Xilinx UG925 Zynq-7000 All Programmable Soc ZC702 Base
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Lecture 04 Linear Structures Sort
Algorithmics (6EAP) MTAT.03.238 Linear structures, sorting, searching, etc Jaak Vilo 2018 Fall Jaak Vilo 1 Big-Oh notation classes Class Informal Intuition Analogy f(n) ∈ ο ( g(n) ) f is dominated by g Strictly below < f(n) ∈ O( g(n) ) Bounded from above Upper bound ≤ f(n) ∈ Θ( g(n) ) Bounded from “equal to” = above and below f(n) ∈ Ω( g(n) ) Bounded from below Lower bound ≥ f(n) ∈ ω( g(n) ) f dominates g Strictly above > Conclusions • Algorithm complexity deals with the behavior in the long-term – worst case -- typical – average case -- quite hard – best case -- bogus, cheating • In practice, long-term sometimes not necessary – E.g. for sorting 20 elements, you dont need fancy algorithms… Linear, sequential, ordered, list … Memory, disk, tape etc – is an ordered sequentially addressed media. Physical ordered list ~ array • Memory /address/ – Garbage collection • Files (character/byte list/lines in text file,…) • Disk – Disk fragmentation Linear data structures: Arrays • Array • Hashed array tree • Bidirectional map • Heightmap • Bit array • Lookup table • Bit field • Matrix • Bitboard • Parallel array • Bitmap • Sorted array • Circular buffer • Sparse array • Control table • Sparse matrix • Image • Iliffe vector • Dynamic array • Variable-length array • Gap buffer Linear data structures: Lists • Doubly linked list • Array list • Xor linked list • Linked list • Zipper • Self-organizing list • Doubly connected edge • Skip list list • Unrolled linked list • Difference list • VList Lists: Array 0 1 size MAX_SIZE-1 3 6 7 5 2 L = int[MAX_SIZE] -
Unrolled Linked Lists
CSCI-1200 Data Structures | Fall 2014 Homework 5 | Unrolled Linked Lists In this assignment you will modify the dslist class from Lecture 11 to implement an unrolled linked list data structure. This data structure is very similar to a standard doubly linked list, except that more than one element may be stored at each node. This data structure can have performance advantages (both in memory and running time) over a standard linked list when storing small items and can be used to better align data in the cache. You're welcome to read more about unrolled linked lists on Wikipedia (but you're not allowed to search for or use any code or pseudocode that might be available online!). Please read the entire handout before beginning your implementation. To complete the assignment you will modify the helper classes (Node and list_iterator) as well as the main class, dslist, which will be renamed \UnrolledLL". Below is a picture of the relationships between the classes (compare this to the picture of the dslist class from Lecture 11): UnrolledLL<int> head: tail: size: 14 list_iterator<int> ptr: offset: 3 Node<int> Node<int> Node<int> prev: NULL prev: prev: num_elements: 6 num_elements: 6 num_elements: 2 elements: 10 11 12 13 14 15elements: 16 17 1819 20 21 elements: 22 23 next: next: next: NULL Each Node object contains a fixed size array (size = 6 in the above example) that will store 1 or more elements from the list. The elements are ordered from left to right. From the outside, this unrolled linked list should perform exactly like an STL list containing the numbers 10 through 23 in sorted order. -
Meant to Provoke Thought Regarding the Current "Software Crisis" at the Time
1 www.onlineeducation.bharatsevaksamaj.net www.bssskillmission.in DATA STRUCTURES Topic Objective: At the end of this topic student will be able to: At the end of this topic student will be able to: Learn about software engineering principles Discover what an algorithm is and explore problem-solving techniques Become aware of structured design and object-oriented design programming methodologies Learn about classes Learn about private, protected, and public members of a class Explore how classes are implemented Become aware of Unified Modeling Language (UML) notation Examine constructors and destructors Learn about the abstract data type (ADT) Explore how classes are used to implement ADT Definition/Overview: Software engineering is the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software, and the study of these approaches. That is the application of engineering to software. The term software engineering first appeared in the 1968 NATO Software Engineering Conference and WWW.BSSVE.INwas meant to provoke thought regarding the current "software crisis" at the time. Since then, it has continued as a profession and field of study dedicated to creating software that is of higher quality, cheaper, maintainable, and quicker to build. Since the field is still relatively young compared to its sister fields of engineering, there is still much work and debate around what software engineering actually is, and if it deserves the title engineering. It has grown organically out of the limitations of viewing software as just programming. Software development is a term sometimes preferred by practitioners in the industry who view software engineering as too heavy-handed and constrictive to the malleable process of creating software. -
Open Data Structures (In Pseudocode)
Open Data Structures (in pseudocode) Edition 0.1Gβ Pat Morin Contents Acknowledgments ix Why This Book? xi 1 Introduction 1 1.1 The Need for Efficiency ..................... 2 1.2 Interfaces ............................. 4 1.2.1 The Queue, Stack, and Deque Interfaces . 5 1.2.2 The List Interface: Linear Sequences . 6 1.2.3 The USet Interface: Unordered Sets .......... 8 1.2.4 The SSet Interface: Sorted Sets ............. 8 1.3 Mathematical Background ................... 9 1.3.1 Exponentials and Logarithms . 10 1.3.2 Factorials ......................... 11 1.3.3 Asymptotic Notation . 12 1.3.4 Randomization and Probability . 15 1.4 The Model of Computation ................... 18 1.5 Correctness, Time Complexity, and Space Complexity . 19 1.6 Code Samples .......................... 21 1.7 List of Data Structures ..................... 23 1.8 Discussion and Exercises .................... 23 2 Array-Based Lists 31 2.1 ArrayStack: Fast Stack Operations Using an Array . 32 2.1.1 The Basics ........................ 32 2.1.2 Growing and Shrinking . 35 2.1.3 Summary ......................... 37 Contents 2.2 FastArrayStack: An Optimized ArrayStack . 37 2.3 ArrayQueue: An Array-Based Queue . 38 2.3.1 Summary ......................... 41 2.4 ArrayDeque: Fast Deque Operations Using an Array . 42 2.4.1 Summary ......................... 44 2.5 DualArrayDeque: Building a Deque from Two Stacks . 44 2.5.1 Balancing ......................... 48 2.5.2 Summary ......................... 50 2.6 RootishArrayStack: A Space-Efficient Array Stack . 50 2.6.1 Analysis of Growing and Shrinking . 55 2.6.2 Space Usage ....................... 55 2.6.3 Summary ......................... 56 2.7 Discussion and Exercises .................... 57 3 Linked Lists 61 3.1 SLList: A Singly-Linked List . -
Saturation in Lock-Based Concurrent Data Structures Ph.D
SATURATION IN LOCK-BASED CONCURRENT DATA STRUCTURES by Kenneth Joseph Platz APPROVED BY SUPERVISORY COMMITTEE: S. Venkatesan, Co-Chair Neeraj Mittal, Co-Chair Ivor Page Cong Liu Copyright c 2017 Kenneth Joseph Platz All rights reserved This dissertation is dedicated to my wife and my parents. They have always believed in me even when I did not. SATURATION IN LOCK-BASED CONCURRENT DATA STRUCTURES by KENNETH JOSEPH PLATZ, BS, MS DISSERTATION Presented to the Faculty of The University of Texas at Dallas in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY IN COMPUTER SCIENCE THE UNIVERSITY OF TEXAS AT DALLAS December 2017 ACKNOWLEDGMENTS I would like to first of all thank my wife Tracy for having the patience and fortitude to stand with me through this journey. Without her support, I could have never started this path much less completed it. Second, I would like to thank my supervising professors, Drs. \Venky" Venkatesan and Neeraj Mittal. They both provided me with frequent guidance and support throughout this entire journey. Finally, I would like to thank the rest of my dissertation committee, Drs. Ivor Page and Cong Liu. Dr. Page especially asked many pointed (and difficult) questions during my proposal defense, and that helped improve the quality of this work. November 2017 v SATURATION IN LOCK-BASED CONCURRENT DATA STRUCTURES Kenneth Joseph Platz, PhD The University of Texas at Dallas, 2017 Supervising Professors: S. Venkatesan, Co-Chair Neeraj Mittal, Co-Chair For over three decades, computer scientists enjoyed a \free lunch" inasmuch as they could depend on processor speeds doubling every three years. -
S.Y. B.Tech. 2017-Pattern
Bansilal Ramnath Agarwal Charitable Trust’s Vishwakarma Institute of Information Technology, Pune-48 (An Autonomous Institute affiliated to Savitribai Phule Pune University) BansilalDepartment Ramnath of ComputerAgarwal Charitable Engineering Trust’s Vishwakarma Institute of Information Technology, Pune-48 (An Autonomous Institute affiliated to Savitribai Phule Pune University) CurriculumSyllabus for for SM.Y.. BTech..Tech. (Mechanical (Computer – Design Engineering) Engineering) Department of Computer Engineering S.Y.B.Tech.(Pattern 2017) Computer Engineering 1 Bansilal Ramnath Agarwal Charitable Trust’s Vishwakarma Institute of Information Technology, Pune-48 (An Autonomous Institute affiliated to Savitribai Phule Pune University) Department of Computer Engineering Vision and Mission of the Department • Vision “Excellence in the field of Computer Engineering for rendering services to the industry and society”. • Mission To empower our students for substantial contribution to economical, technological, entrepreneurial and social progress of the society. To strive for excellence at different levels and diverse dimensions in the field of computer engineering. To encourage students to pursue research and advanced studies for better adaptability towards globalization. Program Specific Outcomes (PSOs) At the end of program, students should be able to • PSO a: Use knowledge to write programs and integrate them with the hardware/software products in the domains of embedded systems, data Science, networking and web technology. • PSO b: Participate in planning and implement solutions to cater to business – specific requirements, displaying team dynamics and professional ethics. Program Outcomes (POs) At the end of program, students should be able to: 1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals and an engineering specialization to the solution of complex engineering problems. -
CSCI-1200 Data Structures — Fall 2018 Lecture 24 – Miscellaneous Data Structures
CSCI-1200 Data Structures | Fall 2018 Lecture 24 { Miscellaneous Data Structures Review from Lecture 23 • STL's for_each, STL's find_if, Function Objects, a.k.a. Functors • Hash table collision resolution: separate chaining vs open addressing • STL's unordered_set (and unordered_map) • Using a hash table to implement a set/map 24.1 The Basic Data Structures This term we've covered a number of core data structures. These structures have fundamentally different memory layouts. These data structures are classic, and are not unique to C++. • array / vector • linked list • binary search tree • binary heap / priority queue • hash table 24.2 A Few Variants of the Basic Data Structures Many variants and advanced extensions and hybrid versions of these data structures are possible. Different applications with different requirements and patterns of data and data sizes and computer hardware will benefit from or leverage different aspects of these variants. This term we've already discussed / implemented a number of data structure variants: • single vs. doubly linked lists using more memory can improve convenience and running time for key operations • dummy nodes or circular linked lists { can reduce need for special case / corner case code • linked grid/matrix (Homework 5) { 2-dimensional linked list with efficient merge/split • bi-directional map (Homework 8) { 2 BSTs allow efficient find/insert/erase on both keys and values • red-black tree { an algorithm to automatically balance a binary search tree • stack and queue { restricted/reduced(!) set of operations on array/vector and list • hash table: separate chaining vs open addressing { reduce memory and avoid pointer dereferencing • priority queue with backpointers (Homework 10) { when you need to update data already in the structure We'll discuss just a few additional variants today. -
ARM Cortex-A Series Programmer's Guide
ARM® Cortex™-A Series Version: 4.0 Programmer’s Guide Copyright © 2011 – 2013 ARM. All rights reserved. ARM DEN0013D (ID012214) ARM Cortex-A Series Programmer’s Guide Copyright © 2011 – 2013 ARM. All rights reserved. Release Information The following changes have been made to this book. Change history Date Issue Confidentiality Change 25 March 2011 A Non-Confidential First release 10 August 2011 B Non-Confidential Second release. Updated to include Virtualization, Cortex-A15 processor, and LPAE. Corrected and revised throughout 25 June 2012 C Non-Confidential Updated to include Cortex-A7 processor, and big.LITTLE. Index added. Corrected and revised throughout. 22 January 2014 D Non-Confidential Updated to include Cortex-A12 processor, Cache Coherent Interconnect, expanded GIC coverage, Multi-core processors, Corrected and revised throughout. Proprietary Notice This Cortex-A Series Programmer’s Guide is protected by copyright and the practice or implementation of the information herein may be protected by one or more patents or pending applications. No part of this Cortex-A Series Programmer’s Guide may be reproduced in any form by any means without the express prior written permission of ARM. No license, express or implied, by estoppel or otherwise to any intellectual property rights is granted by this Cortex-A Series Programmer’s Guide. Your access to the information in this Cortex-A Series Programmer’s Guide is conditional upon your acceptance that you will not use or permit others to use the information for the purposes of determining whether implementations of the information herein infringe any third party patents. This Cortex-A Series Programmer’s Guide is provided “as is”. -
Introduction to Programming in Python Lessons
CLEVELAND INTRODUCTION TO PROGRAMMING IN PYTHON INSTITUTE OF LESSONS 9301C THROUGH 9310C ELECTRONICS Study Guide to accompany Fundamentals of Python – From First Programs Through Data Structures By Kenneth A. Lambert ISBN 10: 1423902181 ISBN 13: 9781423902188 This study guide contains excerpts from the Fundamentals of Python Instructor's resources and the book Fundamentals of Python – From First Programs through Data Structures by Kenneth Lambert © Copyright 2012 Cleveland Institute of Electronics All Rights Reserved / Printed in the United States of America FIRST EDITION / First Printing / March 2012 1 Contents Chat with Your Instructor ......................................................................................................... 3 Chapter 1 - Introduction ............................................................................................................ 4 Chapter 2 - Software Development, Data Types, and Expressions ...................................... 11 Python Lesson 9301C Exam ................................................................................................................. 17 Chapter 3 - Control Statements ................................................................................................. 2 Python Lesson 9302C Exam ................................................................................................................... 7 Chapter 4 - Strings and Text Files........................................................................................... 10 Chapter 5 - Lists and -
Hw2” and Put These Three files in It
Data Structure and Algorithm Homework #2 Due: 2:20pm, Tuesday, March 26, 2013 TA email: [email protected] === Homework submission instructions === • For Problem 1, submit your source code, a Makefile to compile the source, and a brief documentation to the SVN server (katrina.csie.ntu.edu.tw). You should create a new folder \hw2" and put these three files in it. • The filenames of the source code, Makefile, and the documentation file should be \main.c", \Makefile", and \report.txt", respectively; you will get some penalties in your grade if your submission does not follow the naming rule. The documentation file should be in plain text format (.txt file). In the documentation file you should explain how your code works, and anything you would like to convey to the TAs. • For Problem 2 through 5, submit the answers via the SVN server (electronic copy) or to the TA at the beginning of class on the due date (hard copy). • Except the programming assignment, each student may only choose to submit the homework in only one way; either all in hard copies or all via SVN. If you submit your homework partially in one way and partially in the other way, you might only get the score of the part submitted as hard copies or the part submitted via SVN (the part that the grading TA chooses). • If you choose to submit the answers of the writing problems through SVN, please combine the answers of all writing problems into only ONE file in the pdf format, with the file name in the format of \hw2 [student ID].pdf" (e.g. -
Hashing in Other Applications
Outline Basics Hash Collision Hashing in other applications Data and File Structures Laboratory Hashing Malay Bhattacharyya Assistant Professor Machine Intelligence Unit Indian Statistical Institute, Kolkata September, 2018 Malay Bhattacharyya Data and File Structures Laboratory Outline Basics Hash Collision Hashing in other applications 1 Basics 2 Hash Collision 3 Hashing in other applications Malay Bhattacharyya Data and File Structures Laboratory Outline Basics Hash Collision Hashing in other applications What is hashing? Definition (Hashing) Hashing is the process of indexing and retrieving data items in a data structure to provide faster way (preferably O(1)) of finding the element using the hash function. Malay Bhattacharyya Data and File Structures Laboratory Note: The hashed values are kept in a data structure known as hash tables. Outline Basics Hash Collision Hashing in other applications Hash function Definition (Hash function) A hash function h projects a value from a set with many (or even an infinite number of) data items to a value from a set with a fixed number of (fewer) data elements. Malay Bhattacharyya Data and File Structures Laboratory Outline Basics Hash Collision Hashing in other applications Hash function Definition (Hash function) A hash function h projects a value from a set with many (or even an infinite number of) data items to a value from a set with a fixed number of (fewer) data elements. Note: The hashed values are kept in a data structure known as hash tables. Malay Bhattacharyya Data and File Structures Laboratory Definition (Preimage) For a hash value k∗ = h(k), k is defined as the preimage of k∗. -
Fundamental Data Structures Zuyd Hogeschool, ICT Contents
Fundamental Data Structures Zuyd Hogeschool, ICT Contents 1 Introduction 1 1.1 Abstract data type ........................................... 1 1.1.1 Examples ........................................... 1 1.1.2 Introduction .......................................... 2 1.1.3 Defining an abstract data type ................................. 2 1.1.4 Advantages of abstract data typing .............................. 5 1.1.5 Typical operations ...................................... 5 1.1.6 Examples ........................................... 6 1.1.7 Implementation ........................................ 7 1.1.8 See also ............................................ 8 1.1.9 Notes ............................................. 8 1.1.10 References .......................................... 8 1.1.11 Further ............................................ 9 1.1.12 External links ......................................... 9 1.2 Data structure ............................................. 9 1.2.1 Overview ........................................... 10 1.2.2 Examples ........................................... 10 1.2.3 Language support ....................................... 11 1.2.4 See also ............................................ 11 1.2.5 References .......................................... 11 1.2.6 Further reading ........................................ 11 1.2.7 External links ......................................... 12 2 Sequences 13 2.1 Array data type ............................................ 13 2.1.1 History ...........................................