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The art of concurrency /
If you're looking to take full advantage of multi-core processors with concurrent programming, this practical book provides the knowledge and hands-on experience you need. The Art of Concurrency is one of the few resources to focus on implementing algorithms in the shared-memory model of multi-...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Beijing ; Cambridge :
O'Reilly,
©2009.
|
| Edición: | 1st ed. |
| Colección: | Theory in practice
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a If you're looking to take full advantage of multi-core processors with concurrent programming, this practical book provides the knowledge and hands-on experience you need. The Art of Concurrency is one of the few resources to focus on implementing algorithms in the shared-memory model of multi-core processors, rather than just theoretical models or distributed-memory architectures. The book provides detailed explanations and usable samples to help you transform algorithms from serial to parallel code, along with advice and analysis for avoiding mistakes that programmers typically make when first attempting these computations. Written by an Intel engineer with over two decades of parallel and concurrent programming experience, this book will help you:<p Understand parallelism and concurrency Explore differences between programming for shared-memory and distributed-memory Learn guidelines for designing multithreaded applications, including testing and tuning Discover how to make best use of different threading libraries, including Windows threads, POSIX threads, OpenMP, and Intel Threading Building Blocks Explore how to implement concurrent algorithms that involve sorting, searching, graphs, and other practical computations The Art of Concurrency shows you how to keep algorithms scalable to take advantage of new processors with even more cores. For developing parallel code algorithms for concurrent programming, this book is a must. | ||
| 505 | 0 | |a Table of Contents; Preface; Why Should You Read This Book?; Who Is This Book For?; What's in This Book?; Conventions Used in This Book; Using Code Examples; Comments and Questions; Safari® Books Online; Acknowledgments; Chapter 1. Want to Go Faster? Raise Your Hands if You Want to Go Faster!; Some Questions You May Have; What Is a Thread Monkey?; Parallelism and Concurrency: What's the Difference?; Why Do I Need to Know This? What's in It for Me?; Isn't Concurrent Programming Hard?; Aren't Threads Dangerous?; Four Steps of a Threading Methodology | |
| 505 | 8 | |a Step 1. Analysis: Identify Possible ConcurrencyStep 2. Design and Implementation: Threading the Algorithm; Step 3. Test for Correctness: Detecting and Fixing Threading Errors; Step 4. Tune for Performance: Removing Performance Bottlenecks; The testing and tuning cycle; What About Concurrency from Scratch?; Background of Parallel Algorithms; Theoretical Models; Distributed-Memory Programming; Parallel Algorithms Literature; Shared-Memory Programming Versus Distributed-Memory Programming; Common Features; Redundant work; Dividing work; Sharing data; Static/dynamic allocation of work | |
| 505 | 8 | |a Features Unique to Shared MemoryLocal declarations and thread-local storage; Memory effects; Communication in memory; Mutual exclusion; Producer/consumer; Readers/writer locks; This Book's Approach to Concurrent Programming; Chapter 2. Concurrent or Not Concurrent?; Design Models for Concurrent Algorithms; Task Decomposition; What are the tasks and how are they defined?; What are the dependencies between tasks and how can they be satisfied?; How are the tasks assigned to threads?; Example: numerical integration; Data Decomposition; How should you divide the data into chunks? | |
| 505 | 8 | |a How can you ensure that the tasks for each chunk have access to all data required for updates?How are the data chunks (and tasks) assigned to threads?; Example: Game of Life on a finite grid; Concurrent Design Models Wrap-Up; What's Not Parallel; Algorithms with State; Recurrences; Induction Variables; Reduction; Loop-Carried Dependence; Not-so-typical loop-carried dependence; Chapter 3. Proving Correctness and Measuring Performance; Verification of Parallel Algorithms; Example: The Critical Section Problem; First Attempt; Second Attempt; Third Attempt; Fourth Attempt; Dekker's Algorithm | |
| 505 | 8 | |a Case 1Case 2a: T0 is the favored thread; Case 2b: T1 is the favored thread; Case 3; What about indefinite postponement?; What Did You Learn?; There Are No Evil Threads, Just Threads Programmed for Evil; Performance Metrics (How Am I Doing?); Speedup; Amdahl's Law; Gustafson-Barsis's Law; Efficiency; One Final Note on Speedup and Efficiency; Review of the Evolution for Supporting Parallelism in Hardware; Chapter 4. Eight Simple Rules for Designing Multithreaded Applications; Rule 1: Identify Truly Independent Computations; Rule 2: Implement Concurrency at the Highest Level Possible | |
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