Parallel processing Books

Book SynopsisMichel Charpentier is an associate professor with the Computer Science department at the University of New Hampshire (UNH). His interests over the years have ranged from distributed systems to formal verification and mobile sensor networks. He has been with UNH since 1999 and currently teaches courses in programming languages, concurrency, formal verification, and model-checking.Table of ContentsForeword by Cay Horstmann xxiii Preface xxv Acknowledgments xxxv About the Author xxxvii Part I. Functional Programming 1 Chapter 1: Concepts of Functional Programming 3 1.1 What Is Functional Programming? 3 1.2 Functions 4 1.3 From Functions to Functional Programming Concepts 6 1.4 Summary 7 Chapter 2: Functions in Programming Languages 9 2.1 Defining Functions 9 2.2 Composing Functions 10 2.3 Functions Defined as Methods 12 2.4 Operators Defined as Methods 12 2.5 Extension Methods 13 2.6 Local Functions 14 2.7 Repeated Arguments 15 2.8 Optional Arguments 16 2.9 Named Arguments 16 2.10 Type Parameters 17 2.11 Summary 19 Chapter 3: Immutability 21 3.1 Pure and Impure Functions 21 3.2 Actions 23 3.3 Expressions Versus Statements 25 3.4 Functional Variables 26 3.5 Immutable Objects 28 3.6 Implementation of Mutable State 29 3.7 Functional Lists 31 3.8 Hybrid Designs 32 3.9 Updating Collections of Mutable/Immutable Objects 35 3.10 Summary 36 Chapter 4: Case Study: Active–Passive Sets 39 4.1 Object-Oriented Design 39 4.2 Functional Values 41 4.3 Functional Objects 43 4.4 Summary 44 Chapter 5: Pattern Matching and Algebraic Data Types 47 5.1 Functional Switch 47 5.2 Tuples 48 5.3 Options 50 5.4 Revisiting Functional Lists 51 5.5 Trees 53 5.6 Illustration: List Zipper 56 5.7 Extractors 59 5.8 Summary 60 Chapter 6: Recursive Programming 63 6.1 The Need for Recursion 63 6.2 Recursive Algorithms 65 6.3 Key Principles of Recursive Algorithms 67 6.4 Recursive Structures 69 6.5 Tail Recursion 71 6.6 Examples of Tail Recursive Functions 73 6.7 Summary 77 Chapter 7: Recursion on Lists 79 7.1 Recursive Algorithms as Equalities 79 7.2 Traversing Lists 80 7.3 Returning Lists 82 7.4 Building Lists from the Execution Stack 84 7.5 Recursion on Multiple/Nested Lists 85 7.6 Recursion on Sublists Other Than the Tail 88 7.7 Building Lists in Reverse Order 90 7.8 Illustration: Sorting 92 7.9 Building Lists Efficiently 94 7.10 Summary 96 Chapter 8: Case Study: Binary Search Trees 99 8.1 Binary Search Trees 99 8.2 Sets of Integers as Binary Search Trees 100 8.3 Implementation Without Rebalancing 102 8.4 Self-Balancing Trees 107 8.5 Summary 113 Chapter 9: Higher-Order Functions 115 9.1 Functions as Values 115 9.2 Currying 118 9.3 Function Literals 120 9.4 Functions Versus Methods 123 9.5 Single-Abstract-Method Interfaces 124 9.6 Partial Application 125 9.7 Closures 130 9.8 Inversion of Control 133 9.9 Summary 133 Chapter 10: Standard Higher-Order Functions 137 10.1 Functions with Predicate Arguments 137 10.2 map and foreach 140 10.3 atMap 141 10.4 fold and reduce 146 10.5 iterate, tabulate, and unfold 148 10.6 sortWith, sortBy, maxBy, and minBy 149 10.7 groupBy and groupMap 150 10.8 Implementing Standard Higher-Order Functions 152 10.9 foreach, map, atMap, and for-Comprehensions 152 10.10 Summary 155 Chapter 11: Case Study: File Systems as Trees 157 11.1 Design Overview 157 11.2 A Node-Searching Helper Function 158 11.3 String Representation 158 11.4 Building Trees 160 11.5 Querying 164 11.6 Navigation 168 11.7 Tree Zipper 169 11.8 Summary 172 Chapter 12: Lazy Evaluation 173 12.1 Delayed Evaluation of Arguments 173 12.2 By-Name Arguments 174 12.3 Control Abstraction 176 12.4 Internal Domain-Specifc Languages 179 12.5 Streams as Lazily Evaluated Lists 180 12.6 Streams as Pipelines 182 12.7 Streams as Infinite Data Structures 184 12.8 Iterators 184 12.9 Lists, Streams, Iterators, and Views 187 12.10 Delayed Evaluation of Fields and Local Variables 190 12.11 Illustration: Subset-Sum 191 12.12 Summary 193 Chapter 13: Handling Failures 195 13.1 Exceptions and Special Values 195 13.2 Using Option 197 13.3 Using Try 198 13.4 Using Either 199 13.5 Higher-Order Functions and Pipelines 201 13.6 Summary 204 Chapter 14: Case Study: Trampolines 205 14.1 Tail-Call Optimization 205 14.2 Trampolines for Tail-Calls 206 14.3 Tail-Call Optimization in Java 207 14.4 Dealing with Non-Tail-Calls 209 14.5 Summary 213 A Brief Interlude 215 Chapter 15: Types (and Related Concepts) 217 15.1 Typing Strategies 217 15.2 Types as Sets 222 15.3 Types as Services 223 15.4 Abstract Data Types 224 15.5 Type Inference 225 15.6 Subtypes 229 15.7 Polymorphism 232 15.8 Type Variance 235 15.9 Type Bounds 241 15.10 Type Classes 245 15.11 Summary 250 Part II. Concurrent Programming 253 Chapter 16: Concepts of Concurrent Programming 255 16.1 Non-sequential Programs 255 16.2 Concurrent Programming Concepts 258 16.3 Summary 259 Chapter 17: Threads and Nondeterminism 261 17.1 Threads of Execution 261 17.2 Creating Threads Using Lambda Expressions 263 17.3 Nondeterministic Behavior of Multithreaded Programs 263 17.4 Thread Termination 264 17.5 Testing and Debugging Multithreaded Programs 266 17.6 Summary 268 Chapter 18: Atomicity and Locking 271 18.1 Atomicity 271 18.2 Non-atomic Operations 273 18.3 Atomic Operations and Non-atomic Composition 274 18.4 Locking 278 18.5 Intrinsic Locks 279 18.6 Choosing Locking Targets 281 18.7 Summary 283 Chapter 19: Thread-Safe Objects 285 19.1 Immutable Objects 285 19.2 Encapsulating Synchronization Policies 286 19.3 Avoiding Reference Escape 288 19.4 Public and Private Locks 289 19.5 Leveraging Immutable Types 290 19.6 Thread-Safety 293 19.7 Summary 295 Chapter 20: Case Study: Thread-Safe Queue 297 20.1 Queues as Pairs of Lists 297 20.2 Single Public Lock Implementation 298 20.3 Single Private Lock Implementation 301 20.4 Applying Lock Splitting 303 20.5 Summary 305 Chapter 21: Thread Pools 307 21.1 Fire-and-Forget Asynchronous Execution 307 21.2 Illustration: Parallel Server 309 21.3 Different Types of Thread Pools 312 21.4 Parallel Collections 314 21.5 Summary 318 Chapter 22: Synchronization 321 22.1 Illustration of the Need for Synchronization 321 22.2 Synchronizers 324 22.3 Deadlocks 325 22.4 Debugging Deadlocks with Thread Dumps 328 22.5 The Java Memory Model 330 22.6 Summary 335 Chapter 23: Common Synchronizers 337 23.1 Locks 337 23.2 Latches and Barriers 339 23.3 Semaphores 341 23.4 Conditions 343 23.5 Blocking Queues 349 23.6 Summary 353 Chapter 24: Case Study: Parallel Execution 355 24.1 Sequential Reference Implementation 355 24.2 One New Thread per Task 356 24.3 Bounded Number of Threads 357 24.4 Dedicated Thread Pool 359 24.5 Shared Thread Pool 360 24.6 Bounded Thread Pool 361 24.7 Parallel Collections 362 24.8 Asynchronous Task Submission Using Conditions 362 24.9 Two-Semaphore Implementation 367 24.10 Summary 368 Chapter 25: Futures and Promises 369 25.1 Functional Tasks 369 25.2 Futures as Synchronizers 371 25.3 Timeouts, Failures, and Cancellation 374 25.4 Future Variants 375 25.5 Promises 375 25.6 Illustration: Thread-Safe Caching 377 25.7 Summary 379 Chapter 26: Functional-Concurrent Programming 381 26.1 Correctness and Performance Issues with Blocking 381 26.2 Callbacks 384 26.3 Higher-Order Functions on Futures 385 26.4 Function atMap on Futures 388 26.5 Illustration: Parallel Server Revisited 390 26.6 Functional-Concurrent Programming Patterns 393 26.7 Summary 397 Chapter 27: Minimizing Thread Blocking 399 27.1 Atomic Operations 399 27.2 Lock-Free Data Structures 402 27.3 Fork/Join Pools 405 27.4 Asynchronous Programming 406 27.5 Actors 407 27.6 Reactive Streams 411 27.7 Non-blocking Synchronization 412 27.8 Summary 414 Chapter 28: Case Study: Parallel Strategies 417 28.1 Problem Definition 417 28.2 Sequential Implementation with Timeout 419 28.3 Parallel Implementation Using invokeAny 420 28.4 Parallel Implementation Using CompletionService 421 28.5 Asynchronous Implementation with Scala Futures 422 28.6 Asynchronous Implementation with CompletableFuture 426 28.7 Caching Results from Strategies 427 28.8 Summary 431 Appendix A. Features of Java and Kotlin 433 A.1 Functions in Java and Kotlin 433 A.2 Immutability 436 A.3 Pattern Matching and Algebraic Data Types 437 A.4 Recursive Programming 439 A.5 Higher-Order Functions 440 A.6 Lazy Evaluation 446 A.7 Handling Failures 449 A.8 Types 451 A.9 Threads 453 A.10 Atomicity and Locking 454 A.11 Thread-Safe Objects 455 A.12 Thread Pools 457 A.13 Synchronization 459 A.14 Futures and Functional-Concurrent Programming 460 A.15 Minimizing Thread Blocking 461 Glossary 463 Index 465

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Book SynopsisDuane Storti is a professor of mechanical engineering at the University of Washington in Seattle. He has thirty-five years of experience in teaching and research in the areas of engineering mathematics, dynamics and vibrations, computer-aided design, 3D printing, and applied GPU computing.   Mete Yurtoglu is currently pursuing an M.S. in applied mathematics and a Ph.D. in mechanical engineering at the University of Washington in Seattle. His research interests include GPU-based methods for computer vision and machine learning.  Table of Contents Acknowledgments xvii About the Authors xix Introduction 1 What Is CUDA? 1 What Does “Need-to-Know” Mean for Learning CUDA? 2 What Is Meant by “for Engineers”? 3 What Do You Need to Get Started with CUDA? 4 How Is This Book Structured? 4 Conventions Used in This Book 8 Code Used in This Book 8 User’s Guide 9 Historical Context 10 References 12 Chapter 1: First Steps 13 Running CUDA Samples 13 Running Our Own Serial Apps 19 Summary 22 Suggested Projects 23 Chapter 2: CUDA Essentials 25 CUDA’s Model for Parallelism 25 Need-to-Know CUDA API and C Language Extensions 28 Summary 31 Suggested Projects 31 References 31 Chapter 3: From Loops to Grids 33 Parallelizing dist_v1 33 Parallelizing dist_v2 38 Standard Workflow 42 Simplified Workflow 43 Summary 47 Suggested Projects 48 References 48 Chapter 4: 2D Grids and Interactive Graphics 49 Launching 2D Computational Grids 50 Live Display via Graphics Interop 56 Application: Stability 66 Summary 76 Suggested Projects 76 References 77 Chapter 5: Stencils and Shared Memory 79 Thread Interdependence 80 Computing Derivatives on a 1D Grid 81 Summary 117 Suggested Projects 118 References 119 Chapter 6: Reduction and Atomic Functions 121 Threads Interacting Globally 121 Implementing parallel_dot 123 Computing Integral Properties: centroid_2d 130 Summary 138 Suggested Projects 138 References 138 Chapter 7: Interacting with 3D Data 141 Launching 3D Computational Grids: dist_3d 144 Viewing and Interacting with 3D Data: vis_3d 146 Summary 171 Suggested Projects 171 References 171 Chapter 8: Using CUDA Libraries 173 Custom versus Off-the-Shelf 173 Thrust 175 cuRAND 190 NPP 193 Linear Algebra Using cuSOLVER and cuBLAS . 201 cuDNN 207 ArrayFire 207 Summary 207 Suggested 208 References 209 Chapter 9: Exploring the CUDA Ecosystem 211 The Go-To List of Primary Sources 211 Further Sources 217 Summary 218 Suggested Projects 219 Appendix A: Hardware Setup 221 Checking for an NVIDIA GPU: Windows 221 Checking for an NVIDIA GPU: OS X 222 Checking for an NVIDIA GPU: Linux 223 Determining Compute Capability 223 Upgrading Compute Capability 225 Appendix B: Software Setup 229 Windows Setup 229 OS X Setup 238 Linux Setup 240 Appendix C: Need-to-Know C Programming 245 Characterization of C 245 C Language Basics 246 Data Types, Declarations, and Assignments 248 Defining Functions 250 Building Apps: Create, Compile, Run, Debug 251 Arrays, Memory Allocation, and Pointers 262 Control Statements: for, if 263 Sample C Programs 267 References 277 Appendix D: CUDA Practicalities: Timing, Profiling, Error Handling, and Debugging 279 Execution Timing and Profiling 279 Error Handling 292 Debugging in Windows 298 Debugging in Linux 305 CUDA-MEMCHECK 308 Using Visual Studio Property Pages 309 References 312 Index 313

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Book SynopsisThis book is an introduction to the field of parallel algorithms and the underpinning techniques to realize the parallelization. The emphasis is on designing algorithms within the timeless and abstracted context of a high-level programming language. The focus of the presentation is on practical applications of the algorithm design using different models of parallel computation. Each model is illustrated by providing an adequate number of algorithms to solve some problems that quite often arise in many applications in science and engineering.The book is largely self-contained, presuming no special knowledge of parallel computers or particular mathematics. In addition, the solutions to all exercises are included at the end of each chapter.The book is intended as a text in the field of the design and analysis of parallel algorithms. It includes adequate material for a course in parallel algorithms at both undergraduate and graduate levels.

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Book SynopsisThis easy-to-read, hands-on guide demystifies concurrency concepts like threading, asynchronous programming, and parallel processing in any language. For readers who know the basics of programming. Grokking Concurrency is the ultimate guide to effective concurrency practices that will help you leverage multiple cores, excel with high loads, handle terabytes of data, and continue working after hardware and software failures. The core concepts in this guide will remain eternally relevant, whether you are building web apps, IoT systems, or handling big data. Specifically, you will: Get up to speed with the core concepts of concurrency, asynchrony, and parallel programming Learn the strengths and weaknesses of different hardware architectures Improve the sequential performance characteristics of your software Solve common problems for concurrent programming Compose patterns into a series of practices for writing scalable systems Write and implement concurrency systems that scale to any size Grokking Concurrency demystifies writing high-performance concurrent code through clear explanations of core concepts, interesting illustrations, insightful examples, and detailed techniques you can apply to your own projects. About the technology Microservices, big data, real-time systems, and other performance-intensive applications can all slow your systems to a crawl. You know the solution is “concurrency.” Now what? How do you choose among concurrency approaches? How can you be sure you will actually reduce latency and complete your jobs faster? This entertaining, fully illustrated guide answers all of your concurrency questions so you can start taking full advantage of modern multicore processors.Trade ReviewDon't be afraid about concurrency, learn from Grokking Concurrency! Eddu Melendez This book is a model of clarity. It clearly puts back not-so-well-known concepts in context. Luc Rogge The Manning Grokking series has a well deserved good reputation and this book will not let the series down. Patrick Regan

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Book SynopsisMordechai (Moti) Ben-Ari is an Associate Professor in the Department of Science Teaching at the Weizmann Institute of Science in Rehovot, Israel.  He is the author of texts on Ada, concurrent programming, programming languages, and mathematical logic, as well as Just a Theory: Exploring the Nature of Science.  In 2004 he was honored with the ACM/SIGCSE Award for Outstanding Contribution to Computer Science Education.Table of ContentsContents Preface xi 1 What is Concurrent Programming? 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Concurrency as abstract parallelism . . . . . . . . . . . . . . . . 2 1.3 Multitasking . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 The terminology of concurrency . . . . . . . . . . . . . . . . . 4 1.5 Multiple computers . . . . . . . . . . . . . . . . . . . . . . . . 5 1.6 The challenge of concurrent programming . . . . . . . . . . . . 5 2 The Concurrent Programming Abstraction 7 2.1 The role of abstraction . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Concurrent execution as interleaving of atomic statements . . . . 8 2.3 Justification of the abstraction . . . . . . . . . . . . . . . . . . . 13 2.4 Arbitrary interleaving . . . . . . . . . . . . . . . . . . . . . . . 17 2.5 Atomic statements . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.6 Correctness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.7 Fairness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.8 Machine-code instructions . . . . . . . . . . . . . . . . . . . . . 24 2.9 Volatile and non-atomic variables . . . . . . . . . . . . . . . . . 28 2.10 The BACI concurrency simulator . . . . . . . . . . . . . . . . . 29 2.11 Concurrency in Ada . . . . . . . . . . . . . . . . . . . . . . . . 31 2.12 Concurrency in Java . . . . . . . . . . . . . . . . . . . . . . . . 34 2.13 Writing concurrent programs in Promela . . . . . . . . . . . . . 36 2.14 Supplement: the state diagram for the frog puzzle . . . . . . . . 37 3 The Critical Section Problem 45 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.2 The definition of the problem . . . . . . . . . . . . . . . . . . . 45 3.3 First attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 3.4 Proving correctness with state diagrams . . . . . . . . . . . . . . 49 3.5 Correctness of the first attempt . . . . . . . . . . . . . . . . . . 53 3.6 Second attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.7 Third attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3.8 Fourth attempt . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.9 Dekker’s algorithm . . . . . . . . . . . . . . . . . . . . . . . . 60 3.10 Complex atomic statements . . . . . . . . . . . . . . . . . . . . 61 4 Verification of Concurrent Programs 67 4.1 Logical specification of correctness properties . . . . . . . . . . 68 4.2 Inductive proofs of invariants . . . . . . . . . . . . . . . . . . . 69 4.3 Basic concepts of temporal logic . . . . . . . . . . . . . . . . . 72 4.4 Advanced concepts of temporal logic . . . . . . . . . . . . . . . 75 4.5 A deductive proof of Dekker’s algorithm . . . . . . . . . . . . . 79 4.6 Model checking . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.7 Spin and the Promela modeling language . . . . . . . . . . . . . 83 4.8 Correctness specifications in Spin . . . . . . . . . . . . . . . . . 86 4.9 Choosing a verification technique . . . . . . . . . . . . . . . . . 88 5 Advanced Algorithms for the Critical Section Problem 93 5.1 The bakery algorithm . . . . . . . . . . . . . . . . . . . . . . . 93 5.2 The bakery algorithm for N processes . . . . . . . . . . . . . . 95 5.3 Less restrictive models of concurrency . . . . . . . . . . . . . . 96 5.4 Fast algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.5 Implementations in Promela . . . . . . . . . . . . . . . . . . . . 104

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Book SynopsisThis practical book covers design approaches and technologies that make it possible to scale an application quickly and cost-effectively. Author Ian Gorton takes software architects and developers through the principles of foundational distributed systems.

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Book SynopsisDesign, build, and operate scalable and reliable Kubernetes infrastructure for productionKey Features Implement industry best practices to build and manage production-grade Kubernetes infrastructure Learn how to architect scalable Kubernetes clusters, harden container security, and fine-tune resource management Understand, manage, and operate complex business workloads confidently Book DescriptionAlthough out-of-the-box solutions can help you to get a cluster up and running quickly, running a Kubernetes cluster that is optimized for production workloads is a challenge, especially for users with basic or intermediate knowledge. With detailed coverage of cloud industry standards and best practices for achieving scalability, availability, operational excellence, and cost optimization, this Kubernetes book is a blueprint for managing applications and services in production.You'll discover the most common way to deploy and operate Kubernetes clusters, which is to use a public cloud-managed service from AWS, Azure, or Google Cloud Platform (GCP). This book explores Amazon Elastic Kubernetes Service (Amazon EKS), the AWS-managed version of Kubernetes, for working through practical exercises. As you get to grips with implementation details specific to AWS and EKS, you'll understand the design concepts, implementation best practices, and configuration applicable to other cloud-managed services. Throughout the book, you'll also discover standard and cloud-agnostic tools, such as Terraform and Ansible, for provisioning and configuring infrastructure.By the end of this book, you'll be able to leverage Kubernetes to operate and manage your production environments confidently.What you will learn Explore different infrastructure architectures for Kubernetes deployment Implement optimal open source and commercial storage management solutions Apply best practices for provisioning and configuring Kubernetes clusters, including infrastructure as code (IaC) and configuration as code (CAC) Configure the cluster networking plugin and core networking components to get the best out of them Secure your Kubernetes environment using the latest tools and best practices Deploy core observability stacks, such as monitoring and logging, to fine-tune your infrastructure Who this book is forThis book is for cloud infrastructure experts, DevOps engineers, site reliability engineers, and engineering managers looking to design and operate Kubernetes infrastructure for production. Basic knowledge of Kubernetes, Terraform, Ansible, Linux, and AWS is needed to get the most out of this book.Table of ContentsTable of Contents Introduction to Kubernetes Infrastructure and Production-Readiness Architecting Production-Grade Kubernetes Infrastructure Provisioning Kubernetes Clusters Using AWS and Terraform Managing Cluster Configuration with Ansible Configuring and Enhancing Kubernetes Networking Services Securing Kubernetes Effectively Managing Storage and Stateful Applications Deploying Seamless and Reliable Applications Monitoring, Logging, and Observability Operating and Maintaining Efficient Kubernetes Clusters

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Book SynopsisThe latest techniques and principles of parallel and grid database processing The growth in grid databases, coupled with the utility of parallel query processing, presents an important opportunity to understand and utilize high-performance parallel database processing within a major database management system (DBMS). This important new book provides readers with a fundamental understanding of parallelism in data-intensive applications, and demonstrates how to develop faster capabilities to support them. It presents a balanced treatment of the theoretical and practical aspects of high-performance databases to demonstrate how parallel query is executed in a DBMS, including concepts, algorithms, analytical models, and grid transactions. High-Performance Parallel Database Processing and Grid Databases serves as a valuable resource for researchers working in parallel databases and for practitioners interested in building a high-performance database. It is also a mucTable of ContentsPreface xv Part I Introduction 1. Introduction 3 1.1. A Brief Overview: Parallel Databases and Grid Databases 4 1.2. Parallel Query Processing: Motivations 5 1.3. Parallel Query Processing: Objectives 7 1.3.1. Speed Up 7 1.3.2. Scale Up 8 1.3.3. Parallel Obstacles 10 1.4. Forms of Parallelism 12 1.4.1. Interquery Parallelism 13 1.4.2. Intraquery Parallelism 14 1.4.3. Intraoperation Parallelism 15 1.4.4. Interoperation Parallelism 15 1.4.5. Mixed Parallelism—A More Practical Solution 18 1.5. Parallel Database Architectures 19 1.5.1. Shared-Memory and Shared-Disk Architectures 20 1.5.2. Shared-Nothing Architecture 22 1.5.3. Shared-Something Architecture 23 1.5.4. Interconnection Networks 24 1.6. Grid Database Architecture 26 1.7. Structure of this Book 29 1.8. Summary 30 1.9. Bibliographical Notes 30 1.10. Exercises 31 2. Analytical Models 33 2.1. Cost Models 33 2.2. Cost Notations 34 2.2.1. Data Parameters 34 2.2.2. Systems Parameters 36 2.2.3. Query Parameters 37 2.2.4. Time Unit Costs 37 2.2.5. Communication Costs 38 2.3. Skew Model 39 2.4. Basic Operations in Parallel Databases 43 2.4.1. Disk Operations 44 2.4.2. Main Memory Operations 45 2.4.3. Data Computation and Data Distribution 45 2.5. Summary 47 2.6. Bibliographical Notes 47 2.7. Exercises 47 Part II Basic Query Parallelism 3. Parallel Search 51 3.1. Search Queries 51 3.1.1. Exact-Match Search 52 3.1.2. Range Search Query 53 3.1.3. Multiattribute Search Query 54 3.2. Data Partitioning 54 3.2.1. Basic Data Partitioning 55 3.2.2. Complex Data Partitioning 60 3.3. Search Algorithms 69 3.3.1. Serial Search Algorithms 69 3.3.2. Parallel Search Algorithms 73 3.4. Summary 74 3.5. Bibliographical Notes 75 3.6. Exercises 75 4. Parallel Sort and GroupBy 77 4.1. Sorting, Duplicate Removal, and Aggregate Queries 78 4.1.1. Sorting and Duplicate Removal 78 4.1.2. Scalar Aggregate 79 4.1.3. GroupBy 80 4.2. Serial External Sorting Method 80 4.3. Algorithms for Parallel External Sort 83 4.3.1. Parallel Merge-All Sort 83 4.3.2. Parallel Binary-Merge Sort 85 4.3.3. Parallel Redistribution Binary-Merge Sort 86 4.3.4. Parallel Redistribution Merge-All Sort 88 4.3.5. Parallel Partitioned Sort 90 4.4. Parallel Algorithms for GroupBy Queries 92 4.4.1. Traditional Methods (Merge-All and Hierarchical Merging) 92 4.4.2. Two-Phase Method 93 4.4.3. Redistribution Method 94 4.5. Cost Models for Parallel Sort 96 4.5.1. Cost Models for Serial External Merge-Sort 96 4.5.2. Cost Models for Parallel Merge-All Sort 98 4.5.3. Cost Models for Parallel Binary-Merge Sort 100 4.5.4. Cost Models for Parallel Redistribution Binary-Merge Sort 101 4.5.5. Cost Models for Parallel Redistribution Merge-All Sort 102 4.5.6. Cost Models for Parallel Partitioned Sort 103 4.6. Cost Models for Parallel GroupBy 104 4.6.1. Cost Models for Parallel Two-Phase Method 104 4.6.2. Cost Models for Parallel Redistribution Method 107 4.7. Summary 109 4.8. Bibliographical Notes 110 4.9. Exercises 110 5. Parallel Join 112 5.1. Join Operations 112 5.2. Serial Join Algorithms 114 5.2.1. Nested-Loop Join Algorithm 114 5.2.2. Sort-Merge Join Algorithm 116 5.2.3. Hash-Based Join Algorithm 117 5.2.4. Comparison 120 5.3. Parallel Join Algorithms 120 5.3.1. Divide and Broadcast-Based Parallel Join Algorithms 121 5.3.2. Disjoint Partitioning-Based Parallel Join Algorithms 124 5.4. Cost Models 128 5.4.1. Cost Models for Divide and Broadcast 128 5.4.2. Cost Models for Disjoint Partitioning 129 5.4.3. Cost Models for Local Join 130 5.5. Parallel Join Optimization 132 5.5.1. Optimizing Main Memory 132 5.5.2. Load Balancing 133 5.6. Summary 134 5.7. Bibliographical Notes 135 5.8. Exercises 136 Part III Advanced Parallel Query Processing 6. Parallel GroupBy-Join 141 6.1. Groupby-Join Queries 141 6.1.1. Groupby Before Join 142 6.1.2. Groupby After Join 142 6.2. Parallel Algorithms for Groupby-Before-Join Query Processing 143 6.2.1. Early Distribution Scheme 143 6.2.2. Early GroupBy with Partitioning Scheme 145 6.2.3. Early GroupBy with Replication Scheme 146 6.3. Parallel Algorithms for Groupby-After-Join Query Processing 148 6.3.1. Join Partitioning Scheme 148 6.3.2. GroupBy Partitioning Scheme 150 6.4. Cost Model Notations 151 6.5. Cost Model for Groupby-Before-Join Query Processing 153 6.5.1. Cost Models for the Early Distribution Scheme 153 6.5.2. Cost Models for the Early GroupBy with Partitioning Scheme 156 6.5.3. Cost Models for the Early GroupBy with Replication Scheme 158 6.6. Cost Model for “Groupby-After-Join” Query Processing 159 6.6.1. Cost Models for the Join Partitioning Scheme 159 6.6.2. Cost Models for the GroupBy Partitioning Scheme 161 6.7. Summary 163 6.8. Bibliographical Notes 164 6.9. Exercises 164 7. Parallel Indexing 167 7.1. Parallel Indexing–an Internal Perspective on Parallel Indexing Structures 168 7.2. Parallel Indexing Structures 169 7.2.1. Nonreplicated Indexing (NRI) Structures 169 7.2.2. Partially Replicated Indexing (PRI) Structures 171 7.2.3. Fully Replicated Indexing (FRI) Structures 178 7.3. Index Maintenance 180 7.3.1. Maintaining a Parallel Nonreplicated Index 182 7.3.2. Maintaining a Parallel Partially Replicated Index 182 7.3.3. Maintaining a Parallel Fully Replicated Index 188 7.3.4. Complexity Degree of Index Maintenance 188 7.4. Index Storage Analysis 188 7.4.1. Storage Cost Models for Uniprocessors 189 7.4.2. Storage Cost Models for Parallel Processors 191 7.5. Parallel Processing of Search Queries using Index 192 7.5.1. Parallel One-Index Search Query Processing 192 7.5.2. Parallel Multi-Index Search Query Processing 195 7.6. Parallel Index Join Algorithms 200 7.6.1. Parallel One-Index Join 200 7.6.2. Parallel Two-Index Join 203 7.7. Comparative Analysis 207 7.7.1. Comparative Analysis of Parallel Search Index 207 7.7.2. Comparative Analysis of Parallel Index Join 213 7.8. Summary 216 7.9. Bibliographical Notes 217 7.10. Exercises 217 8. Parallel Universal Qualification—Collection Join Queries 219 8.1. Universal Quantification and Collection Join 220 8.2. Collection Types and Collection Join Queries 222 8.2.1. Collection-Equi Join Queries 222 8.2.2. Collection–Intersect Join Queries 223 8.2.3. Subcollection Join Queries 224 8.3. Parallel Algorithms for Collection Join Queries 225 8.4. Parallel Collection-Equi Join Algorithms 225 8.4.1. Disjoint Data Partitioning 226 8.4.2. Parallel Double Sort-Merge Collection-Equi Join Algorithm 227 8.4.3. Parallel Sort-Hash Collection-Equi Join Algorithm 228 8.4.4. Parallel Hash Collection-Equi Join Algorithm 232 8.5. Parallel Collection-Intersect Join Algorithms 233 8.5.1. Non-Disjoint Data Partitioning 234 8.5.2. Parallel Sort-Merge Nested-Loop Collection-Intersect Join Algorithm 244 8.5.3. Parallel Sort-Hash Collection-Intersect Join Algorithm 245 8.5.4. Parallel Hash Collection-Intersect Join Algorithm 246 8.6. Parallel Subcollection Join Algorithms 246 8.6.1. Data Partitioning 247 8.6.2. Parallel Sort-Merge Nested-Loop Subcollection Join Algorithm 248 8.6.3. Parallel Sort-Hash Subcollection Join Algorithm 249 8.6.4. Parallel Hash Subcollection Join Algorithm 251 8.7. Summary 252 8.8. Bibliographical Notes 252 8.9. Exercises 254 9. Parallel Query Scheduling and Optimization 256 9.1. Query Execution Plan 257 9.2. Subqueries Execution Scheduling Strategies 259 9.2.1. Serial Execution Among Subqueries 259 9.2.2. Parallel Execution Among Subqueries 261 9.3. Serial vs. Parallel Execution Scheduling 264 9.3.1. Nonskewed Subqueries 264 9.3.2. Skewed Subqueries 265 9.3.3. Skewed and Nonskewed Subqueries 267 9.4. Scheduling Rules 269 9.5. Cluster Query Processing Model 270 9.5.1. Overview of Dynamic Query Processing 271 9.5.2. A Cluster Query Processing Architecture 272 9.5.3. Load Information Exchange 273 9.6. Dynamic Cluster Query Optimization 275 9.6.1. Correction 276 9.6.2. Migration 280 9.6.3. Partition 281 9.7. Other Approaches to Dynamic Query Optimization 284 9.8. Summary 285 9.9. Bibliographical Notes 286 9.10. Exercises 286 Part IV Grid Databases 10. Transactions in Distributed and Grid Databases 291 10.1. Grid Database Challenges 292 10.2. Distributed Database Systems and Multidatabase Systems 293 10.2.1. Distributed Database Systems 293 10.2.2. Multidatabase Systems 297 10.3. Basic Definitions on Transaction Management 299 10.4. Acid Properties of Transactions 301 10.5. Transaction Management in Various Database Systems 303 10.5.1. Transaction Management in Centralized and Homogeneous Distributed Database Systems 303 10.5.2. Transaction Management in Heterogeneous Distributed Database Systems 305 10.6. Requirements in Grid Database Systems 307 10.7. Concurrency Control Protocols 309 10.8. Atomic Commit Protocols 310 10.8.1. Homogeneous Distributed Database Systems 310 10.8.2. Heterogeneous Distributed Database Systems 313 10.9. Replica Synchronization Protocols 314 10.9.1. Network Partitioning 315 10.9.2. Replica Synchronization Protocols 316 10.10. Summary 318 10.11. Bibliographical Notes 318 10.12. Exercises 319 11. Grid Concurrency Control 321 11.1. A Grid Database Environment 321 11.2. An Example 322 11.3. Grid Concurrency Control 324 11.3.1. Basic Functions Required by GCC 324 11.3.2. Grid Serializability Theorem 325 11.3.3. Grid Concurrency Control Protocol 329 11.3.4. Revisiting the Earlier Example 333 11.3.5. Comparison with Traditional Concurrency Control Protocols 334 11.4. Correctness of GCC Protocol 336 11.5. Features of GCC Protocol 338 11.6. Summary 339 11.7. Bibliographical Notes 339 11.8. Exercises 339 12. Grid Transaction Atomicity and Durability 341 12.1. Motivation 342 12.2. Grid Atomic Commit Protocol (Grid-ACP) 343 12.2.1. State Diagram of Grid-ACP 343 12.2.2. Grid-ACP Algorithm 344 12.2.3. Early-Abort Grid-ACP 346 12.2.4. Discussion 348 12.2.5. Message and Time Complexity Comparison Analysis 349 12.2.6. Correctness of Grid-ACP 350 12.3. Handling Failure of Sites with Grid-ACP 351 12.3.1. Model for Storing Log Files at the Originator and Participating Sites 351 12.3.2. Logs Required at the Originator Site 352 12.3.3. Logs Required at the Participant Site 353 12.3.4. Failure Recovery Algorithm for Grid-ACP 353 12.3.5. Comparison of Recovery Protocols 359 12.3.6. Correctness of Recovery Algorithm 361 12.4. Summary 365 12.5. Bibliographical Notes 366 12.6. Exercises 366 13. Replica Management in Grids 367 13.1. Motivation 367 13.2. Replica Architecture 368 13.2.1. High-Level Replica Management Architecture 368 13.2.2. Some Problems 369 13.3. Grid Replica Access Protocol (GRAP) 371 13.3.1. Read Transaction Operation for GRAP 371 13.3.2. Write Transaction Operation for GRAP 372 13.3.3. Revisiting the Example Problem 375 13.3.4. Correctness of GRAP 377 13.4. Handling Multiple Partitioning 378 13.4.1. Contingency GRAP 378 13.4.2. Comparison of Replica Management Protocols 381 13.4.3. Correctness of Contingency GRAP 383 13.5. Summary 384 13.6. Bibliographical Notes 385 13.7. Exercises 385 14. Grid Atomic Commitment in Replicated Data 387 14.1. Motivation 388 14.1.1. Architectural Reasons 388 14.1.2. Motivating Example 388 14.2. Modified Grid Atomic Commitment Protocol 390 14.2.1. Modified Grid-ACP 390 14.2.2. Correctness of Modified Grid-ACP 393 14.3. Transaction Properties in Replicated Environment 395 14.4. Summary 397 14.5. Bibliographical Notes 397 14.6. Exercises 398 Part V Other Data-Intensive Applications 15. Parallel Online Analytic Processing (OLAP) and Business Intelligence 401 15.1. Parallel Multidimensional Analysis 402 15.2. Parallelization of ROLLUP Queries 405 15.2.1. Analysis of Basic Single ROLLUP Queries 405 15.2.2. Analysis of Multiple ROLLUP Queries 409 15.2.3. Analysis of Partial ROLLUP Queries 411 15.2.4. Parallelization Without Using ROLLUP 412 15.3. Parallelization of CUBE Queries 412 15.3.1. Analysis of Basic CUBE Queries 413 15.3.2. Analysis of Partial CUBE Queries 416 15.3.3. Parallelization Without Using CUBE 417 15.4. Parallelization of Top-N and Ranking Queries 418 15.5. Parallelization of Cume_Dist Queries 419 15.6. Parallelization of NTILE and Histogram Queries 420 15.7. Parallelization of Moving Average and Windowing Queries 422 15.8. Summary 424 15.9. Bibliographical Notes 424 15.10. Exercises 425 16. Parallel Data Mining—Association Rules and Sequential Patterns 427 16.1. From Databases To Data Warehousing To Data Mining: A Journey 428 16.2. Data Mining: A Brief Overview 431 16.2.1. Data Mining Tasks 431 16.2.2. Querying vs. Mining 433 16.2.3. Parallelism in Data Mining 436 16.3. Parallel Association Rules 440 16.3.1. Association Rules: Concepts 441 16.3.2. Association Rules: Processes 444 16.3.3. Association Rules: Parallel Processing 448 16.4. Parallel Sequential Patterns 450 16.4.1. Sequential Patterns: Concepts 452 16.4.2. Sequential Patterns: Processes 456 16.4.3. Sequential Patterns: Parallel Processing 459 16.5. Summary 461 16.6. Bibliographical Notes 461 16.7. Exercises 462 17. Parallel Clustering and Classification 464 17.1. Clustering and Classification 464 17.1.1. Clustering 464 17.1.2. Classification 465 17.2. Parallel Clustering 467 17.2.1. Clustering: Concepts 467 17.2.2. k-Means Algorithm 468 17.2.3. Parallel k-Means Clustering 471 17.3. Parallel Classification 477 17.3.1. Decision Tree Classification: Structures 477 17.3.2. Decision Tree Classification: Processes 480 17.3.3. Decision Tree Classification: Parallel Processing 488 17.4. Summary 495 17.5. Bibliographical Notes 498 17.6. Exercises 498 Permissions 501 List of Conferences and Journals 507 Bibliography 511 Index 541

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Book SynopsisFocuses on the area of parallel and distributed computing, and considers the diverse approaches. Covering a comprehensive set of models and paradigms, this book serves as both an introduction and a survey. It is suitable for students and can be used as a foundation for parallel and distributed computing courses.Trade Review"A supplemental text providing a framework within which individual topics can be elaborated on in...courses...or a survey that researchers can consult before choosing a set of models and paradigms for the overlapping approaches to programming." (SciTech Book News, Vol. 25, No. 3, September 2001) "an excellent introduction to the field of parallel computing .." (CVu - Jnl of the Association C & C++ Users, February 2002)Table of ContentsArchitectures. Data Parallelism. Shared-Memory Programming. Message Passing. Client/Server Computing. Code Mobility. Coordination Models. Object-Oriented Models. High-Level Programming Models. Abstract Models. Final Comparison. References. Index.

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Book SynopsisEdsger Wybe Dijkstra (1930-2002) was one of the most influential researchers in the history of computer science, making fundamental contributions to both the theory and practice of computing. In this book, 31 computer scientists present and discuss Dijkstra’s numerous contributions to computing science and assess their impact.

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Book SynopsisEdsger Wybe Dijkstra (1930-2002) was one of the most influential researchers in the history of computer science, making fundamental contributions to both the theory and practice of computing. In this book, 31 computer scientists present and discuss Dijkstra’s numerous contributions to computing science and assess their impact.

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Book SynopsisThis text provides an introduction to the methods of parallel optimization by introducing parallel computing ideas and techniques into both optimization theory and numerical algorithms for large-scale optimization problems.Trade Review"This book presents a domain that arises where two different branches of science, namely parallel computations and the theory of constrained optimization, intersect with real life problems. This domain, called parallel optimization, has been developing rapidly under the stimulus of progress in computer technology. The book focuses on parallel optimization methods for large-scale constrained optimization problems and structured linear problems. . . . [It] covers a vast portion of parallel optimization, though full coverage of this domain, as the authors admit, goes far beyond the capacity of a single monograph. This book, however, in over 500 pages brings an excellent and in-depth presentation of all the major aspects of a process which matches theory and methods of optimization with modern computers. The volume can be recommended for graduate students, faculty, and researchers in any of those fields."--Mathematical Reviews "This book presents a domain that arises where two different branches of science, namely parallel computations and the theory of constrained optimization, intersect with real life problems. This domain, called parallel optimization, has been developing rapidly under the stimulus of progress in computer technology. The book focuses on parallel optimization methods for large-scale constrained optimization problems and structured linear problems. . . . [It] covers a vast portion of parallel optimization, though full coverage of this domain, as the authors admit, goes far beyond the capacity of a single monograph. This book, however, in over 500 pages brings an excellent and in-depth presentation of all the major aspects of a process which matches theory and methods of optimization with modern computers. The volume can be recommended for graduate students, faculty, and researchers in any of those fields."--Mathematical ReviewsTable of ContentsForeword ; Preface ; Glossary of Symbols ; 1. Introduction ; Part I Theory ; 2. Generalized Distances and Generalized Projections ; 3. Proximal Minimization with D-Functions ; Part II Algorithms ; 4. Penalty Methods, Barrier Methods and Augmented Lagrangians ; 5. Iterative Methods for Convex Feasibility Problems ; 6. Iterative Algorithms for Linearly Constrained Optimization Problems ; 7. Model Decomposition Algorithms ; 8. Decompositions in Interior Point Algorithms ; Part III Applications ; 9. Matrix Estimation Problems ; 10. Image Reconsturction from Projections ; 11. The Inverse Problem in Radiation Therapy Treatment Planning ; 12. Multicommodity Network Flow Problems ; 13. Planning Under Uncertainty ; 14. Decompositions for Parallel Computing ; 15. Numerical Investigations

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisA comprehensive guide to help aspiring and professional C++ developers elevate the performance of their apps by allowing them to run faster and consume fewer resources.Purchase of the print or Kindle book includes a free eBook in PDF format.Key Features Updated to C++20 with completely revised code and more content on error handling, benchmarking, memory allocators, and concurrent programming Explore the latest C++20 features including concepts, ranges, and coroutines Utilize C++ constructs and techniques to carry out effective data structure optimization and memory management Book DescriptionC++ High Performance, Second Edition guides you through optimizing the performance of your C++ apps. This allows them to run faster and consume fewer resources on the device they're running on without compromising the readability of your codebase. The book begins by introducing the C++ language and some of its modern concepts in brief. Once you are familiar with the fundamentals, you will be ready to measure, identify, and eradicate bottlenecks in your C++ codebase. By following this process, you will gradually improve your style of writing code. The book then explores data structure optimization, memory management, and how it can be used efficiently concerning CPU caches. After laying the foundation, the book trains you to leverage algorithms, ranges, and containers from the standard library to achieve faster execution, write readable code, and use customized iterators. It provides hands-on examples of C++ metaprogramming, coroutines, reflection to reduce boilerplate code, proxy objects to perform optimizations under the hood, concurrent programming, and lock-free data structures. The book concludes with an overview of parallel algorithms. By the end of this book, you will have the ability to use every tool as needed to boost the efficiency of your C++ projects.What you will learn Write specialized data structures for performance-critical code Use modern metaprogramming techniques to reduce runtime calculations Achieve efficient memory management using custom memory allocators Reduce boilerplate code using reflection techniques Reap the benefits of lock-free concurrent programming Gain insights into subtle optimizations used by standard library algorithms Compose algorithms using ranges library Develop the ability to apply metaprogramming aspects such as constexpr, constraints, and concepts Implement lazy generators and asynchronous tasks using C++20 coroutines Who this book is forIf you're a C++ developer looking to improve the efficiency of your code or just keen to upgrade your skills to the next level, this book is for you.Table of ContentsTable of Contents A Brief Introduction to C++ Essential C++ Techniques Analyzing and Measuring Performance Data Structures Algorithms Using Ranges and Views Memory Management Compile-Time Programming Essential Utilities Proxy Objects and Lazy Evaluation Concurrency Coroutines and Lazy Generators Asynchronous Programming with Coroutines Parallel STL Algorithms

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