Write High-Performance, Highly Scalable Multicore Applications for Leading Platforms Multicore Application Programming is a comprehensive, practical guide to high-performance multicore programming that any experienced developer can use.   Author Darryl Gove covers the leading approaches to parallelization on Windows, Linux, and Oracle Solaris. Through practical examples, he illuminates the challenges involved in writing applications that fully utilize multicore processors, helping you produce applications that are functionally correct, offer superior performance, and scale well to eight cores, sixteen cores, and beyond.   The book reveals how specific hardware implementations impact application performance and shows how to avoid common pitfalls. Step by step, you’ll write applications that can handle large numbers of parallel threads, and you’ll master advanced parallelization techniques. You’ll learn how to   Identify your best opportunities to use parallelism Share data safely between multiple threads Write applications using POSIX or Windows threads Hand-code synchronization and sharing Take advantage of automatic parallelization and OpenMP Overcome common obstacles to scaling Apply new approaches to writing correct, fast, scalable parallel code   Multicore Application Programming isn’t wedded to a single approach or platform: It is for every experienced C programmer working with any contemporary multicore processor in any leading operating system environment.

Table of Contents:
Preface xv Acknowledgments xix About the Author xxi   Chapter 1: Hardware, Processes, and Threads 1 Examining the Insides of a Computer 1 The Motivation for Multicore Processors 3 The Characteristics of Multiprocessor Systems 18 The Translation of Source Code to Assembly Language 21 Summary 29   Chapter 2: Coding for Performance 31 Defining Performance 31 Understanding Algorithmic Complexity 33 How Structure Impacts Performance 39 The Role of the Compiler 60 Identifying Where Time Is Spent Using Profiling 74 How Not to Optimize 80 Performance by Design 82 Summary 83   Chapter 3: Identifying Opportunities for Parallelism 85 Using Multiple Processes to Improve System Productivity 85 Multiple Users Utilizing a Single System 87 Improving Machine Efficiency Through Consolidation 88 Using Parallelism to Improve the Performance of a Single Task 92 Parallelization Patterns 100 How Dependencies Influence the Ability Run Code in Parallel 110 Identifying Parallelization Opportunities 118 Summary 119   Chapter 4: Synchronization and Data Sharing 121 Data Races 121 Synchronization Primitives 126 Deadlocks and Livelocks 132 Communication Between Threads and Processes 133 Storing Thread-Private Data 141 Summary 142   Chapter 5: Using POSIX Threads 143 Creating Threads 143 Compiling Multithreaded Code 151 Process Termination 153 Sharing Data Between Threads 154 Variables and Memory 175 Multiprocess Programming 179 Sockets 193 Reentrant Code and Compiler Flags 197 Summary 198   Chapter 6: Windows Threading 199 Creating Native Windows Threads 199 Methods of Synchronization and Resource Sharing 208 Wide String Handling in Windows 221 Creating Processes 222 Atomic Updates of Variables 238 Allocating Thread-Local Storage 240 Setting Thread Priority 242 Summary 244   Chapter 7: Using Automatic Parallelization and OpenMP 245 Using Automatic Parallelization to Produce a Parallel Application 245 Using OpenMP to Produce a Parallel Application 256 Ensuring That Code in a Parallel Region Is Executed in Order 285 Collapsing Loops to Improve Workload Balance 286 Enforcing Memory Consistency 287 An Example of Parallelization 288 Summary 293   Chapter 8: Hand-Coded Synchronization and Sharing 295 Atomic Operations 295 Operating System–Provided Atomics 309 Lockless Algorithms 312 Summary 332   Chapter 9: Scaling with Multicore Processors 333 Constraints to Application Scaling 333 Hardware Constraints to Scaling 352 Operating System Constraints to Scaling 369 Multicore Processors and Scaling 380 Summary 381   Chapter 10: Other Parallelization Technologies 383 GPU-Based Computing 383 Language Extensions 386 Alternative Languages 399 Clustering Technologies 402 Transactional Memory 407 Vectorization 408 Summary 409   Chapter 11: Concluding Remarks 411 Writing Parallel Applications 411 Parallel Code on Multicore Processors 414 The Future 416   Bibliography 417 Index 419