What Is a Data Structure? a Data Structure Is a Way of Organizing the Data So That the Data Can Be Used Efficiently
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
CS 106X, Lecture 21 Tries; Graphs
CS 106X, Lecture 21 Tries; Graphs reading: Programming Abstractions in C++, Chapter 18 This document is copyright (C) Stanford Computer Science and Nick Troccoli, licensed under Creative Commons Attribution 2.5 License. All rights reserved. Based on slides created by Keith Schwarz, Julie Zelenski, Jerry Cain, Eric Roberts, Mehran Sahami, Stuart Reges, Cynthia Lee, Marty Stepp, Ashley Taylor and others. Plan For Today • Tries • Announcements • Graphs • Implementing a Graph • Representing Data with Graphs 2 Plan For Today • Tries • Announcements • Graphs • Implementing a Graph • Representing Data with Graphs 3 The Lexicon • Lexicons are good for storing words – contains – containsPrefix – add 4 The Lexicon root the art we all arts you artsy 5 The Lexicon root the contains? art we containsPrefix? add? all arts you artsy 6 The Lexicon S T A R T L I N G S T A R T 7 The Lexicon • We want to model a set of words as a tree of some kind • The tree should be sorted in some way for efficient lookup • The tree should take advantage of words containing each other to save space and time 8 Tries trie ("try"): A tree structure optimized for "prefix" searches struct TrieNode { bool isWord; TrieNode* children[26]; // depends on the alphabet }; 9 Tries isWord: false a b c d e … “” isWord: true a b c d e … “a” isWord: false a b c d e … “ac” isWord: true a b c d e … “ace” 10 Reading Words Yellow = word in the trie A E H S / A E H S A E H S A E H S / / / / / / / / A E H S A E H S A E H S A E H S / / / / / / / / / / / / A E H S A E H S A E H S / / / / / / / -
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 ..................................................................................................... -
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. -
Introduction to Linked List: Review
Introduction to Linked List: Review Source: http://www.geeksforgeeks.org/data-structures/linked-list/ Linked List • Fundamental data structures in C • Like arrays, linked list is a linear data structure • Unlike arrays, linked list elements are not stored at contiguous location, the elements are linked using pointers Array vs Linked List Why Linked List-1 • Advantages over arrays • Dynamic size • Ease of insertion or deletion • Inserting a new element in an array of elements is expensive, because room has to be created for the new elements and to create room existing elements have to be shifted For example, in a system if we maintain a sorted list of IDs in an array id[]. id[] = [1000, 1010, 1050, 2000, 2040] If we want to insert a new ID 1005, then to maintain the sorted order, we have to move all the elements after 1000 • Deletion is also expensive with arrays until unless some special techniques are used. For example, to delete 1010 in id[], everything after 1010 has to be moved Why Linked List-2 • Drawbacks of Linked List • Random access is not allowed. • Need to access elements sequentially starting from the first node. So we cannot do binary search with linked lists • Extra memory space for a pointer is required with each element of the list Representation in C • A linked list is represented by a pointer to the first node of the linked list • The first node is called head • If the linked list is empty, then the value of head is null • Each node in a list consists of at least two parts 1. -
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”. -
University of Cape Town Declaration
The copyright of this thesis vests in the author. No quotation from it or information derived from it is to be published without full acknowledgementTown of the source. The thesis is to be used for private study or non- commercial research purposes only. Cape Published by the University ofof Cape Town (UCT) in terms of the non-exclusive license granted to UCT by the author. University Automated Gateware Discovery Using Open Firmware Shanly Rajan Supervisor: Prof. M.R. Inggs Co-supervisor: Dr M. Welz University of Cape Town Declaration I understand the meaning of plagiarism and declare that all work in the dissertation, save for that which is properly acknowledged, is my own. It is being submitted for the degree of Master of Science in Engineering in the University of Cape Town. It has not been submitted before for any degree or examination in any other university. Signature of Author . Cape Town South Africa May 12, 2013 University of Cape Town i Abstract This dissertation describes the design and implementation of a mechanism that automates gateware1 device detection for reconfigurable hardware. The research facilitates the pro- cess of identifying and operating on gateware images by extending the existing infrastruc- ture of probing devices in traditional software by using the chosen technology. An automated gateware detection mechanism was devised in an effort to build a software system with the goal to improve performance and reduce software development time spent on operating gateware pieces by reusing existing device drivers in the framework of the chosen technology. This dissertation first investigates the system design to see how each of the user specifica- tions set for the KAT (Karoo Array Telescope) project in [28] could be achieved in terms of design decisions, toolchain selection and software modifications. -
Subtyping Recursive Types
ACM Transactions on Programming Languages and Systems, 15(4), pp. 575-631, 1993. Subtyping Recursive Types Roberto M. Amadio1 Luca Cardelli CNRS-CRIN, Nancy DEC, Systems Research Center Abstract We investigate the interactions of subtyping and recursive types, in a simply typed λ-calculus. The two fundamental questions here are whether two (recursive) types are in the subtype relation, and whether a term has a type. To address the first question, we relate various definitions of type equivalence and subtyping that are induced by a model, an ordering on infinite trees, an algorithm, and a set of type rules. We show soundness and completeness between the rules, the algorithm, and the tree semantics. We also prove soundness and a restricted form of completeness for the model. To address the second question, we show that to every pair of types in the subtype relation we can associate a term whose denotation is the uniquely determined coercion map between the two types. Moreover, we derive an algorithm that, when given a term with implicit coercions, can infer its least type whenever possible. 1This author's work has been supported in part by Digital Equipment Corporation and in part by the Stanford-CNR Collaboration Project. Page 1 Contents 1. Introduction 1.1 Types 1.2 Subtypes 1.3 Equality of Recursive Types 1.4 Subtyping of Recursive Types 1.5 Algorithm outline 1.6 Formal development 2. A Simply Typed λ-calculus with Recursive Types 2.1 Types 2.2 Terms 2.3 Equations 3. Tree Ordering 3.1 Subtyping Non-recursive Types 3.2 Folding and Unfolding 3.3 Tree Expansion 3.4 Finite Approximations 4. -
Linux Kernel and Driver Development Training Slides
Linux Kernel and Driver Development Training Linux Kernel and Driver Development Training © Copyright 2004-2021, Bootlin. Creative Commons BY-SA 3.0 license. Latest update: October 9, 2021. Document updates and sources: https://bootlin.com/doc/training/linux-kernel Corrections, suggestions, contributions and translations are welcome! embedded Linux and kernel engineering Send them to [email protected] - Kernel, drivers and embedded Linux - Development, consulting, training and support - https://bootlin.com 1/470 Rights to copy © Copyright 2004-2021, Bootlin License: Creative Commons Attribution - Share Alike 3.0 https://creativecommons.org/licenses/by-sa/3.0/legalcode You are free: I to copy, distribute, display, and perform the work I to make derivative works I to make commercial use of the work Under the following conditions: I Attribution. You must give the original author credit. I Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under a license identical to this one. I For any reuse or distribution, you must make clear to others the license terms of this work. I Any of these conditions can be waived if you get permission from the copyright holder. Your fair use and other rights are in no way affected by the above. Document sources: https://github.com/bootlin/training-materials/ - Kernel, drivers and embedded Linux - Development, consulting, training and support - https://bootlin.com 2/470 Hyperlinks in the document There are many hyperlinks in the document I Regular hyperlinks: https://kernel.org/ I Kernel documentation links: dev-tools/kasan I Links to kernel source files and directories: drivers/input/ include/linux/fb.h I Links to the declarations, definitions and instances of kernel symbols (functions, types, data, structures): platform_get_irq() GFP_KERNEL struct file_operations - Kernel, drivers and embedded Linux - Development, consulting, training and support - https://bootlin.com 3/470 Company at a glance I Engineering company created in 2004, named ”Free Electrons” until Feb. -
4 Hash Tables and Associative Arrays
4 FREE Hash Tables and Associative Arrays If you want to get a book from the central library of the University of Karlsruhe, you have to order the book in advance. The library personnel fetch the book from the stacks and deliver it to a room with 100 shelves. You find your book on a shelf numbered with the last two digits of your library card. Why the last digits and not the leading digits? Probably because this distributes the books more evenly among the shelves. The library cards are numbered consecutively as students sign up, and the University of Karlsruhe was founded in 1825. Therefore, the students enrolled at the same time are likely to have the same leading digits in their card number, and only a few shelves would be in use if the leadingCOPY digits were used. The subject of this chapter is the robust and efficient implementation of the above “delivery shelf data structure”. In computer science, this data structure is known as a hash1 table. Hash tables are one implementation of associative arrays, or dictio- naries. The other implementation is the tree data structures which we shall study in Chap. 7. An associative array is an array with a potentially infinite or at least very large index set, out of which only a small number of indices are actually in use. For example, the potential indices may be all strings, and the indices in use may be all identifiers used in a particular C++ program.Or the potential indices may be all ways of placing chess pieces on a chess board, and the indices in use may be the place- ments required in the analysis of a particular game. -
CSE 307: Principles of Programming Languages Classes and Inheritance
OOP Introduction Type & Subtype Inheritance Overloading and Overriding CSE 307: Principles of Programming Languages Classes and Inheritance R. Sekar 1 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Topics 1. OOP Introduction 3. Inheritance 2. Type & Subtype 4. Overloading and Overriding 2 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Section 1 OOP Introduction 3 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding OOP (Object Oriented Programming) So far the languages that we encountered treat data and computation separately. In OOP, the data and computation are combined into an “object”. 4 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Benefits of OOP more convenient: collects related information together, rather than distributing it. Example: C++ iostream class collects all I/O related operations together into one central place. Contrast with C I/O library, which consists of many distinct functions such as getchar, printf, scanf, sscanf, etc. centralizes and regulates access to data. If there is an error that corrupts object data, we need to look for the error only within its class Contrast with C programs, where access/modification code is distributed throughout the program 5 / 52 OOP Introduction Type & Subtype Inheritance Overloading and Overriding Benefits of OOP (Continued) Promotes reuse. by separating interface from implementation. We can replace the implementation of an object without changing client code. Contrast with C, where the implementation of a data structure such as a linked list is integrated into the client code by permitting extension of new objects via inheritance. Inheritance allows a new class to reuse the features of an existing class. -
Data Structures Using “C”
DATA STRUCTURES USING “C” DATA STRUCTURES USING “C” LECTURE NOTES Prepared by Dr. Subasish Mohapatra Department of Computer Science and Application College of Engineering and Technology, Bhubaneswar Biju Patnaik University of Technology, Odisha SYLLABUS BE 2106 DATA STRUCTURE (3-0-0) Module – I Introduction to data structures: storage structure for arrays, sparse matrices, Stacks and Queues: representation and application. Linked lists: Single linked lists, linked list representation of stacks and Queues. Operations on polynomials, Double linked list, circular list. Module – II Dynamic storage management-garbage collection and compaction, infix to post fix conversion, postfix expression evaluation. Trees: Tree terminology, Binary tree, Binary search tree, General tree, B+ tree, AVL Tree, Complete Binary Tree representation, Tree traversals, operation on Binary tree-expression Manipulation. Module –III Graphs: Graph terminology, Representation of graphs, path matrix, BFS (breadth first search), DFS (depth first search), topological sorting, Warshall’s algorithm (shortest path algorithm.) Sorting and Searching techniques – Bubble sort, selection sort, Insertion sort, Quick sort, merge sort, Heap sort, Radix sort. Linear and binary search methods, Hashing techniques and hash functions. Text Books: 1. Gilberg and Forouzan: “Data Structure- A Pseudo code approach with C” by Thomson publication 2. “Data structure in C” by Tanenbaum, PHI publication / Pearson publication. 3. Pai: ”Data Structures & Algorithms; Concepts, Techniques & Algorithms