoa, Via Opera Pia 13, I-16145, Genoa, Italy ([email protected], [email protected]) 2 DITEN, University of Genoa, Via Opera Pia 11A, I-16145, Genoa, Italy ([email protected]) Abstract One of the biggest challenges of the current big data landscape is our inability to pro- cess vast amounts of information in a reasonable time. In this work, we explore and com- pare two distributed computing frameworks implemented on commodity cluster architectures: MPI/OpenMP on Beowulf that is high-performance oriented and exploits multi-machine/multi- core infrastructures, and Apache Spark on Hadoop which targets iterative algorithms through in-memory computing. We use the Google Cloud Platform service to create virtual machine clusters, run the frameworks, and evaluate two supervised machine learning algorithms: KNN and Pegasos SVM. Results obtained from experiments with a particle physics data set show MPI/OpenMP outperforms Spark by more than one order of magnitude in terms of processing speed and provides more consistent performance. However, Spark shows better data manage- ment infrastructure and the possibility of dealing with other aspects such as node failure and data replication. Keywords: Big Data, Supervised Learning, Spark, Hadoop, MPI, OpenMP, Beowulf, Cloud, Parallel Computing 1 Introduction The information age brings along an explosion of big data from multiple sources in every aspect of our lives: human activity signals from wearable sensors, experiments from particle discovery research and stock market data systems are only a few examples [48]. -
Program & Exhibits Guide
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Choosing the Right Hardware for the Beowulf Clusters Considering Price/Performance Ratio
CHOOSING THE RIGHT HARDWARE FOR THE BEOWULF CLUSTERS CONSIDERING PRICE/PERFORMANCE RATIO Muhammet ÜNAL Selma YÜNCÜ e-mail: [email protected] e-mail: [email protected] Gazi University, Faculty of Engineering and Architecture, Department of Electrical & Electronics Engineering 06570, Maltepe, Ankara, Turkey Key words: Computer Hardware Architecture, Parallel Computers, Distributed Computing, Networks of Workstations (NOW), Beowulf, Cluster, Benchmark ABSTRACT Scalability requires: In this study, reasons of choosing Beowulf Clusters from • Functionality and Performance: The scaled-up system different types of supercomputers are given. Then Beowulf should provide more functionality or better systems are briefly summarized, and some design performance. information about the hardware system of the High Performance Computing (HPC) Laboratory that will be • Scaling in Cost: The cost paid for scaling up must be build up in Gazi University Electrical and Electronics reasonable. Engineering department is given. • Compatibility: The same components, including hardware, system software, and application software I. INTRODUCTION should still be usable with little change. The world of modern computing potentially offers many helpful methods and tools to scientists and engineers, but II. SYSTEMS PERFORMANCE ATTRIBUTES the fast pace of change in computer hardware, software, Basic performance attributes are summarized in table 1. and algorithms often makes practical use of the newest Machine size n is the number of processors in a parallel computing technology difficult. Just two generations ago computer. Workload W is the number of computation based on Moore’s law [1], a plethora of vector operations in a program Ppeak is the peak speed of a supercomputers, non-scalable multiprocessors, and MPP processor.