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• Preface ix Notation xiii
• Chapter 1. Introduction 1 1.1 Overview 1 1.2 What is parallel computing? 3 1.3 Performance 4 1.4 Why parallel? 11 1.5 Two simple examples 15 1.6 Mesh-based applications 24 1.7 Parallel perspectives 30 1.8 Exercises 33
• Chapter 2. Parallel Performance 37
• 2.1 Summation example 37 2.2 Performance measures 38 2.3 Limits to performance 44 2.4 Scalability 48 2.5 Parallel performance analysis 56 2.6 Parallel payoff 59 2.7 Real world parallelism 64 2.8 Starting SPMD programming 66 2.9 Exercises 66
• Chapter 3. Computer Architecture 71 3.1 PMS notation 71 3.2 Shared memory multiprocessor 75 3.3 Distributed memory multicomputer 79 3.4 Pipeline and vector processors 87 3.5 Comparison of parallel architectures 89 3.6 Taxonomies 92 3.7 Current trends 94 3.8 Exercises 95
• Chapter 4. Dependences 99 4.1 Data dependences 100 4.2 Loop-carried data dependences 103 4.3 Dependence examples 110 4.4 Testing for loop-carried dependences 112 4.5 Loop transformations 114 4.6 Dependence examples continued 120 4.7 Exercises 123
• Chapter 5. Parallel Languages 127 5.1 Critical factors 129 5.2 Command and control 134 5.3 Memory models 136 5.4 Shared memory programming 139 5.5 Message passing 143 5.6 Examples and comments 148 5.7 Parallel language developments 153 5.8 Exercises 154
• Chapter 6. Collective Operations 157 6.1 The @notation 157 6.2 Tree/ring algorithms 158 6.3 Reduction operations 162 6.4 Reduction operation applications 164 6.5 Parallel prefix algorithms 168 6.6 Performance of reduction operations 169 6.7 Data movement operations 173 6.8 Exercises 174
• Chapter 7. Current Programming Standards 177 7.1 Introduction to MPI 177 7.2 Collective operations in MPI 181 7.3 Introduction to POSIX threads 184 7.4 Exercises 187
• Chapter 8. The Planguage Model 191 8.1 I P language details 192 8.2 Ranges and arrays 198 8.3 Reduction operations in Pfortran 200 8.4 Introduction to PC 204 8.5 Reduction operations in PC 206 8.6 Planguages versus message passing 207 8.7 Exercises 208
• Chapter 9. High Performance Fortran 213 9.1 HPF data distribution directives 214 9.2 Other mechanisms for expressing concurrency 219 9.3 Compiling HPF 220 9.4 HPF comparisons and review 221 9.5 Exercises 222
• Chapter 10. Loop Tiling 227 10.1 Loop tiling 227 10.2 Work vs.data decomposition 228 10.3 Tiling in OpenMP 228 10.4 Teams 232 10.5 Parallel regions 233 10.6 Exercises 234
• Chapter 11. Matrix Eigen Analysis 237 11.1 The Leslie matrix model 237 11.2 The power method 242 11.3 A parallel Leslie matrix program 244 11.4 Matrix-vector product 249 11.5 Power method applications 251 11.6 Exercises 253
• Chapter 12. Linea Systems 257 12.1 Gaussian elimination 257 12.2 Solving triangular systems in parallel 262 12.3 Divide-and-conquer algorithms 271 12.4 Exercises 277 12.5 Projects 281
• Chapter 13. Particle Dynamics 283 13.1 Model assumptions 284 13.2 Using Newton's third law 285 13.3 Further code complications 288 13.4 Pair list generation 290 13.5 Force calculation with a pair list 296 13.6 Performance of replication algorithm 299 13.7 Case study:particle dynamics in HPF 302 13.8 Exercises 307 13.9 Projects 310
• Chapter 14. Mesh Methods 315 14.1 Boundary value problems 315 14.2 Iterative methods 319 14.3 Multigrid methods 322 14.4 Multidimensional problems 327 14.5 Initial value problems 328 14.6 Exercises 333 14.7 Projects 334
• Chapter 15. Sorting 335 15.1 Introduction 335 15.2 Parallel sorting 337 15.3 Spatial sorting 342 15.4 Exercises 353 15.5 Projects 355
• Bibliography 357 Index 369.
• (source: Nielsen Book Data)

What does Google's management of billions of Web pages have in common with analysis of a genome with billions of nucleotides? Both apply methods that coordinate many processors to accomplish a single task. From mining genomes to the World Wide Web, from modeling financial markets to global weather patterns, parallel computing enables computations that would otherwise be impractical if not impossible with sequential approaches alone. Its fundamental role as an enabler of simulations and data analysis continues an advance in a wide range of application areas. "Scientific Parallel Computing" is the first textbook to integrate all the fundamentals of parallel computing in a single volume while also providing a basis for a deeper understanding of the subject. Designed for graduate and advanced undergraduate courses in the sciences and in engineering, computer science, and mathematics, it focuses on the three key areas of algorithms, architecture, languages, and their crucial synthesis in performance. The book's computational examples, whose math prerequisites are not beyond the level of advanced calculus, derive from a breadth of topics in scientific and engineering simulation and data analysis. The programming exercises presented early in the book are designed to bring students up to speed quickly, while the book later develops projects challenging enough to guide students toward research questions in the field. The new paradigm of cluster computing is fully addressed. A supporting web site provides access to all the codes and software mentioned in the book, and offers topical information on popular parallel computing systems. The book integrates all the fundamentals of parallel computing essential for today's high-performance requirements. It is ideal for graduate and advanced undergraduate students in the sciences and in engineering, computer science, and mathematics. Extensive programming and theoretical exercises enable students to write parallel codes quickly. More challenging projects later in the book introduce research questions. New paradigm of cluster computing is fully addressed. Supporting web site provides access to all the codes and software mentioned in the book.
(source: Nielsen Book Data)

• 1. Parallel Machines and Computations.
• 2. Potential for Parallel Computations.
• 3. Vector Algorithms and Architectures.
• 4. MIMD Computers and Multiprocessors.
• 5. Distributed Memory Multiprocessors.
• 6. Interconnection Networks.
• 7. Data Dependence and Parallelism.
• 8. Implementing Synchronization and Data Sharing.
• 9. Parallel Processor Performance.
• 10. Temporal Behavior of Parallel Programs.
• 11. Parallel I/O. Appendix A. Routines of the MPI Message Passing Library. Appendix B. Synchronization Mechanisms. Bibliography. Index.
• (source: Nielsen Book Data)

For senior-level/graduate courses in Parallel Computing and Processing in departments of Engineering, Computer Science and Mathematics. This carefully class tested text provides comprehensive coverage of the fundamentals of parallel processing with integration of parallel architectures, algorithms, and languages. Its unique presentation integrates the three topics and explains their performance implications, providing students with the depth of knowledge and expertise needed to design and develop successful parallel applications.
(source: Nielsen Book Data)

Daily life relies more and more on safety critical systems, e.g. in areas such as power plant control, traffic management, flight control, and many more. MOVEP is a school devoted to the broad subject of modeling and verifying software and hardware systems. This volume contains tutorials and annotated bibliographies covering the main subjects addressed at MOVEP 2000. The four tutorials deal with Model Checking, Theorem Proving, Composition and Abstraction Techniques, and Timed Systems. Three research papers give detailed views of High-Level Message Sequence Charts, Industrial Applications of Model Checking, and the use of Formal Methods in Security. Finally, four annotated bibliographies give an overview of Infinite State Space Systems, Testing Transition Systems, Fault-Model-Driven Test Derivation, and Mobile Processes.
(source: Nielsen Book Data)

• Preface. Contributing Authors. 1. Optimal Bounds on Tail Probabilities: A Study of an Approach
• A. Cohen, et al. 2. Parallelism in Comparison Problems
• D. Krizanc. 3. Random Sampling
• R. Raman. 4. Randomized Algorithms on the Mesh
• L. Narayanan. 5. Efficient Randomized Algorithms
• D.S.L. Wei, K. Naik. 6. Ultrafast Randomized Parallel Algorithms for Spanning Forests
• A. Dessmark, et al. 7. Parallel Randomized Techniques for Some Fundamental Geometric Problems
• S. Ramaswami. 8. Capturing the Connectivity of High-Dimensional Geometric Spaces
• D. Hsu, et al. 9. Randomized Parallel Prefetching and Buffer Management
• M. Kalluhalla, P.J. Varman. 10. DFA Problems
• B. Ravikumar. 11. LAPACK90
• J. Dongarra, J. Wasniewski. References. Appendix A. Appendix B. Appendix D. Appendix E. Index.
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The technique of randomization has become very prevalent since it offers superior performance and simplicity. Numerous researchers work in this area of vital importance. Parallel Computing is also very important since one can get excellent speedups using parallel computers. This book combines these two domains. It provides a summary of the state-of-the-art results and techniques in the area of randomized parallel computing. There are few texts in the area of randomized computing, and more surprisingly there is no text in the area of randomized parallel computing. Thus our book fills the void in this very important area. Audience: This is a reference book for researchers, educators, and students. It can also be used as a text for an advanced graduate course on randomized computing, parallel computing, or distributed computing.
(source: Nielsen Book Data)

• Fundamental Concepts: Introduction to Parallelism. A Taste of Parallel Algorithms. Parallel Algorithm Complexity. Models of Parallel Processing. Extreme Models: PRAM and Basic Algorithms. More Shared-Memory Algorithms. Sorting and Selection Networks. Other Circuit-Level Examples. Mesh-Based Architectures: Sorting on a 2D Mesh or Torus. Routing on a 2D Mesh or Torus. Numerical 2D Mesh Algorithms. Other Mesh-Related Architectures. Low-Diameter Architectures: Hypercubes and Their Algorithms. Sorting and Routing on Hypercubes. Other Hypercubic Architectures. A Sampler of Other Networks. Some Broad Topics: Emulation and Scheduling. Data Storage, Input, and Output. Reliable Parallel Processing. System and Software Issues. Implementation Aspects: Shared-Memory MIMD Machines. Message-Passing MIMD Machines. Data-Parallel SIMD Machines. Past, Present, and Future. Index.
• (source: Nielsen Book Data)

This original text provides comprehensive coverage of parallel algorithms and architectures, beginning with fundamental concepts and continuing through architectural variations and aspects of implementation. Unlike the authors of similar texts, Professor Parhami reviews the circuit model and problem driven parallel machines, variants of mesh architectures, and composite and hierarchical systems, among other subjects. With its balanced treatment of theory and practical designs, class tested lecture material and problems, and helpful case studies, this book is suited to graduate and upper level undergraduate students of advanced architecture or parallel processing.
(source: Nielsen Book Data)

• Preface 1 Introduction: Examples and Requirements Part I Background and Fundamentals 2 System Structure and Dynamic Execution 3 The Hardware Interface, I/O and Communications 4 Support for Processes 5 Fundamentals of Distributed Systems 6 Memory Management 7 File Management Part II Single Concurrent Actions 8 System Structure 9 Low-Level Synchronization Primitives-Implementation 10 Low-Level Primitives-Use in Systems and Languages 11 Language Primitives for Shared Memory 12 IPC and System Structure 13 IPC Without Shared Memory 14 Crash Resilience and Persistent Data 15 Distributed IPC Part III Concurrent Composite Actions 16 Decomposable Abstract Operations 17 Resource Allocation and Deadlock 18 Transactions 19 Concurrency Control 20 Recovery 21 Distributed Transactions 22 Distributed Computations Part IV Case Studies 23 UNIX 24 Microkernels: MACH and CHORUS 25 Windows NT 26 Middleware: Corba and Java 27 Transaction Processing Monitors and Systems Appendix: Evolution of Computer Systems.
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The second edition of Concurrent Systems presents a modern approach to software systems, ideally suited to the needs of today's students and programmers. It focuses on theose concepts in concurrency that play a vital role in the design of operating systems, database systems, distributed systems and communication networks.
(source: Nielsen Book Data)

Massively parallel processing is currently the most promising answer to the quest for increased computer performance. This has resulted in the development of new programming languages and programming environments and has stimulated the design and production of massively parallel supercomputers. The efficiency of concurrent computation and input/output essentially depends on the proper utilization of specific architectural features of the underlying hardware. This book focuses on development of runtime systems supporting execution of parallel code and on supercompilers automatically parallelizing code written in a sequential language. Fortran has been chosen for the presentation of the material because of its dominant role in high-performance programming for scientific and engineering applications.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Basic concepts
• 3. Extremal graph properties
• 4. Rounding, interval partitioning and separation
• 5. Primal-dual method
• 6. Graph decomposition
• 7. Further parallel approximations
• 8. Non-approximability
• 9. Syntactical defined phrases
• Appendix: Definition of problems
• Bibliography
• Index.
• (source: Nielsen Book Data)

Various problems in computer science are 'hard', that is NP-complete, and so not realistically computable; thus in order to solve them they have to be approximated. This book is a survey of the basic techniques for approximating combinatorial problems using parallel algorithms. Its core is a collection of techniques that can be used to provide parallel approximations for a wide range of problems (for example, flows, coverings, matchings, travelling salesman problems, graphs), but in order to make the book reasonably self-contained, the authors provide an introductory chapter containing the basic definitions and results. A final chapter deals with problems that cannot be approximated, and the book is ended by an appendix that gives a convenient summary of the problems described in the book. This is an up-to-date reference for research workers in the area of algorithms, but it can also be used for graduate courses in the subject.
(source: Nielsen Book Data)

• 1. Introductio
• n2. Models of Computatio
• n3. Combinational Circuit
• s4. Parallel Prefix Computatio
• n5. Divide and Conque
• r6. Pointer-Based Data Structure
• s7. Linear Array
• s8. Meshes and Related Model
• s9. Hypercubes and Star
• s10. Models Using Buse
• s11. Broadcasting with Selective Reductio
• n12. Parallel SynergyBibliographyIndex.
• (source: Nielsen Book Data)

Focusing throughout on models of computation and methods of problem solving, this text shows how a parallel algorithm can be designed for a given computational problem to run on a parallel computer, and then how it can be analyzed to determine its goodness. The book covers in detail the main three models of computation used to design parallel algorithms (namely combinational circuits, shared memory machines, and interconnection networks). The algorithms are grouped by method of design (prefix computation, divide and conquer, and pointer based methods) and described for interconnection networks varying from the simplest (linear array) to the most advanced (star). The chapter on parallel synergy shows that the concept of superlinear speedup is mathematically possible, and describes some real-life examples where such speedup can be achieved.
(source: Nielsen Book Data)

Increasingly, research in parallel computing is focused on how high-level language constructs can be implemented. Through the introduction of a language, EVAL, the authors show how such languages can help to develop a better understanding of the nature of data parallelism and consequently produce more elegant and portable programs.
(source: Nielsen Book Data)

• 1. The Technology, Policy and Business of Computers
• 2. History and Present State of Parallel Computing
• 3. Parallel Computing System Software and Workloads
• 4. Parallel System Architecture
• 5. A Performance Overview of Parallel Processing
• 6. Performance Speedup and Stability
• 7. Performance Scalability
• 8. Demonstrating PPTs 1, 2, and 3
• 9. Demonstrating PPTs 4 and 5
• 10. The Future of High Performance Computing.
• (source: Nielsen Book Data)

High performance computing is in a confused state as hardware speed gains attentuate and parallelism appears to be the only way to sustain performance as growth. After more than a decade of commercial development, no standard or widely acceptable systems have emerged. This volume defines practical parallelism tests, and suggests how they can be passed by giving specific technical suggestions and outlining policies that should be taken.
(source: Nielsen Book Data)

Many parallel computer architectures are especially suited for particular classes of applications. However, there are only a few parallel architectures equally well suited for standard programs. Much effort is invested into research in compiler techniques to make programming parallel machines easier. This book presents methods for automatic parallelization, so that programs need not to be tailored for specific architectures; here the focus is on fine-grain parallelism, offered by most new microprocessor architectures. The book addresses compiler writers, computer architects, and students by demonstrating the manifold complex relationships between architecture and compiler technology.
(source: Nielsen Book Data)

This volume provides an ideal introduction to key topics in parallel computing. With its cogent overview of the essentials of the subject as well as lists of P -complete- and open problems, extensive remarks corresponding to each problem, a thorough index, and extensive references, the book will prove invaluable to programmers stuck on problems that are particularly difficult to parallelize. In providing an up-to-date survey of parallel computing research from 1994, Topics in Parallel Computing will prove invaluable to researchers and professionals with an interest in the super computers of the future.
(source: Nielsen Book Data)

• 1. The key idea
• 2. Parallel software development
• 3. Architectural background
• 4. Models and their properties
• 5. The categorical data type of lists
• 6. Software development using lists
• 7. Other operations on lists
• 8. A cost calculus for lists
• 9. Building categorical data types
• 10. Lists, bags and finite sets
• 11. Trees
• 12. Arrays
• 13. Graphs
• 14. Conclusions
• Appendix A. C++ Library for lists
• Appendix B. Historical background.
• (source: Nielsen Book Data)

The major reason for the lack of use of parallel computing is the mismatch between the complexity and variety of parallel hardware, and the software development tools to program it. The cost of developing software needs to be amortised over decades, but the platforms on which it executes change every few years, requiring complete rewrites. The evident cost-effectiveness of parallel computation has not been realized because of this mismatch. This book presents an integrated approach to parallel software development by addressing both software and performance issues together. It presents a methodology for software construction that produces architecture-independent and intellectually abstract software. The software can execute efficiently on a range of existing and potential hardware configurations. The approach is based on the construction of categorical data types, a generalization of abstract data types, and of objects. Categorical data types abstract both from the representation of a data type, and also from the detailed control flow necessary to perform operations on it. They thus impose a strong separation between the semantics, on which programs can depend, and the implementation, which is therefore free to hide the parallel machine properties that are used.
(source: Nielsen Book Data)

• Preface
• 1. Introduction
• 2. The paradigms of parallel computing
• 3. Programming parallel computers
• 4. Connectivity
• 5. Processor design
• 6. System performance
• 7. Some case studies
• 8. Conclusions
• Bibliography
• References
• Index.
• (source: Nielsen Book Data)

This book sets out the principles of parallel computing in a way which will be useful to student and potential user alike. It includes coverage of both conventional and neural computers. The content of the book is arranged hierarchically. It explains why, where and how parallel computing is used; the fundamental paradigms employed in the field; how systems are programmed or trained; technical aspects including connectivity and processing element complexity; and how system performance is estimated (and why doing so is difficult). The penultimate chapter of the book comprises a set of case studies of archetypal parallel computers, each study written by an individual closely connected with the system in question. The final chapter correlates the various aspects of parallel computing into a taxonomy of systems.
(source: Nielsen Book Data)