Data Structures and Algorithms for Game Developers Limited Warranty and Disclaimer of Liability

Total Page:16

File Type:pdf, Size:1020Kb

Data Structures and Algorithms for Game Developers Limited Warranty and Disclaimer of Liability DATA STRUCTURES AND ALGORITHMS FOR GAME DEVELOPERS LIMITED WARRANTY AND DISCLAIMER OF LIABILITY THE CD-ROM THAT ACCOMPANIES THE BOOK MAY BE USED ON A SINGLE PC ONLY. THE LICENSE DOES NOT PERMIT THE USE ON A NETWORK (OF ANY KIND). YOU FURTHER AGREE THAT THIS LICENSE GRANTS PERMISSION TO USE THE PRODUCTS CONTAINED HEREIN, BUT DOES NOT GIVE YOU RIGHT OF OWNERSHIP TO ANY OF THE CONTENT OR PRODUCT CONTAINED ON THIS CD-ROM. USE OF THIRD-PARTY SOFTWARE CONTAINED ON THIS CD-ROM IS LIMITED TO AND SUBJECT TO LICENSING TERMS FOR THE RESPECTIVE PRODUCTS. CHARLES RIVER MEDIA, INC. (“CRM”) AND/OR ANYONE WHO HAS BEEN INVOLVED IN THE WRITING, CREATION, OR PRODUCTION OF THE ACCOMPA- NYING CODE (“THE SOFTWARE”) OR THE THIRD-PARTY PRODUCTS CON- TAINED ON THE CD-ROM OR TEXTUAL MATERIAL IN THE BOOK, CANNOT AND DO NOT WARRANT THE PERFORMANCE OR RESULTS THAT MAY BE OBTAINED BY USING THE SOFTWARE OR CONTENTS OF THE BOOK. THE AUTHOR AND PUBLISHER HAVE USED THEIR BEST EFFORTS TO ENSURE THE ACCURACY AND FUNCTIONALITY OF THE TEXTUAL MATERIAL AND PROGRAMS CONTAINED HEREIN. WE HOWEVER, MAKE NO WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, REGARDING THE PERFORMANCE OF THESE PROGRAMS OR CON- TENTS. THE SOFTWARE IS SOLD “AS IS” WITHOUT WARRANTY (EXCEPT FOR DEFECTIVE MATERIALS USED IN MANUFACTURING THE DISK OR DUE TO FAULTY WORKMANSHIP). THE AUTHOR, THE PUBLISHER, DEVELOPERS OF THIRD-PARTY SOFTWARE, AND ANYONE INVOLVED IN THE PRODUCTION AND MANUFACTURING OF THIS WORK SHALL NOT BE LIABLE FOR DAMAGES OF ANY KIND ARISING OUT OF THE USE OF (OR THE INABILITY TO USE) THE PROGRAMS, SOURCE CODE, OR TEXTUAL MATERIAL CONTAINED IN THIS PUBLICATION. THIS INCLUDES, BUT IS NOT LIMITED TO, LOSS OF REVENUE OR PROFIT, OR OTHER INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OF THE PRODUCT. THE SOLE REMEDY IN THE EVENT OF A CLAIM OF ANY KIND IS EXPRESSLY LIMITED TO REPLACEMENT OF THE BOOK AND/OR CD-ROM, AND ONLY AT THE DISCRETION OF CRM. THE USE OF “IMPLIED WARRANTY” AND CERTAIN “EXCLUSIONS” VARIES FROM STATE TO STATE, AND MAY NOT APPLY TO THE PURCHASER OF THIS PRODUCT. DATA STRUCTURES AND ALGORITHMS FOR GAME DEVELOPERS ALLEN SHERROD CHARLES RIVER MEDIA Boston, Massachusetts Copyright 2007 Career & Professional Group, a division of Thomson Learning, Inc. Published by Charles River Media, an imprint of Thomson Learning Inc. All rights reserved. No part of this publication may be reproduced in any way, stored in a retrieval system of any type, or transmitted by any means or media, electronic or mechanical, including, but not limited to, photocopy, recording, or scanning, without prior permission in writing from the publisher. Cover Design: Tyler Creative CHARLES RIVER MEDIA 25 Thomson Place Boston, Massachusetts 02210 617-757-7900 617-757-7951 (FAX) [email protected] www.charlesriver.com This book is printed on acid-free paper. Allen Sherrod. Data Structures and algorithms for Game Developers. ISBN: 1-58450-495-1 ISBN-13: 978-1-58450-495-5 eISBN: 1-58450-663-6 All brand names and product names mentioned in this book are trademarks or service marks of their respective companies. Any omission or misuse (of any kind) of service marks or trademarks should not be regarded as intent to infringe on the property of others. The publisher recognizes and respects all marks used by companies, manufacturers, and developers as a means to distinguish their products. Library of Congress Cataloging-in-Publication Data Sherrod, Allen. Data structures and algorithms for game developers / Allen Sherrod. p. cm. ISBN-13: 978-1-58450-495-5 (hardback with cd-rom : alk. paper) ISBN-10: 1-58450-495-1 (hardback with cd-rom : alk. paper) 1. Computer games--Programming. 2. Data structures (Computer science) 3. Computer algorithms. I. Title. QA76.76.C672S535 2007 794.8'1526--dc22 2007006782 Printed in the United States of America 07 7 6 5 4 3 2 First Edition CHARLES RIVER MEDIA titles are available for site license or bulk purchase by institutions, user groups, corporations, etc. For additional information, please contact the Special Sales Department at 800-347-7707. Requests for replacement of a defective CD-ROM must be accompanied by the original disc, your mailing address, telephone number, date of purchase and purchase price. Please state the nature of the problem, and send the information to CHARLES RIVER MEDIA, 25 Thomson Place, Boston, Massachusetts 02210. CRM’s sole obligation to the purchaser is to replace the disc, based on defective materials or faulty workmanship, but not on the operation or functionality of the product. Contents Acknowledgments ix Introduction xi About the Author xvi 1Introduction to Data Structures 1 Data Structures and Algorithms 2 Data Structures in Games and Simulations 4 C++ versus Java and C# 6 The C++ STL 11 Template Classes and Functions 12 Big-O Notation 15 Summary 17 Chapter Review Questions 17 2 Arrays 19 The Data Structures Known as Arrays 20 Algorithms: Insertion and Deletion 25 Ordered Arrays 31 Algorithms: Basic Searches 33 STL Arrays 47 Bit Arrays 63 Summary 70 Chapter Review Questions 70 Programming Projects 71 3 Recursion 73 Recursion Defined 75 Triangular Numbers 81 Factorials 86 Summary 89 Chapter Review Questions 89 Programming Projects 90 v vi Contents 4 Introduction to Sorting 91 Introduction to Sorting 92 The Bubble Sort 93 The Selection Sort 98 The Insertion Sort 101 STL Sorting 105 The Merge Sort 109 Summary 115 Chapter Review Questions 116 Programming Projects 118 5 Link Lists 119 Introduction to Link Lists 120 Singly and Double-Ended Linked Lists 124 Doubly Linked Lists 135 STL Link Lists 141 Tips and Things to Remember When Using Link Lists 151 Summary 152 Chapter Review Questions 152 Programming Projects 153 6 Stacks and Queues 155 Introduction to Stacks 156 STL Stacks 168 Introduction to Queues 172 STL Queues 194 Summary 206 Chapter Review Questions 206 Programming Projects 207 7 Hash Tables 209 Introduction to Hash Tables 210 Hash Functions 213 Working with Hash Tables 215 Implementing Hash Tables 220 Nonstandard Hash Containers 242 Summary 250 Chapter Review Questions 251 Programming Projects 252 8 Advanced Sorting 253 Advanced Sorting Topics 254 Contents vii Shellsort 255 Partitioning 260 Quicksort 265 Radix Sort 275 Additional Types of Sorting 280 Summary 282 Chapter Review Questions 282 Programming Projects 284 9 Trees 285 Introduction to Trees 286 Tree Example 289 Binary Trees 298 k-dimensional Trees 315 Additional Types of Trees 322 Summary 324 Chapter Review Questions 324 Programming Projects 326 10 Heaps 327 Introduction to Heaps 328 Heap Sort 336 Priority Queues Using Heaps 339 STL Heap Functions 346 Summary 348 Chapter Review Questions 348 Programming Projects 350 11 Graphs 351 Introduction to Graphs 352 Searching with Graphs 357 Topological Sorting 376 Weighted Graphs 385 Artificial Intelligence 391 Summary 393 Chapter Review Questions 394 Programming Projects 396 12 Additional STL Algorithms 397 Strings 398 map and multimap 404 set and multiset 411 viii Contents STL Algorithms 415 Summary 427 Chapter Review Questions 428 Programming Projects 429 13 Scene Management 431 Introduction to Scene Management 432 Game Math 435 Scene Graphs 449 Binary Space Partitioning Trees 460 Quad-Trees and Octrees 466 Additional Management Topics 472 Summary 477 Chapter Review Questions 478 Programming Projects 479 14 Data Compression 481 Introduction to Data Compression 482 Introduction to Texture Compression 494 Introduction to Data Encryption 514 Summary 515 Chapter Review Questions 515 Programming Projects 517 15 Conclusions 519 Quick Review 520 The Next Step 524 Summary 525 Appendix A Additional Resources 527 Appendix B Chapter Review Question Answers 531 Appendix C OpenGL 539 Appendix D NonStandard Containers and Algorithms 543 Index Acknowledgments ith every book I write I learn a lot about myself as a writer and as a person. Along my journey I’ve received a lot of help and support that Wwent a long way to making each of my book projects possible. I would like to thank my friends, family, and the men and women at Charles River Media and Thomson Learning who believed in me from the beginning. I would also like to thank the readers of each of my books and the loyal visitors of www. UltimateGameProgramming.com. I would like to give a special thanks to Jenifer Niles of Charles River Media for believing in me and giving me this chance. ix This page intentionally left blank Introduction What to Expect in the First Edition Who this Book is For What You Need to Know Before Reading this Book The Software Needed for this Book How this Book Is Organized WHAT TO EXPECT IN THE FIRST EDITION The creation of a video game is no easy task for anyone to undertake. When it comes to programming, a lot of knowledge needs to be obtained to create an effi- cient and solid product. The game industry is a very competitive business, where games are defined heavily by the products that came before them. These products have raised the bar in terms of what is considered cutting-edge and standard in the industry. The more advanced games become, the more data is required to realize a game, especially from a graphics point of view. As games continue to push the en- velope of what can be done and as they continue to increase gamers’ expectations, it becomes harder to create top-of-the-line quality products. This book is the first edition of Data Structures for Game Developers. In this book you can expect to walk away with detailed knowledge about various data structures and the algorithms that can be performed on them. This combination is the key to creating real-time simulations that push the envelope on what is consid- ered cutting-edge.
Recommended publications
  • Advanced Data Structures
    Advanced Data Structures PETER BRASS City College of New York CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521880374 © Peter Brass 2008 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published in print format 2008 ISBN-13 978-0-511-43685-7 eBook (EBL) ISBN-13 978-0-521-88037-4 hardback Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate. Contents Preface page xi 1 Elementary Structures 1 1.1 Stack 1 1.2 Queue 8 1.3 Double-Ended Queue 16 1.4 Dynamical Allocation of Nodes 16 1.5 Shadow Copies of Array-Based Structures 18 2 Search Trees 23 2.1 Two Models of Search Trees 23 2.2 General Properties and Transformations 26 2.3 Height of a Search Tree 29 2.4 Basic Find, Insert, and Delete 31 2.5ReturningfromLeaftoRoot35 2.6 Dealing with Nonunique Keys 37 2.7 Queries for the Keys in an Interval 38 2.8 Building Optimal Search Trees 40 2.9 Converting Trees into Lists 47 2.10
    [Show full text]
  • An Evolutionary Approach for Sorting Algorithms
    ORIENTAL JOURNAL OF ISSN: 0974-6471 COMPUTER SCIENCE & TECHNOLOGY December 2014, An International Open Free Access, Peer Reviewed Research Journal Vol. 7, No. (3): Published By: Oriental Scientific Publishing Co., India. Pgs. 369-376 www.computerscijournal.org Root to Fruit (2): An Evolutionary Approach for Sorting Algorithms PRAMOD KADAM AND Sachin KADAM BVDU, IMED, Pune, India. (Received: November 10, 2014; Accepted: December 20, 2014) ABstract This paper continues the earlier thought of evolutionary study of sorting problem and sorting algorithms (Root to Fruit (1): An Evolutionary Study of Sorting Problem) [1]and concluded with the chronological list of early pioneers of sorting problem or algorithms. Latter in the study graphical method has been used to present an evolution of sorting problem and sorting algorithm on the time line. Key words: Evolutionary study of sorting, History of sorting Early Sorting algorithms, list of inventors for sorting. IntroDUCTION name and their contribution may skipped from the study. Therefore readers have all the rights to In spite of plentiful literature and research extent this study with the valid proofs. Ultimately in sorting algorithmic domain there is mess our objective behind this research is very much found in documentation as far as credential clear, that to provide strength to the evolutionary concern2. Perhaps this problem found due to lack study of sorting algorithms and shift towards a good of coordination and unavailability of common knowledge base to preserve work of our forebear platform or knowledge base in the same domain. for upcoming generation. Otherwise coming Evolutionary study of sorting algorithm or sorting generation could receive hardly information about problem is foundation of futuristic knowledge sorting problems and syllabi may restrict with some base for sorting problem domain1.
    [Show full text]
  • Fast As a Shadow, Expressive As a Tree: Hybrid Memory Monitoring for C
    Fast as a Shadow, Expressive as a Tree: Hybrid Memory Monitoring for C Nikolai Kosmatov1 with Arvid Jakobsson2, Guillaume Petiot1 and Julien Signoles1 [email protected] [email protected] SASEFOR, November 24, 2015 A.Jakobsson, N.Kosmatov, J.Signoles (CEA) Hybrid Memory Monitoring for C 2015-11-24 1 / 48 Outline Context and motivation Frama-C, a platform for analysis of C code Motivation The memory monitoring library An overview Patricia trie model Shadow memory based model The Hybrid model Design principles Illustrating example Dataflow analysis An overview How it proceeds Evaluation Conclusion and future work A.Jakobsson, N.Kosmatov, J.Signoles (CEA) Hybrid Memory Monitoring for C 2015-11-24 2 / 48 Context and motivation Frama-C, a platform for analysis of C code Outline Context and motivation Frama-C, a platform for analysis of C code Motivation The memory monitoring library An overview Patricia trie model Shadow memory based model The Hybrid model Design principles Illustrating example Dataflow analysis An overview How it proceeds Evaluation Conclusion and future work A.Jakobsson, N.Kosmatov, J.Signoles (CEA) Hybrid Memory Monitoring for C 2015-11-24 3 / 48 Context and motivation Frama-C, a platform for analysis of C code A brief history I 90's: CAVEAT, Hoare logic-based tool for C code at CEA I 2000's: CAVEAT used by Airbus during certification process of the A380 (DO-178 level A qualification) I 2002: Why and its C front-end Caduceus (at INRIA) I 2006: Joint project on a successor to CAVEAT and Caduceus I 2008: First public release of Frama-C (Hydrogen) I Today: Frama-C Sodium (v.11) I Multiple projects around the platform I A growing community of users.
    [Show full text]
  • Rockjit: Securing Just-In-Time Compilation Using Modular Control-Flow Integrity
    RockJIT: Securing Just-In-Time Compilation Using Modular Control-Flow Integrity Ben Niu Gang Tan Lehigh University Lehigh University 19 Memorial Dr West 19 Memorial Dr West Bethlehem, PA, 18015 Bethlehem, PA, 18015 [email protected] [email protected] ABSTRACT For performance, modern managed language implementations Managed languages such as JavaScript are popular. For perfor- adopt Just-In-Time (JIT) compilation. Instead of performing pure mance, modern implementations of managed languages adopt Just- interpretation, a JIT compiler dynamically compiles programs into In-Time (JIT) compilation. The danger to a JIT compiler is that an native code and performs optimization on the fly based on informa- attacker can often control the input program and use it to trigger a tion collected through runtime profiling. JIT compilation in man- vulnerability in the JIT compiler to launch code injection or JIT aged languages is the key to high performance, which is often the spraying attacks. In this paper, we propose a general approach only metric when comparing JIT engines, as seen in the case of called RockJIT to securing JIT compilers through Control-Flow JavaScript. Hereafter, we use the term JITted code for native code Integrity (CFI). RockJIT builds a fine-grained control-flow graph that is dynamically generated by a JIT compiler, and code heap for from the source code of the JIT compiler and dynamically up- memory pages that hold JITted code. dates the control-flow policy when new code is generated on the fly. In terms of security, JIT brings its own set of challenges. First, a Through evaluation on Google’s V8 JavaScript engine, we demon- JIT compiler is large and usually written in C/C++, which lacks strate that RockJIT can enforce strong security on a JIT compiler, memory safety.
    [Show full text]
  • Speculative Separation for Privatization and Reductions
    Speculative Separation for Privatization and Reductions Nick P. Johnson Hanjun Kim Prakash Prabhu Ayal Zaksy David I. August Princeton University, Princeton, NJ yIntel Corporation, Haifa, Israel fnpjohnso, hanjunk, pprabhu, [email protected] [email protected] Abstract Memory Layout Static Speculative Automatic parallelization is a promising strategy to improve appli- Speculative LRPD [22] R−LRPD [7] cation performance in the multicore era. However, common pro- Privateer (this work) gramming practices such as the reuse of data structures introduce Dynamic PD [21] artificial constraints that obstruct automatic parallelization. Privati- Polaris [29] ASSA [14] zation relieves these constraints by replicating data structures, thus Static Array Expansion [10] enabling scalable parallelization. Prior privatization schemes are Criterion DSA [31] RSSA [23] limited to arrays and scalar variables because they are sensitive to Privatization Manual Paralax [32] STMs [8, 18] the layout of dynamic data structures. This work presents Privateer, the first fully automatic privatization system to handle dynamic and Figure 1: Privatization Criterion and Memory Layout. recursive data structures, even in languages with unrestricted point- ers. To reduce sensitivity to memory layout, Privateer speculatively separates memory objects. Privateer’s lightweight runtime system contention and relaxes the program dependence structure by repli- validates speculative separation and speculative privatization to en- cating the reused storage locations, producing multiple copies in sure correct parallel execution. Privateer enables automatic paral- memory that support independent, concurrent access. Similarly, re- lelization of general-purpose C/C++ applications, yielding a ge- duction techniques relax ordering constraints on associative, com- omean whole-program speedup of 11.4× over best sequential ex- mutative operators by replacing (or expanding) storage locations.
    [Show full text]
  • CS2110 Lecture 28 Mar. 29, 2021
    CS2110 Lecture 28 Mar. 29, 2021 • Quiz 3 has been graded Score 1-4 5-8 9-12 13-16 16-20 # of people 0 4 4 3 6 Median: 13 High: 20 (four people) • Important schedule change: quiz 4 changed to April 23 • DS7 has been posted, due Wednesday by 5pM • It is easy but requires you to use the pylab module. So you need to use an IDE that includes pylab or figure out how to install pylab in IDLE or Wing or whatever you use. Attend DS toMorrow to get help with that if necessary Today • continue algorithM analysis – Appendix B of (non-interactive version of) textbook (or Ch 21 if you have printed version) • Start sorting Last tiMe discussed “RAM” Model used to count steps of program execution. Considering again the 4 + 3n steps for foo(n) def foo(n): i = 0 result = 0 while i <= n: result = result + i i = i + 1 return answer • I said that we usually ignore the 4. It turns out we are also usually happy to ignore the leading constant on the n. n is what's iMportant - the number of steps required grows linearly with n. • Throwing out those constants doesn't always Make sense - at "tuning" tiMe or other tiMes, we Might want/need to consider the constants. But in big picture coMparisons, it's often helpful and valid to siMplify things by ignoring theM. We’ll look at two More examples before forMalizing this throwing-away-stuff approach via Big-O notation. FroM last tiMe - when can we search quickly? • When the input is sorted.
    [Show full text]
  • Sorting Algorithm 1 Sorting Algorithm
    Sorting algorithm 1 Sorting algorithm In computer science, a sorting algorithm is an algorithm that puts elements of a list in a certain order. The most-used orders are numerical order and lexicographical order. Efficient sorting is important for optimizing the use of other algorithms (such as search and merge algorithms) that require sorted lists to work correctly; it is also often useful for canonicalizing data and for producing human-readable output. More formally, the output must satisfy two conditions: 1. The output is in nondecreasing order (each element is no smaller than the previous element according to the desired total order); 2. The output is a permutation, or reordering, of the input. Since the dawn of computing, the sorting problem has attracted a great deal of research, perhaps due to the complexity of solving it efficiently despite its simple, familiar statement. For example, bubble sort was analyzed as early as 1956.[1] Although many consider it a solved problem, useful new sorting algorithms are still being invented (for example, library sort was first published in 2004). Sorting algorithms are prevalent in introductory computer science classes, where the abundance of algorithms for the problem provides a gentle introduction to a variety of core algorithm concepts, such as big O notation, divide and conquer algorithms, data structures, randomized algorithms, best, worst and average case analysis, time-space tradeoffs, and lower bounds. Classification Sorting algorithms used in computer science are often classified by: • Computational complexity (worst, average and best behaviour) of element comparisons in terms of the size of the list . For typical sorting algorithms good behavior is and bad behavior is .
    [Show full text]
  • Download/Repository/Ivan Fratric.Pdf, 2012
    UC Irvine UC Irvine Electronic Theses and Dissertations Title Binary Recompilation via Dynamic Analysis and the Protection of Control and Data-flows Therein Permalink https://escholarship.org/uc/item/4gd0b9ht Author Nash, Joseph Michael Publication Date 2020 License https://creativecommons.org/licenses/by-sa/4.0/ 4.0 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, IRVINE Binary Recompilation via Dynamic Analysis and the Protection of Control and Data-flows Therein DISSERTATION submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Computer Science by Joseph Nash Dissertation Committee: Professor Michael Franz, Chair Professor Ardalan Amiri Sani Professor Alexander V. Veidenbaum 2020 Parts of Chapter3 c 2020 ACM Reprinted, with permission, from BinRec: Attack Surface Reduction Through Dynamic Binary Recovery., Anil Altinay, Joseph Nash, Taddeus Kroes, Prahbu Rajasekaran, Dixin Zhou, Adrian Dabrowski, David Gens, Yeoul Na, Stijn Volckaert, Herbert Bos, Cristiano Giuffrida, Michael Franz, in Proceedings of the Fifteenth EuroSys Conference , EUROSYS 2020. Parts of Chapter5 c 2018 Springer. Reprinted, with permission, from Hardware Assisted Randomization of Data, Brian Belleville, Hyungon Moon, Jangseop Shin, Dongil Hwang, Joseph M. Nash, Seonhwa Jung, Yeoul Na, Stijn Volckaert, Per Larsen, Yunheung Paek, Michael Franz , in Proceedings of the 21st International Symposium on Research in Attacks, Intrusions and Defenses, RAID 2018. Parts of Chapter4 c 2017 ACM. Reprinted, with permission, from Control-Flow Integrity: Precision, Security, and Performance, Nathan Burow, Scott A. Carr, Joseph Nash, Per Larsen, Michael Franz, Stefan Brunthaler, Mathias Payer. , in Proceedings of the 21st International Symposium on Research in Attacks, Intrusions and Defenses, ACM Computing Surveys 2017.
    [Show full text]
  • Evaluation of Sorting Algorithms, Mathematical and Empirical Analysis of Sorting Algorithms
    International Journal of Scientific & Engineering Research Volume 8, Issue 5, May-2017 86 ISSN 2229-5518 Evaluation of Sorting Algorithms, Mathematical and Empirical Analysis of sorting Algorithms Sapram Choudaiah P Chandu Chowdary M Kavitha ABSTRACT:Sorting is an important data structure in many real life applications. A number of sorting algorithms are in existence till date. This paper continues the earlier thought of evolutionary study of sorting problem and sorting algorithms concluded with the chronological list of early pioneers of sorting problem or algorithms. Latter in the study graphical method has been used to present an evolution of sorting problem and sorting algorithm on the time line. An extensive analysis has been done compared with the traditional mathematical methods of ―Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort. Observations have been obtained on comparing with the existing approaches of All Sorts. An “Empirical Analysis” consists of rigorous complexity analysis by various sorting algorithms, in which comparison and real swapping of all the variables are calculatedAll algorithms were tested on random data of various ranges from small to large. It is an attempt to compare the performance of various sorting algorithm, with the aim of comparing their speed when sorting an integer inputs.The empirical data obtained by using the program reveals that Quick sort algorithm is fastest and Bubble sort is slowest. Keywords: Bubble Sort, Insertion sort, Quick Sort, Merge Sort, Selection Sort, Heap Sort,CPU Time. Introduction In spite of plentiful literature and research in more dimension to student for thinking4. Whereas, sorting algorithmic domain there is mess found in this thinking become a mark of respect to all our documentation as far as credential concern2.
    [Show full text]
  • Practical Analysis of Framework-Intensive Applications
    PRACTICAL ANALYSIS OF FRAMEWORK-INTENSIVE APPLICATIONS by BRUNO DUFOUR A DISSERTATION SUBMITTED TO THE GRADUATE SCHOOL –NEW BRUNSWICK RUTGERS,THE STATE UNIVERSITY OF NEW JERSEY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE PROGRAM IN COMPUTER SCIENCE WRITTEN UNDER THE DIRECTION OF BARBARA G. RYDER AND APPROVED BY New Brunswick, New Jersey January 2010 ABSTRACT OF THE DISSERTATION Practical analysis of framework-intensive applications by BRUNO DUFOUR Dissertation director: Barbara G. Ryder Many modern applications (e.g., web applications) are composed of a relatively small amount of application code that calls a large number of third-party libraries and frame- works. Such framework-intensive systems typically exhibit different characteristics from traditional applications. Current tools and techniques are often inadequate in analyzing applications of such scale and complexity. Approaches based on static analysis suffer problems of insufficient scalability and/or insufficient precision. Purely dynamic analy- ses, introduce too much execution overhead, especially for production systems, or are too limited in the information gathered. The main contribution of this thesis is a new analysis paradigm, blended analysis, com- bines elements of static and dynamic analyses in order to enable analyses of framework- intensive applications that achieve good precision at a practical cost. This is accomplished by narrowing the focus of a static analysis to a set of executions of interest identified us- ing a lightweight dynamic analysis. We also present an optimization technique that further reduces the amount of code to be analyzed by removing infeasible basic blocks, and leads to significant increases in scalability and precision of the analysis.
    [Show full text]
  • Detecting Cacheable Data to Remove Bloat
    Cachetor: Detecting Cacheable Data to Remove Bloat Khanh Nguyen and Guoqing Xu University of California, Irvine, CA, USA {khanhtn1, guoqingx}@ics.uci.edu ABSTRACT 1. INTRODUCTION Modern object-oriented software commonly suffers from runtime Many applications suffer from chronic runtime bloat—excessive bloat that significantly affects its performance and scalability. Stud- memory usage and run-time work to accomplish simple tasks— ies have shown that one important pattern of bloat is the work re- that significantly affects scalability and performance. Our experi- peatedly done to compute the same data values. Very often the ence with dozens of large-scale, real-world applications [33, 35, cost of computation is very high and it is thus beneficial to mem- 36, 37] shows that a very important source of runtime bloat is the oize the invariant data values for later use. While this is a com- work repeatedly done to compute identical data values—if the com- mon practice in real-world development, manually finding invariant putation is expensive, significant performance improvement can data values is a daunting task during development and tuning. To be achieved by memoizing1 these values and avoiding computing help the developers quickly find such optimization opportunities for them many times. In fact, caching important data (instead of re- performance improvement, we propose a novel run-time profiling computing them) is already a well-known programming practice. tool, called Cachetor, which uses a combination of dynamic depen- For example, in the white paper “WebSphere Application Server dence profiling and value profiling to identify and report operations Development Best Practices for Performance and Scalability” [3], that keep generating identical data values.
    [Show full text]
  • Sorting Algorithm 1 Sorting Algorithm
    Sorting algorithm 1 Sorting algorithm A sorting algorithm is an algorithm that puts elements of a list in a certain order. The most-used orders are numerical order and lexicographical order. Efficient sorting is important for optimizing the use of other algorithms (such as search and merge algorithms) which require input data to be in sorted lists; it is also often useful for canonicalizing data and for producing human-readable output. More formally, the output must satisfy two conditions: 1. The output is in nondecreasing order (each element is no smaller than the previous element according to the desired total order); 2. The output is a permutation (reordering) of the input. Since the dawn of computing, the sorting problem has attracted a great deal of research, perhaps due to the complexity of solving it efficiently despite its simple, familiar statement. For example, bubble sort was analyzed as early as 1956.[1] Although many consider it a solved problem, useful new sorting algorithms are still being invented (for example, library sort was first published in 2006). Sorting algorithms are prevalent in introductory computer science classes, where the abundance of algorithms for the problem provides a gentle introduction to a variety of core algorithm concepts, such as big O notation, divide and conquer algorithms, data structures, randomized algorithms, best, worst and average case analysis, time-space tradeoffs, and upper and lower bounds. Classification Sorting algorithms are often classified by: • Computational complexity (worst, average and best behavior) of element comparisons in terms of the size of the list (n). For typical serial sorting algorithms good behavior is O(n log n), with parallel sort in O(log2 n), and bad behavior is O(n2).
    [Show full text]