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Supercomputing in Plain English: Overview
Supercomputing in Plain English An Introduction to High Performance Computing Part I: Overview: What the Heck is Supercomputing? Henry Neeman, Director OU Supercomputing Center for Education & Research Goals of These Workshops To introduce undergrads, grads, staff and faculty to supercomputing issues To provide a common language for discussing supercomputing issues when we meet to work on your research NOT: to teach everything you need to know about supercomputing – that can’t be done in a handful of hourlong workshops! OU Supercomputing Center for Education & Research 2 What is Supercomputing? Supercomputing is the biggest, fastest computing right this minute. Likewise, a supercomputer is the biggest, fastest computer right this minute. So, the definition of supercomputing is constantly changing. Rule of Thumb: a supercomputer is 100 to 10,000 times as powerful as a PC. Jargon: supercomputing is also called High Performance Computing (HPC). OU Supercomputing Center for Education & Research 3 What is Supercomputing About? Size Speed OU Supercomputing Center for Education & Research 4 What is Supercomputing About? Size: many problems that are interesting to scientists and engineers can’t fit on a PC – usually because they need more than 2 GB of RAM, or more than 60 GB of hard disk. Speed: many problems that are interesting to scientists and engineers would take a very very long time to run on a PC: months or even years. But a problem that would take a month on a PC might take only a few hours on a supercomputer. OU Supercomputing -
5. Data Types
IEEE FOR THE FUNCTIONAL VERIFICATION LANGUAGE e Std 1647-2011 5. Data types The e language has a number of predefined data types, including the integer and Boolean scalar types common to most programming languages. In addition, new scalar data types (enumerated types) that are appropriate for programming, modeling hardware, and interfacing with hardware simulators can be created. The e language also provides a powerful mechanism for defining OO hierarchical data structures (structs) and ordered collections of elements of the same type (lists). The following subclauses provide a basic explanation of e data types. 5.1 e data types Most e expressions have an explicit data type, as follows: — Scalar types — Scalar subtypes — Enumerated scalar types — Casting of enumerated types in comparisons — Struct types — Struct subtypes — Referencing fields in when constructs — List types — The set type — The string type — The real type — The external_pointer type — The “untyped” pseudo type Certain expressions, such as HDL objects, have no explicit data type. See 5.2 for information on how these expressions are handled. 5.1.1 Scalar types Scalar types in e are one of the following: numeric, Boolean, or enumerated. Table 17 shows the predefined numeric and Boolean types. Both signed and unsigned integers can be of any size and, thus, of any range. See 5.1.2 for information on how to specify the size and range of a scalar field or variable explicitly. See also Clause 4. 5.1.2 Scalar subtypes A scalar subtype can be named and created by using a scalar modifier to specify the range or bit width of a scalar type. -
Computer Hardware Architecture Lecture 4
Computer Hardware Architecture Lecture 4 Manfred Liebmann Technische Universit¨atM¨unchen Chair of Optimal Control Center for Mathematical Sciences, M17 [email protected] November 10, 2015 Manfred Liebmann November 10, 2015 Reading List • Pacheco - An Introduction to Parallel Programming (Chapter 1 - 2) { Introduction to computer hardware architecture from the parallel programming angle • Hennessy-Patterson - Computer Architecture - A Quantitative Approach { Reference book for computer hardware architecture All books are available on the Moodle platform! Computer Hardware Architecture 1 Manfred Liebmann November 10, 2015 UMA Architecture Figure 1: A uniform memory access (UMA) multicore system Access times to main memory is the same for all cores in the system! Computer Hardware Architecture 2 Manfred Liebmann November 10, 2015 NUMA Architecture Figure 2: A nonuniform memory access (UMA) multicore system Access times to main memory differs form core to core depending on the proximity of the main memory. This architecture is often used in dual and quad socket servers, due to improved memory bandwidth. Computer Hardware Architecture 3 Manfred Liebmann November 10, 2015 Cache Coherence Figure 3: A shared memory system with two cores and two caches What happens if the same data element z1 is manipulated in two different caches? The hardware enforces cache coherence, i.e. consistency between the caches. Expensive! Computer Hardware Architecture 4 Manfred Liebmann November 10, 2015 False Sharing The cache coherence protocol works on the granularity of a cache line. If two threads manipulate different element within a single cache line, the cache coherency protocol is activated to ensure consistency, even if every thread is only manipulating its own data. -
Occam 2.1 Reference Manual
occam 2.1 reference manual SGS-THOMSON Microelectronics Limited May 12, 1995 iv occam 2.1 REFERENCE MANUAL SGS-THOMSON Microelectronics Limited First published 1988 by Prentice Hall International (UK) Ltd as the occam 2 Reference Manual. SGS-THOMSON Microelectronics Limited 1995. SGS-THOMSON Microelectronics reserves the right to make changes in specifications at any time and without notice. The information furnished by SGS-THOMSON Microelectronics in this publication is believed to be accurate, but no responsibility is assumed for its use, nor for any infringement of patents or other rights of third parties resulting from its use. No licence is granted under any patents, trademarks or other rights of SGS-THOMSON Microelectronics. The INMOS logo, INMOS, IMS and occam are registered trademarks of SGS-THOMSON Microelectronics Limited. Document number: 72 occ 45 03 All rights reserved. No part of this publication may be reproduced, stored in a retrival system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior permission, in writing, from SGS-THOMSON Microelectronics Limited. Contents Contents v Contents overview ix Preface xi Introduction 1 Syntax and program format 3 1 Primitive processes 5 1.1 Assignment 5 1.2 Communication 6 1.3 SKIP and STOP 7 2 Constructed processes 9 2.1 Sequence 9 2.2 Conditional 11 2.3 Selection 13 2.4 WHILE loop 14 2.5 Parallel 15 2.6 Alternation 19 2.7 Processes 24 3 Data types 25 3.1 Primitive data types 25 3.2 Named data types 26 3.3 Literals -
Floating-Point Exception Handing
ISO/IEC JTC1/SC22/WG5 N1372 Working draft of ISO/IEC TR 15580, second edition Information technology ± Programming languages ± Fortran ± Floating-point exception handing This page to be supplied by ISO. No changes from first edition, except for for mechanical things such as dates. i ISO/IEC TR 15580 : 1998(E) Draft second edition, 13th August 1999 ISO/IEC Foreword To be supplied by ISO. No changes from first edition, except for for mechanical things such as dates. ii ISO/IEC Draft second edition, 13th August 1999 ISO/IEC TR 15580: 1998(E) Introduction Exception handling is required for the development of robust and efficient numerical software. In particular, it is necessary in order to be able to write portable scientific libraries. In numerical Fortran programming, current practice is to employ whatever exception handling mechanisms are provided by the system/vendor. This clearly inhibits the production of fully portable numerical libraries and programs. It is particularly frustrating now that IEEE arithmetic (specified by IEEE 754-1985 Standard for binary floating-point arithmetic, also published as IEC 559:1989, Binary floating-point arithmetic for microprocessor systems) is so widely used, since built into it are the five conditions: overflow, invalid, divide-by-zero, underflow, and inexact. Our aim is to provide support for these conditions. We have taken the opportunity to provide support for other aspects of the IEEE standard through a set of elemental functions that are applicable only to IEEE data types. This proposal involves three standard modules: IEEE_EXCEPTIONS contains a derived type, some named constants of this type, and some simple procedures. -
Embedded System Introduction.Pdf
EmbeddedEmbedded SystemSystem IntroductionIntroduction ANDES Confidential WWW.ANDESTECH.COM Embedded System vs Desktop Page 2 相同點 CPU Storage I/O 相異點 Desktop ‧可執行多種功能 ‧作業系統對於系統資源的管理較為複雜 Embedded System ‧執行特定功能 ‧作業系統對於系統資源的管理較為簡單 Page 3 SystemSystem LayerLayer Application Application Operating System Operating System Firmware Firmware Firmware Hardware Hardware Hardware Desktop computer Complex embedded Simple embedded computer computer Page 4 HardwareHardware ArchitectureArchitecture DesktopDesktop ComputerComputer SystemSystem HardwareHardware ArchitectureArchitecture CPU AGP Slot North Bridge Memory PCI Interface USB Interface South Bridge IDE Interface System BIOS ISA Interface Super IO Port Page 5 HardwareHardware ArchitectureArchitecture EmbeddedEmbedded SystemSystem ComputerComputer HardwareHardware ArchitectureArchitecture ADC CPU SPI Digital I/O ROM IIC Host RAM Timers Computer UART Bus Interface Memory Network Interface I/O Page 6 What is the Embedded System? Page 7 IntroductionIntroduction Challenges in embedded system design. Design methodologies. Page 8 EmbeddedEmbedded SystemSystem ?? •An embedded system is a special-purpose computer system designed to perform one or a few dedicated functions •with real-time computing constraints • include hardware, software and mechanical parts Page 9 EmbeddingEmbedding aa computercomputer Page 10 ComponentsComponents ofof anan embeddedembedded systemsystem Characteristics Low power Closed operating environment Cost sensitive Page 11 ComponentsComponents ofof anan embeddedembedded -
14 Los Alamos National Laboratory
14 Los Alamos National Laboratory Every year for the past 17 years, the director of Los Alamos alternative for assessing the safety, reliability, and perfor- National Laboratory has had a legally required task: write mance of the stockpile: virtual-world simulations. a letter—a personal assessment of Los Alamos–designed warheads and bombs in the U.S. nuclear stockpile. Th i s I, Iceberg letter is sent to the secretaries of Energy and Defense and to Hollywood movies such as the Matrix series or I, Robot the Nuclear Weapons Council. Th rough them the letter goes typically portray supercomputers as massive, room-fi lling to the president of the United States. machines that churn out answers to the most complex questions—all by themselves. In fact, like the director’s Th e technical basis for the director’s assessment comes from Annual Assessment Letter, supercomputers are themselves the Laboratory’s ongoing execution of the nation’s Stockpile the tip of an iceberg. Stewardship Program; Los Alamos’ mission is to study its portion of the aging stockpile, fi nd any problems, and address them. And for the past 17 years, the director’s letter has said, Without people, a supercomputer in eff ect, that any problems that have arisen in Los Alamos would be no more than a humble jumble weapons are being addressed and resolved without the need for full-scale underground nuclear testing. of wires, bits, and boxes. When it comes to the Laboratory’s work on the annual assess- Although these rows of huge machines are the most visible ment, the director’s letter is just the tip of the iceberg. -
Mark Vie Controller Standard Block Library for Public Disclosure Contents
GEI-100682AC Mark* VIe Controller Standard Block Library These instructions do not purport to cover all details or variations in equipment, nor to provide for every possible contingency to be met during installation, operation, and maintenance. The information is supplied for informational purposes only, and GE makes no warranty as to the accuracy of the information included herein. Changes, modifications, and/or improvements to equipment and specifications are made periodically and these changes may or may not be reflected herein. It is understood that GE may make changes, modifications, or improvements to the equipment referenced herein or to the document itself at any time. This document is intended for trained personnel familiar with the GE products referenced herein. Public – This document is approved for public disclosure. GE may have patents or pending patent applications covering subject matter in this document. The furnishing of this document does not provide any license whatsoever to any of these patents. GE provides the following document and the information included therein as is and without warranty of any kind, expressed or implied, including but not limited to any implied statutory warranty of merchantability or fitness for particular purpose. For further assistance or technical information, contact the nearest GE Sales or Service Office, or an authorized GE Sales Representative. Revised: July 2018 Issued: Sept 2005 © 2005 – 2018 General Electric Company. ___________________________________ * Indicates a trademark of General -
Adaptivfloat: a Floating-Point Based Data Type for Resilient Deep Learning Inference
ADAPTIVFLOAT:AFLOATING-POINT BASED DATA TYPE FOR RESILIENT DEEP LEARNING INFERENCE Thierry Tambe 1 En-Yu Yang 1 Zishen Wan 1 Yuntian Deng 1 Vijay Janapa Reddi 1 Alexander Rush 2 David Brooks 1 Gu-Yeon Wei 1 ABSTRACT Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low word sizes as their shrinking dynamic ranges cannot adequately capture the wide data distributions commonly seen in sequence transduction models. We present AdaptivFloat, a floating-point inspired number representation format for deep learning that dynamically maximizes and optimally clips its available dynamic range, at a layer granularity, in order to create faithful encoding of neural network parameters. AdaptivFloat consistently produces higher inference accuracies compared to block floating-point, uniform, IEEE-like float or posit encodings at very low precision (≤ 8-bit) across a diverse set of state-of-the-art neural network topologies. And notably, AdaptivFloat is seen surpassing baseline FP32 performance by up to +0.3 in BLEU score and -0.75 in word error rate at weight bit widths that are ≤ 8-bit. Experimental results on a deep neural network (DNN) hardware accelerator, exploiting AdaptivFloat logic in its computational datapath, demonstrate per-operation energy and area that is 0.9× and 1.14×, respectively, that of equivalent bit width integer-based accelerator variants. (a) ResNet-50 Weight Histogram (b) Inception-v3 Weight Histogram 1 INTRODUCTION 105 Max Weight: 1.32 105 Max Weight: 1.27 Min Weight: -0.78 Min Weight: -1.20 4 4 Deep learning approaches have transformed representation 10 10 103 103 learning in a multitude of tasks. -
Advanced Engineering Mathematics
5 Linear Systems of ODEs 5.1 Systems of ODEs In a sense, Chapter 5 equals Chapter 2 “plus” Chapter 3, in the sense that Chapter 5 combines use of matrix theory and ordinary differential equation (ODE) methods. When we have more than one linear ODE, results from matrix theory turn out to be useful. Example 5.1 For the circuit shown in Figure 5.1, let v(t) be the voltage drop across the capacitor and I(t) be the loop current. The input V(t) is a given function. Assume, as usual, that L, R, and C are constants. Write down a system of ODEs in R2 that models this circuit. Method: The series RLC circuit shown in Figure 5.1 is analogous to the DC series RLC circuit discussed near the end of Section 3.3. The first ODE models the voltage drop across the capacitor being v(t) = 1 q(t),whereq(t) is the charge on the capacitor and C ˙ q˙(t) = I(t). The second ODE in the system is Kirchhoff’s voltage law, LI(t)+RI(t)+v(t) = V(t), after dividing through by L. The system is ⎧ ⎫ ⎨˙( ) = 1 ( ) ⎬ v t C I t ⎩ ⎭ . (5.1) ˙( ) = 1 ( ( ) − ( ) − ( )) I t L V t RI t v t More generally, consider a system of two ODEs in unknowns x1(t), x2(t): ⎧ ⎫ ˙ ⎨x1(t) = F1 t, x1(t), x2(t) ⎬ ⎩ ⎭ . (5.2) ˙ x2(t) = F2 t, x1(t), x2(t) A special case is ⎧ ⎫ ˙ ⎨x1(t) = a11(t)x1 + a12(t)x2 + f1(t)⎬ ⎩ ⎭ , (5.3) ˙ x2(t) = a21(t)x1 + a22(t)x2 + f2(t) which is called a linear system. -
DRAFT1.2 Methods
HL7 v3.0 Data Types Specification - Version 0.9 Table of Contents Abstract . 1 1 Introduction . 2 1.1 Goals . 3 DRAFT1.2 Methods . 6 1.2.1 Analysis of Semantic Fields . 7 1.2.2 Form of Data Type Definitions . 10 1.2.3 Generalized Types . 11 1.2.4 Generic Types . 12 1.2.5 Collections . 15 1.2.6 The Meta Model . 18 1.2.7 Implicit Type Conversion . 22 1.2.8 Literals . 26 1.2.9 Instance Notation . 26 1.2.10 Typus typorum: Boolean . 28 1.2.11 Incomplete Information . 31 1.2.12 Update Semantics . 33 2 Text . 36 2.1 Introduction . 36 2.1.1 From Characters to Strings . 36 2.1.2 Display Properties . 37 2.1.3 Encoding of appearance . 37 2.1.4 From appearance of text to multimedial information . 39 2.1.5 Pulling the pieces together . 40 2.2 Character String . 40 2.2.1 The Unicode . 41 2.2.2 No Escape Sequences . 42 2.2.3 ITS Responsibilities . 42 2.2.4 HL7 Applications are "Black Boxes" . 43 2.2.5 No Penalty for Legacy Systems . 44 2.2.6 Unicode and XML . 47 2.3 Free Text . 47 2.3.1 Multimedia Enabled Free Text . 48 2.3.2 Binary Data . 55 2.3.3 Outstanding Issues . 57 3 Things, Concepts, and Qualities . 58 3.1 Overview of the Problem Space . 58 3.1.1 Concept vs. Instance . 58 3.1.2 Real World vs. Artificial Technical World . 59 3.1.3 Segmentation of the Semantic Field . -
Hardware Components and Internal PC Connections
Technological University Dublin ARROW@TU Dublin Instructional Guides School of Multidisciplinary Technologies 2015 Computer Hardware: Hardware Components and Internal PC Connections Jerome Casey Technological University Dublin, [email protected] Follow this and additional works at: https://arrow.tudublin.ie/schmuldissoft Part of the Engineering Education Commons Recommended Citation Casey, J. (2015). Computer Hardware: Hardware Components and Internal PC Connections. Guide for undergraduate students. Technological University Dublin This Other is brought to you for free and open access by the School of Multidisciplinary Technologies at ARROW@TU Dublin. It has been accepted for inclusion in Instructional Guides by an authorized administrator of ARROW@TU Dublin. For more information, please contact [email protected], [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License Higher Cert/Bachelor of Technology – DT036A Computer Systems Computer Hardware – Hardware Components & Internal PC Connections: You might see a specification for a PC 1 such as "containing an Intel i7 Hexa core processor - 3.46GHz, 3200MHz Bus, 384 KB L1 cache, 1.5MB L2 cache, 12 MB L3 cache, 32nm process technology; 4 gigabytes of RAM, ATX motherboard, Windows 7 Home Premium 64-bit operating system, an Intel® GMA HD graphics card, a 500 gigabytes SATA hard drive (5400rpm), and WiFi 802.11 bgn". This section aims to discuss a selection of hardware parts, outline common metrics and specifications