Open Implementations and Metaobject Protocols
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Application of TRIE Data Structure and Corresponding Associative Algorithms for Process Optimization in GRID Environment
Application of TRIE data structure and corresponding associative algorithms for process optimization in GRID environment V. V. Kashanskya, I. L. Kaftannikovb South Ural State University (National Research University), 76, Lenin prospekt, Chelyabinsk, 454080, Russia E-mail: a [email protected], b [email protected] Growing interest around different BOINC powered projects made volunteer GRID model widely used last years, arranging lots of computational resources in different environments. There are many revealed problems of Big Data and horizontally scalable multiuser systems. This paper provides an analysis of TRIE data structure and possibilities of its application in contemporary volunteer GRID networks, including routing (L3 OSI) and spe- cialized key-value storage engine (L7 OSI). The main goal is to show how TRIE mechanisms can influence de- livery process of the corresponding GRID environment resources and services at different layers of networking abstraction. The relevance of the optimization topic is ensured by the fact that with increasing data flow intensi- ty, the latency of the various linear algorithm based subsystems as well increases. This leads to the general ef- fects, such as unacceptably high transmission time and processing instability. Logically paper can be divided into three parts with summary. The first part provides definition of TRIE and asymptotic estimates of corresponding algorithms (searching, deletion, insertion). The second part is devoted to the problem of routing time reduction by applying TRIE data structure. In particular, we analyze Cisco IOS switching services based on Bitwise TRIE and 256 way TRIE data structures. The third part contains general BOINC architecture review and recommenda- tions for highly-loaded projects. -
Metaobject Protocols: Why We Want Them and What Else They Can Do
Metaobject protocols: Why we want them and what else they can do Gregor Kiczales, J.Michael Ashley, Luis Rodriguez, Amin Vahdat, and Daniel G. Bobrow Published in A. Paepcke, editor, Object-Oriented Programming: The CLOS Perspective, pages 101 ¾ 118. The MIT Press, Cambridge, MA, 1993. © Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. Metaob ject Proto cols WhyWeWant Them and What Else They Can Do App ears in Object OrientedProgramming: The CLOS Perspective c Copyright 1993 MIT Press Gregor Kiczales, J. Michael Ashley, Luis Ro driguez, Amin Vahdat and Daniel G. Bobrow Original ly conceivedasaneat idea that could help solve problems in the design and implementation of CLOS, the metaobject protocol framework now appears to have applicability to a wide range of problems that come up in high-level languages. This chapter sketches this wider potential, by drawing an analogy to ordinary language design, by presenting some early design principles, and by presenting an overview of three new metaobject protcols we have designed that, respectively, control the semantics of Scheme, the compilation of Scheme, and the static paral lelization of Scheme programs. Intro duction The CLOS Metaob ject Proto col MOP was motivated by the tension b etween, what at the time, seemed liketwo con icting desires. The rst was to have a relatively small but p owerful language for doing ob ject-oriented programming in Lisp. The second was to satisfy what seemed to b e a large numb er of user demands, including: compatibility with previous languages, p erformance compara- ble to or b etter than previous implementations and extensibility to allow further exp erimentation with ob ject-oriented concepts see Chapter 2 for examples of directions in which ob ject-oriented techniques might b e pushed. -
Chapter 2 Basics of Scanning And
Chapter 2 Basics of Scanning and Conventional Programming in Java In this chapter, we will introduce you to an initial set of Java features, the equivalent of which you should have seen in your CS-1 class; the separation of problem, representation, algorithm and program – four concepts you have probably seen in your CS-1 class; style rules with which you are probably familiar, and scanning - a general class of problems we see in both computer science and other fields. Each chapter is associated with an animating recorded PowerPoint presentation and a YouTube video created from the presentation. It is meant to be a transcript of the associated presentation that contains little graphics and thus can be read even on a small device. You should refer to the associated material if you feel the need for a different instruction medium. Also associated with each chapter is hyperlinked code examples presented here. References to previously presented code modules are links that can be traversed to remind you of the details. The resources for this chapter are: PowerPoint Presentation YouTube Video Code Examples Algorithms and Representation Four concepts we explicitly or implicitly encounter while programming are problems, representations, algorithms and programs. Programs, of course, are instructions executed by the computer. Problems are what we try to solve when we write programs. Usually we do not go directly from problems to programs. Two intermediate steps are creating algorithms and identifying representations. Algorithms are sequences of steps to solve problems. So are programs. Thus, all programs are algorithms but the reverse is not true. -
C Programming: Data Structures and Algorithms
C Programming: Data Structures and Algorithms An introduction to elementary programming concepts in C Jack Straub, Instructor Version 2.07 DRAFT C Programming: Data Structures and Algorithms, Version 2.07 DRAFT C Programming: Data Structures and Algorithms Version 2.07 DRAFT Copyright © 1996 through 2006 by Jack Straub ii 08/12/08 C Programming: Data Structures and Algorithms, Version 2.07 DRAFT Table of Contents COURSE OVERVIEW ........................................................................................ IX 1. BASICS.................................................................................................... 13 1.1 Objectives ...................................................................................................................................... 13 1.2 Typedef .......................................................................................................................................... 13 1.2.1 Typedef and Portability ............................................................................................................. 13 1.2.2 Typedef and Structures .............................................................................................................. 14 1.2.3 Typedef and Functions .............................................................................................................. 14 1.3 Pointers and Arrays ..................................................................................................................... 16 1.4 Dynamic Memory Allocation ..................................................................................................... -
Bit Nove Signal Interface Processor
www.audison.eu bit Nove Signal Interface Processor POWER SUPPLY CONNECTION Voltage 10.8 ÷ 15 VDC From / To Personal Computer 1 x Micro USB Operating power supply voltage 7.5 ÷ 14.4 VDC To Audison DRC AB / DRC MP 1 x AC Link Idling current 0.53 A Optical 2 sel Optical In 2 wire control +12 V enable Switched off without DRC 1 mA Mem D sel Memory D wire control GND enable Switched off with DRC 4.5 mA CROSSOVER Remote IN voltage 4 ÷ 15 VDC (1mA) Filter type Full / Hi pass / Low Pass / Band Pass Remote OUT voltage 10 ÷ 15 VDC (130 mA) Linkwitz @ 12/24 dB - Butterworth @ Filter mode and slope ART (Automatic Remote Turn ON) 2 ÷ 7 VDC 6/12/18/24 dB Crossover Frequency 68 steps @ 20 ÷ 20k Hz SIGNAL STAGE Phase control 0° / 180° Distortion - THD @ 1 kHz, 1 VRMS Output 0.005% EQUALIZER (20 ÷ 20K Hz) Bandwidth @ -3 dB 10 Hz ÷ 22 kHz S/N ratio @ A weighted Analog Input Equalizer Automatic De-Equalization N.9 Parametrics Equalizers: ±12 dB;10 pole; Master Input 102 dBA Output Equalizer 20 ÷ 20k Hz AUX Input 101.5 dBA OPTICAL IN1 / IN2 Inputs 110 dBA TIME ALIGNMENT Channel Separation @ 1 kHz 85 dBA Distance 0 ÷ 510 cm / 0 ÷ 200.8 inches Input sensitivity Pre Master 1.2 ÷ 8 VRMS Delay 0 ÷ 15 ms Input sensitivity Speaker Master 3 ÷ 20 VRMS Step 0,08 ms; 2,8 cm / 1.1 inch Input sensitivity AUX Master 0.3 ÷ 5 VRMS Fine SET 0,02 ms; 0,7 cm / 0.27 inch Input impedance Pre In / Speaker In / AUX 15 kΩ / 12 Ω / 15 kΩ GENERAL REQUIREMENTS Max Output Level (RMS) @ 0.1% THD 4 V PC connections USB 1.1 / 2.0 / 3.0 Compatible Microsoft Windows (32/64 bit): Vista, INPUT STAGE Software/PC requirements Windows 7, Windows 8, Windows 10 Low level (Pre) Ch1 ÷ Ch6; AUX L/R Video Resolution with screen resize min. -
Abstract Data Types
Chapter 2 Abstract Data Types The second idea at the core of computer science, along with algorithms, is data. In a modern computer, data consists fundamentally of binary bits, but meaningful data is organized into primitive data types such as integer, real, and boolean and into more complex data structures such as arrays and binary trees. These data types and data structures always come along with associated operations that can be done on the data. For example, the 32-bit int data type is defined both by the fact that a value of type int consists of 32 binary bits but also by the fact that two int values can be added, subtracted, multiplied, compared, and so on. An array is defined both by the fact that it is a sequence of data items of the same basic type, but also by the fact that it is possible to directly access each of the positions in the list based on its numerical index. So the idea of a data type includes a specification of the possible values of that type together with the operations that can be performed on those values. An algorithm is an abstract idea, and a program is an implementation of an algorithm. Similarly, it is useful to be able to work with the abstract idea behind a data type or data structure, without getting bogged down in the implementation details. The abstraction in this case is called an \abstract data type." An abstract data type specifies the values of the type, but not how those values are represented as collections of bits, and it specifies operations on those values in terms of their inputs, outputs, and effects rather than as particular algorithms or program code. -
Common Lisp Object System Specification 3. Metaobject Protocol
Common Lisp Object System Specification 3. Metaobject Protocol This document was written by Daniel G. Bobrow, Linda G. DeMichiel, Richard P. Gabriel, Sonya E. Keene, Gregor Kiczales, and David A. Moon. Contributors to this document include Patrick Dussud, Kenneth Kahn, Jim Kempf, Larry Masinter, Mark Stefik, Daniel L. Weinreb, and Jon L White. CONTENTS CONTENTS ................................................... ............ 3–2 Status of Document ................................................... ........... 3–5 Terminology ................................................... ................ 3–6 Introduction ................................................... ................ 3–7 Class Organization in the CLOS Kernel ............................................... 3–9 The Classes in the CLOS Kernel ................................................... 3–10 standard-class ................................................... ............ 3–11 forward-referenced-class ................................................... ..... 3–11 built-in-class ................................................... ............. 3–11 structure-class ................................................... ............ 3–12 funcallable-standard-class ................................................... .... 3–12 standard-slot-description ................................................... .... 3–12 standard-class-slot-description ................................................... 3–12 standard-method ................................................... .......... 3–12 -
Lecture 2: Variables and Primitive Data Types
Lecture 2: Variables and Primitive Data Types MIT-AITI Kenya 2005 1 In this lecture, you will learn… • What a variable is – Types of variables – Naming of variables – Variable assignment • What a primitive data type is • Other data types (ex. String) MIT-Africa Internet Technology Initiative 2 ©2005 What is a Variable? • In basic algebra, variables are symbols that can represent values in formulas. • For example the variable x in the formula f(x)=x2+2 can represent any number value. • Similarly, variables in computer program are symbols for arbitrary data. MIT-Africa Internet Technology Initiative 3 ©2005 A Variable Analogy • Think of variables as an empty box that you can put values in. • We can label the box with a name like “Box X” and re-use it many times. • Can perform tasks on the box without caring about what’s inside: – “Move Box X to Shelf A” – “Put item Z in box” – “Open Box X” – “Remove contents from Box X” MIT-Africa Internet Technology Initiative 4 ©2005 Variables Types in Java • Variables in Java have a type. • The type defines what kinds of values a variable is allowed to store. • Think of a variable’s type as the size or shape of the empty box. • The variable x in f(x)=x2+2 is implicitly a number. • If x is a symbol representing the word “Fish”, the formula doesn’t make sense. MIT-Africa Internet Technology Initiative 5 ©2005 Java Types • Integer Types: – int: Most numbers you’ll deal with. – long: Big integers; science, finance, computing. – short: Small integers. -
Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code
Signedness-Agnostic Program Analysis: Precise Integer Bounds for Low-Level Code Jorge A. Navas, Peter Schachte, Harald Søndergaard, and Peter J. Stuckey Department of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia Abstract. Many compilers target common back-ends, thereby avoid- ing the need to implement the same analyses for many different source languages. This has led to interest in static analysis of LLVM code. In LLVM (and similar languages) most signedness information associated with variables has been compiled away. Current analyses of LLVM code tend to assume that either all values are signed or all are unsigned (except where the code specifies the signedness). We show how program analysis can simultaneously consider each bit-string to be both signed and un- signed, thus improving precision, and we implement the idea for the spe- cific case of integer bounds analysis. Experimental evaluation shows that this provides higher precision at little extra cost. Our approach turns out to be beneficial even when all signedness information is available, such as when analysing C or Java code. 1 Introduction The “Low Level Virtual Machine” LLVM is rapidly gaining popularity as a target for compilers for a range of programming languages. As a result, the literature on static analysis of LLVM code is growing (for example, see [2, 7, 9, 11, 12]). LLVM IR (Intermediate Representation) carefully specifies the bit- width of all integer values, but in most cases does not specify whether values are signed or unsigned. This is because, for most operations, two’s complement arithmetic (treating the inputs as signed numbers) produces the same bit-vectors as unsigned arithmetic. -
Kind of Quantity’
NIST Technical Note 2034 Defning ‘kind of quantity’ David Flater This publication is available free of charge from: https://doi.org/10.6028/NIST.TN.2034 NIST Technical Note 2034 Defning ‘kind of quantity’ David Flater Software and Systems Division Information Technology Laboratory This publication is available free of charge from: https://doi.org/10.6028/NIST.TN.2034 February 2019 U.S. Department of Commerce Wilbur L. Ross, Jr., Secretary National Institute of Standards and Technology Walter Copan, NIST Director and Undersecretary of Commerce for Standards and Technology Certain commercial entities, equipment, or materials may be identifed in this document in order to describe an experimental procedure or concept adequately. Such identifcation is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the entities, materials, or equipment are necessarily the best available for the purpose. National Institute of Standards and Technology Technical Note 2034 Natl. Inst. Stand. Technol. Tech. Note 2034, 7 pages (February 2019) CODEN: NTNOEF This publication is available free of charge from: https://doi.org/10.6028/NIST.TN.2034 NIST Technical Note 2034 1 Defning ‘kind of quantity’ David Flater 2019-02-06 This publication is available free of charge from: https://doi.org/10.6028/NIST.TN.2034 Abstract The defnition of ‘kind of quantity’ given in the International Vocabulary of Metrology (VIM), 3rd edition, does not cover the historical meaning of the term as it is most commonly used in metrology. Most of its historical meaning has been merged into ‘quantity,’ which is polysemic across two layers of abstraction. -
Data Structures, Buffers, and Interprocess Communication
Data Structures, Buffers, and Interprocess Communication We’ve looked at several examples of interprocess communication involving the transfer of data from one process to another process. We know of three mechanisms that can be used for this transfer: - Files - Shared Memory - Message Passing The act of transferring data involves one process writing or sending a buffer, and another reading or receiving a buffer. Most of you seem to be getting the basic idea of sending and receiving data for IPC… it’s a lot like reading and writing to a file or stdin and stdout. What seems to be a little confusing though is HOW that data gets copied to a buffer for transmission, and HOW data gets copied out of a buffer after transmission. First… let’s look at a piece of data. typedef struct { char ticker[TICKER_SIZE]; double price; } item; . item next; . The data we want to focus on is “next”. “next” is an object of type “item”. “next” occupies memory in the process. What we’d like to do is send “next” from processA to processB via some kind of IPC. IPC Using File Streams If we were going to use good old C++ filestreams as the IPC mechanism, our code would look something like this to write the file: // processA is the sender… ofstream out; out.open(“myipcfile”); item next; strcpy(next.ticker,”ABC”); next.price = 55; out << next.ticker << “ “ << next.price << endl; out.close(); Notice that we didn’t do this: out << next << endl; Why? Because the “<<” operator doesn’t know what to do with an object of type “item”. -
Metaclasses: Generative C++
Metaclasses: Generative C++ Document Number: P0707 R3 Date: 2018-02-11 Reply-to: Herb Sutter ([email protected]) Audience: SG7, EWG Contents 1 Overview .............................................................................................................................................................2 2 Language: Metaclasses .......................................................................................................................................7 3 Library: Example metaclasses .......................................................................................................................... 18 4 Applying metaclasses: Qt moc and C++/WinRT .............................................................................................. 35 5 Alternatives for sourcedefinition transform syntax .................................................................................... 41 6 Alternatives for applying the transform .......................................................................................................... 43 7 FAQs ................................................................................................................................................................. 46 8 Revision history ............................................................................................................................................... 51 Major changes in R3: Switched to function-style declaration syntax per SG7 direction in Albuquerque (old: $class M new: constexpr void M(meta::type target,