DOCSLIB.ORG

Data Structures

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Data Structures KARNATAKA STATE OPEN UNIVERSITY DATA STRUCTURES BCA I SEMESTER SUBJECT CODE: BCA 04 SUBJECT TITLE: DATA STRUCTURES 1 STRUCTURE OF THE COURSE CONTENT BLOCK 1 PROBLEM SOLVING Unit 1: Top-down Design – Implementation Unit 2: Verification – Efficiency Unit 3: Analysis Unit 4: Sample Algorithm BLOCK 2 LISTS, STACKS AND QUEUES Unit 1: Abstract Data Type (ADT) Unit 2: List ADT Unit 3: Stack ADT Unit 4: Queue ADT BLOCK 3 TREES Unit 1: Binary Trees Unit 2: AVL Trees Unit 3: Tree Traversals and Hashing Unit 4: Simple implementations of Tree BLOCK 4 SORTING Unit 1: Insertion Sort Unit 2: Shell sort and Heap sort Unit 3: Merge sort and Quick sort Unit 4: External Sort BLOCK 5 GRAPHS Unit 1: Topological Sort Unit 2: Path Algorithms Unit 3: Prim‘s Algorithm Unit 4: Undirected Graphs – Bi-connectivity Books: 1. R. G. Dromey, ―How to Solve it by Computer‖ (Chaps 1-2), Prentice-Hall of India, 2002. 2. M. A. Weiss, ―Data Structures and Algorithm Analysis in C‖, 2nd ed, Pearson Education Asia, 2002. 3. Y. Langsam, M. J. Augenstein and A. M. Tenenbaum, ―Data Structures using C‖, Pearson Education Asia, 2004 4. Richard F. Gilberg, Behrouz A. Forouzan, ―Data Structures – A Pseudocode Approach with C‖, Thomson Brooks / COLE, 1998. 5. Aho, J. E. Hopcroft and J. D. Ullman, ―Data Structures and Algorithms‖, Pearson education Asia, 1983. 2 BLOCK I BLOCK I PROBLEM SOLVING Computer problem-solving can be summed up in one word – it is demanding! It is an intricate process requiring much thought, careful planning, logical precision, persistence, and attention to detail. At the same time it can be challenging, exciting, and satisfying experience with considerable room for personal creativity and expression. It computer problem=-solving is 3 approached in this spirit then the chances of success are greatly amplified. In the discussion which follows in this introductory chapter we will attempt to lay the foundations for our study of computer problem-solving. This block consists of the following units: Unit 1: Top-down Design – Implementation Unit 2: Verification – Efficiency Unit 3: Analysis Unit 4: Sample Algorithm UNIT 1 TOP-DOWN DESIGN – IMPLEMENTATION 4 STRUCTURE 1. 0 Aims and objectives 1.1 Introduction 1.2 Top- Down Design 1.3 Advantages of Adopting a Top-Down design 1.4 Implementation of Algorithms 1.5 Let Us Sum Up 1.6 Keywords 1.7 Questions for Discussion 1.8 Suggestion for Books Reading 1.0 AIMS AND OBJECTIVES At the end of this lesson, students should be able to demonstrate appropriate skills, and show an understanding of the following: Aims and objectives of Top-down design Introduction Top-down Design Implementation Advantages of this methodology 1.3 INTRODUCTION The primary goal in computer problem-solving is an algorithm which is capable of being implemented as a correct and efficient computer program. In our discussion leading up to the consideration of algorithm design we have been mostly concerned with the very broad aspects of problem solving. We now need consider those aspects of problem-solving. We now need to consider those aspects of problem-solving and algorithm design which are closer to the algorithm implementation. Once we have defined the problem to be solved and we have at least a vague idea of how to solve it, we can begin to bring to bear powerful techniques for designing algorithms. The key to being able to successfully design algorithms lies in being able to manage the inherent complexity of most problems that require computer solution. People as problem-solvers are only able to focus on, and comprehend at one time, a very limited span 5 of logic or instructions. A technique for algorithm design that tries to accommodate this human limitation is known as top-down design or stepwise refinement. 1.2 TOP-DOWN DESIGN Top-down design is a strategy that we can apply to take the solution of a computer problem from a vague outline to a precisely defined algorithm and program implementation. Top- down design provides us with a way of handling the inherent logical complexity and detail frequently encountered in computer algorithms. It allows us to build our solutions to a problem in a stepwise fashion. In this way, specific and complex details of the implementation are encountered only at the stage when we have done sufficient ground work on the overall structure and relationships among the various parts of the problem. Solution method where the problem is broken down into smaller sub-problems, which in turn are broken down into smaller sub-problems, continuing until each sub-problem can be solved in few steps. Problem-solving technique in which the problem is divided into sub problems; the process is applied to each sub problem Modules: Self-contained collection of steps that solve a problem or sub problem Abstract Step: An algorithmic step containing unspecified details Concrete Step: An algorithm step in which all details are specified 1.2.1 Example: Main Module Top Abstract (Main Program) Level 0 Level 1 Level 2 Level 3 Figure: An example of top-down design Process continues for as many levels as it takes to make every step concrete Bottom Particular Name of (sub) problem at one level becomes a module at next lower level 1.2.2 Breaking a problem into sub problems 6 Before we can apply top-down design to a problem we must first do the problem-solving ground work that gives us at least the broadest of outlines of our solution. Sometimes this might demand a lengthy and creative investigation into the problems while at the other times the problem description may in itself provide the necessary starting point for top-down design. The general outline may consist of single statement or a set of statements. Top-down design suggests that we take the general statements that we have about the solution, one at a time, and break them down into a set of more precisely defined subtasks. These subtasks should more accurately describe how the final goal is to be reached. With each splitting of a task into subtask it is essential that the way in which the subtasks need to interact with each other be precisely defined. Only in this way is it possible to preserve the overall structure of the solution to the problem. Preservation of the overall structure into the solution to a problem is important both for making the algorithm comprehensible and also for making it possible to prove the correctness of the solution. The process of repeatedly breaking a task down into subtasks and then each subtasks into still smaller subtasks must continue until the eventually end up with subtasks that can be implemented as program statements. For most algorithms we only need to go down to two or three levels although obviously for large software projects this is not true. The larger and more complex the problem, the more it will need to be broken down to be made tractable. The process of breaking down the solution to a problem into subtasks in the manner described results in an implement able set of subtasks that fit quiet naturally into block-structured languages like Pascal and Algol. There can therefore be a smooth and natural interface between the stepwise refined algorithm and the final program implementation—a highly desirable situation for keeping the implementation task as simple as possible. A General Example Planning a large party Figure: Sub dividing the party planning Problem o Create an address list that includes each person‘s name, address, telephone number, and e-mail address o This list should then be printed in alphabetical order o The names to be included in the list are on scraps of paper and business cards 7 1.2.3 Choice of suitable data structure: One of the most important decisions we have to make in formulating computer solutions to problems is the choice of appropriate data structures. General Outline Input Conditions Output Requirements Body of Algorithm Subtask 1 Subtask 2 Subtask 3 Subtask 1a Subtask 1b Subtask 2a Subtask 2b Figure: Schematic breakdown of a problem into subtasks as employed in top-down design. All programs operate on data and consequently the way the data is organized can have a profound effect on every aspect of the final solution. In particular, an inappropriate choice of data structure often leads to clumsy, inefficient, and difficult implementations. On the other hand, an appropriate choice usually leads to a simple, transparent, and efficient implementation. 1.2.4 Construction of loops In moving from general statements about the implementation towards sub-tasks that can be realized as computations, almost invariable we are led to a series of iterative constructs, or loops, and structures that are conditionally executed. These structures, together with input/output statements, computable expressions, and assignments, make up the heart of the program implementations. 1.2.5 Establishing initial conditions for loops: 8 To establish the initial conditions for a loop, a usually effective strategy is to set the loop variables to the values that would have to assume in order to solve the smallest problem associated with the loop. i:= 0 s:=0 Solution for n=0 1.2.6 Finding the iterative construct Once we have the conditions for solving the smallest problem the next step is to try to extend it to the next smallest problem (in this case when i=1). That is we want to build on the solution to the problem for i=0 to get the solution for i=1 The solution for n=1 is: i:= 0 s:=a[1] Solution for n=1 1.2.7 Termination of loops There are a number of ways in which loops can be terminated.
Load more

Recommended publications
  • An Aggregate Computing Approach to Self-Stabilizing Leader Election
    An Aggregate Computing Approach to Self-Stabilizing Leader Election Yuanqiu Mo Jacob Beal Soura Dasgupta University of Iowa Raytheon BBN Technologies University of Iowa⇤ Iowa City, Iowa 52242 Cambridge, MA, USA 02138 Iowa City, Iowa 52242 Email: [email protected] Email: [email protected] Email: [email protected] Abstract—Leader election is one of the core coordination 4" problems of distributed systems, and has been addressed in 3" 1" many different ways suitable for different classes of systems. It is unclear, however, whether existing methods will be effective 7" 1" for resilient device coordination in open, complex, networked 3" distributed systems like smart cities, tactical networks, personal 3" 0" networks and the Internet of Things (IoT). Aggregate computing 2" provides a layered approach to developing such systems, in which resilience is provided by a layer comprising a set of (a) G block (b) C block (c) T block adaptive algorithms whose compositions have been shown to cover a large class of coordination activities. In this paper, Fig. 1. Illustration of three basis block operators: (a) information-spreading (G), (b) information aggregation (C), and (c) temporary state (T) we show how a feedback interconnection of these basis set algorithms can perform distributed leader election resilient to device topology and position changes. We also characterize a key design parameter that defines some important performance a separation of concerns into multiple abstraction layers, much attributes: Too large a value impairs resilience to loss of existing like the OSI model for communication [2], factoring the leaders, while too small a value leads to multiple leaders.
    [Show full text]
  • Optimizing Sorting with Genetic Algorithms ∗
    Optimizing Sorting with Genetic Algorithms ∗ Xiaoming Li, Mar´ıa Jesus´ Garzaran´ and David Padua Department of Computer Science University of Illinois at Urbana-Champaign {xli15, garzaran, padua}@cs.uiuc.edu http://polaris.cs.uiuc.edu Abstract 1 Introduction Although compiler technology has been extraordinarily The growing complexity of modern processors has made successful at automating the process of program optimiza- the generation of highly efficient code increasingly difficult. tion, much human intervention is still needed to obtain high- Manual code generation is very time consuming, but it is of- quality code. One reason is the unevenness of compiler im- ten the only choice since the code generated by today’s com- plementations. There are excellent optimizing compilers for piler technology often has much lower performance than some platforms, but the compilers available for some other the best hand-tuned codes. A promising code generation platforms leave much to be desired. A second, and perhaps strategy, implemented by systems like ATLAS, FFTW, and more important, reason is that conventional compilers lack SPIRAL, uses empirical search to find the parameter values semantic information and, therefore, have limited transfor- of the implementation, such as the tile size and instruction mation power. An emerging approach that has proven quite schedules, that deliver near-optimal performance for a par- effective in overcoming both of these limitations is to use ticular machine. However, this approach has only proven library generators. These systems make use of semantic in- successful on scientific codes whose performance does not formation to apply transformations at all levels of abstrac- depend on the input data.
    [Show full text]
  • A Theory of Fault Recovery for Component-Based Models⋆
    A Theory of Fault Recovery for Component-based Models? Borzoo Bonakdarpour1, Marius Bozga2, and Gregor G¨ossler3 1 School of Computer Science, University of Waterloo Email: [email protected] 2 VERIMAG/CNRS, Gieres, France Email: [email protected] 3 INRIA-Grenoble, Montbonnot, France Email: [email protected] Abstract. This paper introduces a theory of fault recovery for component- based models. We specify a model in terms of a set of atomic components incrementally composed and synchronized by a set of glue operators. We define what it means for such models to provide a recovery mechanism, so that the model converges to its normal behavior in the presence of faults (e.g., in self-stabilizing systems). We present a sufficient condition for in- crementally composing components to obtain models that provide fault recovery. We identify corrector components whose presence in a model is essential to guarantee recovery after the occurrence of faults. We also for- malize component-based models that effectively separate recovery from functional concerns. We also show that any model that provides fault recovery can be transformed into an equivalent model, where functional and recovery tasks are modularized in different components. Keywords: Fault-tolerance; Transformation; Separation of concerns; BIP 1 Introduction Fault-tolerance has always been an active line of research in design and imple- mentation of dependable systems. Intuitively, tolerating faults involves providing a system with the means to handle unexpected defects, so that the system meets its specification even in the presence of faults. In this context, the notion of spec- ification may vary depending upon the guarantees that the system must deliver in the presence of faults.
    [Show full text]
  • Algorithms, Searching and Sorting
    Python for Data Scientists L8: Algorithms, searching and sorting 1 Iterative and recursion algorithms 2 Iterative algorithms • looping constructs (while and for loops) lead to iterative algorithms • can capture computation in a set of state variables that update on each iteration through loop 3 Iterative algorithms • “multiply x * y” is equivalent to “add x to itself y times” • capture state by • result 0 • an iteration number starts at y y y-1 and stop when y = 0 • a current value of computation (result) result result + x 4 Iterative algorithms def multiplication_iter(x, y): result = 0 while y > 0: result += x y -= 1 return result 5 Recursion The process of repeating items in a self-similar way 6 Recursion • recursive step : think how to reduce problem to a simpler/smaller version of same problem • base case : • keep reducing problem until reach a simple case that can be solved directly • when y = 1, x*y = x 7 Recursion : example x * y = x + x + x + … + x y items = x + x + x + … + x y - 1 items = x + x * (y-1) Recursion reduction def multiplication_rec(x, y): if y == 1: return x else: return x + multiplication_rec(x, y-1) 8 Recursion : example 9 Recursion : example 10 Recursion : example 11 Recursion • each recursive call to a function creates its own scope/environment • flow of control passes back to previous scope once function call returns value 12 Recursion vs Iterative algorithms • recursion may be simpler, more intuitive • recursion may be efficient for programmer but not for computers def fib_iter(n): if n == 0: return 0 O(n) elif n == 1: return 1 def fib_recur(n): else: if n == 0: O(2^n) a = 0 return 0 b = 1 elif n == 1: for i in range(n-1): return 1 tmp = a else: a = b return fib_recur(n-1) + fib_recur(n-2) b = tmp + b return b 13 Recursion : Proof by induction How do we know that our recursive code will work ? → Mathematical Induction To prove a statement indexed on integers is true for all values of n: • Prove it is true when n is smallest value (e.g.
    [Show full text]
  • Full Functional Verification of Linked Data Structures
    Full Functional Verification of Linked Data Structures Karen Zee Viktor Kuncak Martin C. Rinard MIT CSAIL, Cambridge, MA, USA EPFL, I&C, Lausanne, Switzerland MIT CSAIL, Cambridge, MA, USA ∗ [email protected] viktor.kuncak@epfl.ch [email protected] Abstract 1. Introduction We present the first verification of full functional correctness for Linked data structures such as lists, trees, graphs, and hash tables a range of linked data structure implementations, including muta- are pervasive in modern software systems. But because of phenom- ble lists, trees, graphs, and hash tables. Specifically, we present the ena such as aliasing and indirection, it has been a challenge to de- use of the Jahob verification system to verify formal specifications, velop automated reasoning systems that are capable of proving im- written in classical higher-order logic, that completely capture the portant correctness properties of such data structures. desired behavior of the Java data structure implementations (with the exception of properties involving execution time and/or mem- 1.1 Background ory consumption). Given that the desired correctness properties in- In principle, standard specification and verification approaches clude intractable constructs such as quantifiers, transitive closure, should work for linked data structure implementations. But in prac- and lambda abstraction, it is a challenge to successfully prove the tice, many of the desired correctness properties involve logical generated verification conditions. constructs such transitive closure and
    [Show full text]
  • 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]
  • A Polynomial-Time Randomized Reduction from Tournament Isomorphism to Tournament Asymmetry∗
    A Polynomial-Time Randomized Reduction from Tournament Isomorphism to Tournament Asymmetry∗ Pascal Schweitzer RWTH Aachen University, Aachen, Germany [email protected] Abstract The paper develops a new technique to extract a characteristic subset from a random source that repeatedly samples from a set of elements. Here a characteristic subset is a set that when containing an element contains all elements that have the same probability. With this technique at hand the paper looks at the special case of the tournament isomorphism problem that stands in the way towards a polynomial-time algorithm for the graph isomorphism problem. Noting that there is a reduction from the automorphism (asymmetry) problem to the isomorphism problem, a reduction in the other direction is nevertheless not known and remains a thorny open problem. Applying the new technique, we develop a randomized polynomial-time Turing-reduction from the tournament isomorphism problem to the tournament automorphism problem. This is the first such reduction for any kind of combinatorial object not known to have a polynomial-time solvable isomorphism problem. 1998 ACM Subject Classification F.2.2 Computations on Discrete Structures, F.1.3 Reducibility and Completeness Keywords and phrases graph isomorphism, asymmetry, tournaments, randomized reductions Digital Object Identifier 10.4230/LIPIcs.ICALP.2017.66 1 Introduction The graph automorphism problem asks whether a given input graph has a non-trivial automorphism. In other words the task is to decide whether a given graph is asymmetric. This computational problem is typically seen in the context of the graph isomorphism problem, which is itself equivalent under polynomial-time Turing reductions to the problem of computing a generating set for all automorphisms of a graph [17].
    [Show full text]
  • Bubble Sort: an Archaeological Algorithmic Analysis
    Bubble Sort: An Archaeological Algorithmic Analysis Owen Astrachan 1 Computer Science Department Duke University [email protected] Abstract 1 Introduction Text books, including books for general audiences, in- What do students remember from their first program- variably mention bubble sort in discussions of elemen- ming courses after one, five, and ten years? Most stu- tary sorting algorithms. We trace the history of bub- dents will take only a few memories of what they have ble sort, its popularity, and its endurance in the face studied. As teachers of these students we should ensure of pedagogical assertions that code and algorithmic ex- that what they remember will serve them well. More amples used in early courses should be of high quality specifically, if students take only a few memories about and adhere to established best practices. This paper is sorting from a first course what do we want these mem- more an historical analysis than a philosophical trea- ories to be? Should the phrase Bubble Sort be the first tise for the exclusion of bubble sort from books and that springs to mind at the end of a course or several courses. However, sentiments for exclusion are sup- years later? There are compelling reasons for excluding 1 ported by Knuth [17], “In short, the bubble sort seems discussion of bubble sort , but many texts continue to to have nothing to recommend it, except a catchy name include discussion of the algorithm after years of warn- and the fact that it leads to some interesting theoreti- ings from scientists and educators.
    [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]
  • Data Structures & Algorithms
    DATA STRUCTURES & ALGORITHMS Tutorial 6 Questions SORTING ALGORITHMS Required Questions Question 1. Many operations can be performed faster on sorted than on unsorted data. For which of the following operations is this the case? a. checking whether one word is an anagram of another word, e.g., plum and lump b. findin the minimum value. c. computing an average of values d. finding the middle value (the median) e. finding the value that appears most frequently in the data Question 2. In which case, the following sorting algorithm is fastest/slowest and what is the complexity in that case? Explain. a. insertion sort b. selection sort c. bubble sort d. quick sort Question 3. Consider the sequence of integers S = {5, 8, 2, 4, 3, 6, 1, 7} For each of the following sorting algorithms, indicate the sequence S after executing each step of the algorithm as it sorts this sequence: a. insertion sort b. selection sort c. heap sort d. bubble sort e. merge sort Question 4. Consider the sequence of integers 1 T = {1, 9, 2, 6, 4, 8, 0, 7} Indicate the sequence T after executing each step of the Cocktail sort algorithm (see Appendix) as it sorts this sequence. Advanced Questions Question 5. A variant of the bubble sorting algorithm is the so-called odd-even transposition sort . Like bubble sort, this algorithm a total of n-1 passes through the array. Each pass consists of two phases: The first phase compares array[i] with array[i+1] and swaps them if necessary for all the odd values of of i.
    [Show full text]
  • Space-Efficient Data Structures, Streams, and Algorithms
    Andrej Brodnik Alejandro López-Ortiz Venkatesh Raman Alfredo Viola (Eds.) Festschrift Space-Efficient Data Structures, Streams, LNCS 8066 and Algorithms Papers in Honor of J. Ian Munro on the Occasion of His 66th Birthday 123 Lecture Notes in Computer Science 8066 Commenced Publication in 1973 Founding and Former Series Editors: Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Alfred Kobsa University of California, Irvine, CA, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen TU Dortmund University, Germany Madhu Sudan Microsoft Research, Cambridge, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbruecken, Germany Andrej Brodnik Alejandro López-Ortiz Venkatesh Raman Alfredo Viola (Eds.) Space-Efficient Data Structures, Streams, and Algorithms Papers in Honor of J. Ian Munro on the Occasion of His 66th Birthday 13 Volume Editors Andrej Brodnik University of Ljubljana, Faculty of Computer and Information Science Ljubljana, Slovenia and University of Primorska,
    [Show full text]
  • Intel Threading Building Blocks
    Praise for Intel Threading Building Blocks “The Age of Serial Computing is over. With the advent of multi-core processors, parallel- computing technology that was once relegated to universities and research labs is now emerging as mainstream. Intel Threading Building Blocks updates and greatly expands the ‘work-stealing’ technology pioneered by the MIT Cilk system of 15 years ago, providing a modern industrial-strength C++ library for concurrent programming. “Not only does this book offer an excellent introduction to the library, it furnishes novices and experts alike with a clear and accessible discussion of the complexities of concurrency.” — Charles E. Leiserson, MIT Computer Science and Artificial Intelligence Laboratory “We used to say make it right, then make it fast. We can’t do that anymore. TBB lets us design for correctness and speed up front for Maya. This book shows you how to extract the most benefit from using TBB in your code.” — Martin Watt, Senior Software Engineer, Autodesk “TBB promises to change how parallel programming is done in C++. This book will be extremely useful to any C++ programmer. With this book, James achieves two important goals: • Presents an excellent introduction to parallel programming, illustrating the most com- mon parallel programming patterns and the forces governing their use. • Documents the Threading Building Blocks C++ library—a library that provides generic algorithms for these patterns. “TBB incorporates many of the best ideas that researchers in object-oriented parallel computing developed in the last two decades.” — Marc Snir, Head of the Computer Science Department, University of Illinois at Urbana-Champaign “This book was my first introduction to Intel Threading Building Blocks.
    [Show full text]