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Parallel And Distributed Computing : Architectures And Algorithms - Isbn:9788120352124

Category: Computers

  • Book Title: PARALLEL AND DISTRIBUTED COMPUTING : ARCHITECTURES AND ALGORITHMS
  • ISBN 13: 9788120352124
  • ISBN 10: 8120352122
  • Author: BASU, S. K.
  • Category: Computers
  • Category (general): Computers
  • Publisher: PHI Learning Pvt. Ltd.
  • Format & Number of pages: 400 pages, book
  • Synopsis: An elementary data flow program equivalent to the above program is given in Figure 5.9(c). Figure 5.9(d) shows a DFG for ... Figure 5.12 Example data flow. AssociAtive 106 Parallel and Distributed Computing: Architectures and Algorithms.

Another description

Handbook of Sensor Networks: Algorithms and Architectures (Wiley Series on Parallel and Distributed Computing)

Written by an international team of recognized experts in sensor networks from prestigious organizations such as Motorola, Fujitsu, the Massachusetts Institute of Technology, Cornell University, and the University of Illinois, Handbook of Sensor Networks: Algorithms and Architectures tackles important challenges and presents the latest trends and innovations in this growing field.

Striking a balance between theoretical and practical coverage, this comprehensive reference explores a myriad of possible architectures for future commercial, social, and educational applications, and offers insightful information and analyses of critical issues, including:
* Sensor training and security
* Embedded operating systems
* Signal processing and medium access
* Target location, tracking, and sensor localization
* Broadcasting, routing, and sensor area coverage
* Topology construction and maintenance
* Data-centric protocols and data gathering
* Time synchronization and calibration
* Energy scavenging and power sources

With exercises throughout, students, researchers, and professionals in computer science, electrical engineering, and telecommunications will find this an essential read to bring themselves up to date on the key challenges affecting the sensors industry.

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Handbook of Sensor Networks: Algorithms and Architectures (Wiley. [Full Version]

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Articles

Berman K, Paul J

Berman K. Paul J. Algorithms: Sequential, Parallel, and Distributed

Kenneth A. Berman, Jerome L. Paul, Course Technology, 2005, pages: 996

Algorithms: Sequential, Parallel, and Distributed offers in-depth coverage of traditional and current topics in sequential algorithms, as well as a solid introduction to the theory of parallel and distributed algorithms. In light of the emergence of modern computing environments such as parallel computers, the Internet, and cluster and grid computing, it is important that computer science students be exposed to algorithms that exploit these technologies. Berman and Paul's text will teach students how to create new algorithms or modify existing algorithms, thereby enhancing students' ability to think independently.

Table of contents:
Introduction to Algorithms
Introduction and Preliminaries
Algorithms from Ancient to Modern Times.
Toward a Modern Theory of Algorithms.
Computing in the Third Millennium.
Closing Remarks.
Design and Analysis Fundamentals
Guidelines for Algorithm Design.
Recursion.
Data Structures and Algorithm Design.
Major Design Strategies.
Analyzing Algorithm Performance.
Designing and Analysing Some Basic Comparison-Based List Algorithms.
Closing Remarks.
Mathematical Tools for Algorithm Analysis
Asymptotic Behavior of Functions.
Asymptotic Order Formulae for Three Important Series.
Recurrence Relations for Complexity.
Mathematical Induction and Proving the Correctness of Algorithms.
Establishing Lower Bounds for Problems.
Hard Problems.
NP-Complete Problems.
CLosing Remarks.
Trees and Applications to Algorithms
Definitions.
Mathematical Properties of Binary Trees.
mplementation of Trees and Forests.
Tree Traversal.
Binary Search Trees.
Priority Queues and Heaps.
mplementing Disjoint Sets.
Closing Remarks.
More on Sorting Algorithms
Shellsort.
Bingosort.
Radixsort.
External Sorting.
Closing Remarks.
Probability and Average Complexity of Algorithms
Expectation and Average Complexity.
Techniques for Computing Average Complexity.
Average Complexity of LinearSearch.
Average Complexity of InsertionSort.
Average Complexity of QuickSort.
Average Complexity of MaxMin2.
Average Complexity of BinarySearch and SearchBinSrchTree.
Searching a Link-Ordered List.
Closing Remarks.
Major Design Strategies
The Greedy Method
General Description.
Optimal Sequential Storage Order.
The Knapsack Problem.
Huffman Codes.
Closing Remarks.
Divide-And-Conquer
The Divide-And-Conquer Paradigm.
Symbolic Algebraic Operations on Polynomials.
Multiplication of Large Integers.
Multiplication of Matrices.
Selecting the k-th Smallest Value in a List.
Two Classical Problems in Computational Geometry.
Closing Remarks.
Dynamic Programming
Optimization Problems and the Principle of Optimality.
Optimal Parenthesization for Computing a Chained Matrix Product.
Optimal Binary Search Trees.
Longest Common Subsequence.
Closing Remarks.
Backtracking and Branch-and-Bound
State-Space Trees.
Backtracking.
Branch-and-Bound.
Closing Remarks.
Graph and Network Algorithms
Graphs and Diagrams
Graphs and Digraphs.
Search and Traversal of Graphs.
Topological Sorting.
Closing Remarks.
Minimum Spanning Tree and Shortest-Path Algorithms
Minimum Spanning Tree.
Shortest Paths in Graphs and Digraphs.
Closing Remarks.
Graph Connectivity and Fault-Tolerance of Networks
Strongly Connected Components.
Articulation Points and Biconnected Components.
Fault-Tolerance Routing Schemes.
Closing Remarks.
Matching and Network Flow Algorithms
Perfect Matchings in Bipartite Graphs.
Maximum Flows in Capacitated Networks.
Closing Remarks.
Parallel and Distributed Algorithms
Introduction to Parallel Algorithms and Architectures
Approaches to the Design of Parallel Algorithms.
Architectural Constraints and the Design of Parallel Algorithms.
Performance Measures of Parallel Algorithms.
Parallel Sorting.
Closing Remarks.
Parallel Design Strategies
Parallel Prefix Computations.
Pointer Jumping.
Matrix Operations in Parallel.
Closing Remarks.
Internet Algorithms
Search Engines.
Ranking Web Pages.
Hashing.
Caching, Content Delivery, and Consistent Hashing.
Message Security Algorithms: RSA.
Closing Remarks.
Distributed Computation Algorithms
SPMD Distributed-Computing Model.
Message Passing.
Distributed Even-Odd Transposition Sort.
Embarrassingly Parallel Master-Worker Paradigm.
Shared-Task Master-Worker Paradigm.
Distributed Depth-First Search (Backtracking).
Closing Remarks.
Distributed Network Algorithms
Leader Election.
Broadcasting and Breadth-First Search.
Shortest Paths.
All-Pairs Shortest Paths.
Minimum Spanning Tree.
Asynchronous Model.
Closing Remarks.
Special Topics
String Matching and Document Processing.
The Naive Algorithm.
The Knuth-Morris-Pratt Algorithm.
The Boyer-Moore String-Matching Algorithm.
The Karp-Rabin String-Matching Algorithm.
Approximate String Matching.
Tries and Suffix Trees.
Closing Remarks.
Balanced Search Trees
The Dictionary Problem.
Rotations in Binary Search Trees.
Red-Black Trees.
B-Trees.
Closing Remarks.
The Fast Fourier Transform
The Discrete Fourier Transform.
The Fast Fourier Transform.
An Iterative Version of the Fast Fourier Transform.
Transforming the Problem Domain.
The Inverse Discrete Fourier Transform and Fast Polynomial Multiplication.
The Fast Fourier Transform in Parallel.
Closing Remarks.
Heuristic Search Strategies: A*-Search and Game Trees
Artificial Intelligence: Production Systems.
Puzzle Game.
A*-Search.
Least-Cost Branch-and-Bound.
Game Trees.
Closing Remarks.
Probabilistic and Randomized Algorithms
Probabilistic Algorithms.
Randomizing Deterministic Algorithms.
Monte Carlo and Las Vegas Algorithms.
Probabilistic Numerical Algorithms.
Probabilistic Parallel Algorithms.
Closing Remarks.
Lower-Bound Theory
Basic Terminology and Techniques.
Decision Trees and Comparison Trees.
Adversary Arguments.
Lower Bounds for Parallel Algorithms.
Closing Remarks.
NP-Complete Problems
The Classes P and NP.
Reducibility.
NP-Complete Problems: Cook's Theorem.
Some Sample NP-Complete Problems.
The Class co-NP.
The Classes NC and P-Complete.
Closing Remarks.
Approximation Algorithms
The Traveling Salesman Problem.
Bin Packing.
The Steiner Tree Problem.
The Facility Location Problem.
Closing Remarks.

References and Suggestions for Further Reading
Exercises
Appendix A: Mathematical Notation and Background
Appendix B: Linear Data Structures
Appendix C: Interpolating Asymptotis Behavior
Appendix D: Random Walks in Digraphs
Appendix E: Elementary Probability Theory
Appendix F: Examples of Message-Passing Interface Code
Appendix G: Pseudocode Conventions

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Journal of Parallel and Distributed Computing

Journal of Parallel and Distributed Computing Journal of Parallel and Distributed Computing Journal Metrics
  • Source Normalized Impact per Paper (SNIP): 1.727Source Normalized Impact per Paper (SNIP):
    2015: 1.727
    SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
  • SCImago Journal Rank (SJR): 0.851SCImago Journal Rank (SJR):
    2015: 0.851
    SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
  • Impact Factor: 1.320Impact Factor:
    2015: 1.320
    The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
    © Thomson Reuters Journal Citation Reports 2016
  • 5-Year Impact Factor: 1.356Five-Year Impact Factor:
    2015: 1.356
    To calculate the five year Impact Factor, citations are counted in 2015 to the previous five years and divided by the source items published in the previous five years.
    © Journal Citation Reports 2016, Published by Thomson Reuters
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    This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing .

    The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems.

    Research Areas Include:

    • Theory of parallel/distributed computing
    • Parallel algorithms and their implementation
    • Innovative computer architectures
    • Shared-memory multiprocessors
    • Peer-to-peer systems
    • Distributed sensor networks
    • Pervasive computing
    • Optical computing
    • Software tools and environments
    • Languages, compilers, and operating systems
    • Fault-tolerant computing
    • Applications and.

    This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing .

    The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems.

    Research Areas Include:

    • Theory of parallel/distributed computing
    • Parallel algorithms and their implementation
    • Innovative computer architectures
    • Shared-memory multiprocessors
    • Peer-to-peer systems
    • Distributed sensor networks
    • Pervasive computing
    • Optical computing
    • Software tools and environments
    • Languages, compilers, and operating systems
    • Fault-tolerant computing
    • Applications and performance analysis
    • Bioinformatics
    • Cyber trust and security
    • Parallel programming
    • Grid computing

    Benefits to authors
    We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services .

    Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

    This journal supports the following content innovations

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    PPT - Parallel and Distributed Computing PowerPoint Presentation

    Parallel and Distributed Computing PowerPoint PPT Presentation Why Use Parallel Computing

    Save time – wall clock time – many processors work together

    Solve larger problems – larger than one processor’s CPU and memory can handle

    Provide concurrency – do multiple things at the same time: online access to databases, search engine

    Google’s 4,000 PC servers are one of the largest in clusters the world

    Other Reasons for Parallel Computing

    Taking advantages of non-local resources – using computing resources on a wide area network, or even internet (grid & cloud computing)

    Remote Access Resources

    Cost savings – using multiple “cheap” computing resources instead of a high-end CPU

    Overcoming memory constraints – for large problems, using memories of multiple computers may overcome the memory constraint obstacle

    Need for Large Scale Modeling SIMD Machines

    Main characteristic of SIMD machines is that:

    all processors must execute the same instruction (on a different data element ) at any instant in the program's execution.

    These machines execute in "lock-step" synchronous to a global clock

    All processors must complete execution of the current instruction before any is allowed to proceed to the next instruction.

    SIMD Machines

    SIMD machines typically contain more, simpler, processors (Processing Elements (PE))

    Best suited for specialized problems characterized by a high degree of regularity, such as graphics/image processing.

    Two varieties: Processor Arrays and Vector Pipelines:

    Processor Arrays: Connection Machine CM-2, MasPar MP-1 & MP-2, ILLIAC IV

    Vector Pipelines: IBM 9000, Cray X-MP, Y-MP & C90, Fujitsu VP, NEC SX-2, Hitachi S820, ETA10

    Most modern computers, particularly those with graphics processor units (GPUs) employ SIMD instructions and execution units.

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    Книги на Google Play – Topics in Parallel and Distributed Computing

    Topics in Parallel and Distributed Computing

    provides resources and guidance for those learning PDC as well as those teaching students new to the discipline.

    The pervasiveness of computing devices containing multicore CPUs and GPUs, including home and office PCs, laptops, and mobile devices, is making even common users dependent on parallel processing. Certainly, it is no longer sufficient for even basic programmers to acquire only the traditional sequential programming skills. The preceding trends point to the need for imparting a broad-based skill set in PDC technology.

    However, the rapid changes in computing hardware platforms and devices, languages, supporting programming environments, and research advances, poses a challenge both for newcomers and seasoned computer scientists.

    This edited collection has been developed over the past several years in conjunction with the IEEE technical committee on parallel processing (TCPP), which held several workshops and discussions on learning parallel computing and integrating parallel concepts into courses throughout computer science curricula.

    • Contributed and developed by the leading minds in parallel computing research and instruction
    • Provides resources and guidance for those learning PDC as well as those teaching students new to the discipline
    • Succinctly addresses a range of parallel and distributed computing topics
    • Pedagogically designed to ensure understanding by experienced engineers and newcomers
    • Developed over the past several years in conjunction with the IEEE technical committee on parallel processing (TCPP), which held several workshops and discussions on learning parallel computing and integrating parallel concepts

    Sushil K. Prasad (BTech'85 IIT Kharagpur, MS'86 Washington State, Pullman; PhD'90 Central Florida, Orlando - all in Computer Science/Engineering) is a Professor of Computer Science at Georgia State University and Director of Distributed and Mobile Systems (DiMoS) Lab. He has carried out theoretical as well as experimental research in parallel and distributed computing, resulting in 140+ refereed publications, several patent applications, and about $3M in external research funds as principal investigator and over $6M overall (NSF/NIH/GRA/Industry).

    Sushil has been honored as an ACM Distinguished Scientist in Fall 2013 for his research on parallel data structures and applications. He was the elected chair of IEEE Technical Committee on Parallel Processing for two terms (2007-11), and received its highest honors in 2012 - IEEE TCPP Outstanding Service Award. Currently, he is leading the NSF-supported IEEE-TCPP curriculum initiative on parallel and distributed computing with a vision to ensure that all computer science and engineering graduates are well-prepared in parallelism through their core courses in this era of multi- and many-cores desktops and handhelds. His current research interests are in Parallel Data Structures and Algorithms, and Computation over Geo-Spatiotemporal Datasets over Cloud, GPU and Multicore Platforms. His homepage is www.cs.gsu.edu/prasad.

    Anshul Gupta is a Principal Research Staff Member in Mathematical Sciences department at IBM T.J. Watson Research Center. His research interests include sparse matrix computations and their applications in optimization and computational sciences, parallel algorithms, and graph/combinatorial algorithms for scientific computing. He has coauthored several journal articles and conference papers on these topics and a textbook titled "Introduction to Parallel Computing." He is the primary author of Watson Sparse Matrix Package (WSMP), one of the most robust and scalable parallel direct solvers for large sparse systems of linear equations.

    Arnold L. Rosenberg is a Research Professor in the Computer Science Department at Northeastern University; he also holds the rank of Distinguished University Professor Emeritus in the Computer Science Department at the University of Massachusetts Amherst. Prior to joining UMass, Rosenberg was a Professor of Computer Science at Duke University from1981 to 1986, and a Research Sta_ Member at the IBM Watson Research Center from 1965 to 1981. He has held visiting positions at Yale University and the University of Toronto. He was a Lady Davis Visiting Professor at the Technion (Israel Institute of Technology) in 1994, and a Fulbright Senior Research Scholar at the University of Paris-South in 2000. Rosenberg's research focuses on developing algorithmic models and techniques to exploit the new modalities of "collaborative computing" (wherein multiple computers cooperate to solve a computational problem) that result from emerging computing technologies. Rosenberg is the author or coauthor of more than 170 technical papers on these and other topics in theoretical computer science and discrete mathematics. He is the coauthor of the research book "Graph Separators, with Applications" and the author of the textbook "The Pillars of Computation Theory: State, Encoding, Nondeterminism"; additionally, he has served as coeditor of several books. Dr. Rosenberg is a Fellow of the ACM, a Fellow of the IEEE, and a Golden Core member of the IEEE Computer Society. Rosenberg received an A.B. in mathematics at Harvard College and an A.M. and Ph.D. in applied mathematics at Harvard University. More details are available at http://www.cs.umass.edu/

    Alan Sussman is a Professor in the Department of Computer Science and Institute for Advanced Computer Studies at the University of Maryland. Working with students and other researchers at Maryland and other institutions he has published over 100 conference and journal papers and received several best paper awards in various topics related to software tools for high performance parallel and distributed computing, and has contributed chapters to 6 books. His research interests include peer-to-peer distributed systems, software engineering for high performance computing, and large scale data intensive computing. He is an associate editor for the Journal of Parallel and Distributed Computing, a subject area editor for the Parallel Computing journal, and an associate editor for IEEE Transactions on Services Computing. Software tools he has built have been widely distributed and used in many computational science applications, in areas such as earth science, space science, and medical informatics. He received his Ph.D. in computer science from Carnegie Mellon University.

    Charles Weems is co-director of the Architecture and Language Implementation lab at the University of Massachusetts. His current research interests include architectures for media and embedded applications, GPU computing, and high precision arithmetic. Previously he led development of two generations of a heterogeneous parallel processor for machine vision, called the Image Understanding Architecture, and co-directed initial work on the Scale compiler that was eventually used for the TRIPS architecture. He is the author of numerous articles, has served on many program committees, chaired the 1997 IEEE CAMP Workshop, the 1999 IEEE Frontiers Symposium, co-chaired IEEE IPDPS in 1999, 2000, and 2013, was general vice-chair for IPDPS from 2001 through 2005, and co-chairs the LSPP workshop. He has co-authored twenty-six introductory CS texts, and co-edited the book Associative Processing and Processors. He is a member of ACM, Senior Member of IEEE, a member of the Executive Committee of the IEEE TC on Parallel Processing, has been an editor for IEEE TPDS, Elsevier JPDC, and is an editor with Parallel Computing.

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    Parallel and distributed 1

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    Handbook of Sensor Networks: Algorithms and Architectures (Wiley Series on Parallel and Distributed Computing) - Ivan Stojmenovic

    Handbook of Sensor Networks: Algorithms and Architectures (Wiley Series on Parallel and Distributed Computing) Description:

    The State Of The Art Of Sensor Networks

    Written by an international team of recognized experts in sensor networks from prestigious organizations such as Motorola, Fujitsu, the Massachusetts Institute of Technology, Cornell University, and the University of Illinois, Handbook of Sensor Networks: Algorithms and Architectures tackles important challenges and presents the latest trends and innovations in this growing field.

    Striking a balance between theoretical and practical coverage, this comprehensive reference explores a myriad of possible architectures for future commercial, social, and educational applications, and offers insightful information and analyses of critical issues, including:
    * Sensor training and security
    * Embedded operating systems
    * Signal processing and medium access
    * Target location, tracking, and sensor localization
    * Broadcasting, routing, and sensor area coverage
    * Topology construction and maintenance
    * Data-centric protocols and data gathering
    * Time synchronization and calibration
    * Energy scavenging and power sources

    With exercises throughout, students, researchers, and professionals in computer science, electrical engineering, and telecommunications will find this an essential read to bring themselves up to date on the key challenges affecting the sensors industry.

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