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In today’s fast and competitive world, a program’s performance is just as important to customers as the features it provides. This practical guide teaches developers performance-tuning principles that enable optimization in C++. You’ll learn how to make code that already embodies best practices of C++ design run faster and consume fewer resources on any computer—whether it’s a watch, phone, workstation, supercomputer, or globe-spanning network of servers. Author Kurt Guntheroth provides several running examples that demonstrate how to apply these principles incrementally to improve existing code so it meets customer requirements for responsiveness and throughput. The advice in this book will prove itself the first time you hear a colleague exclaim, “Wow, that was fast. Who fixed something?” Locate performance hot spots using the profiler and software timers Learn to perform repeatable experiments to measure performance of code changes Optimize use of dynamically allocated variables Improve performance of hot loops and functions Speed up string handling functions Recognize efficient algorithms and optimization patterns Learn the strengths—and weaknesses—of C++ container classes View searching and sorting through an optimizer’s eye Make efficient use of C++ streaming I/O functions Use C++ thread-based concurrency features effectively
This guide fills in the knowledge gaps for intermediate-advanced programmers who have been trained without regard to software performance from a program level. Information on advanced tips and cautions can prepare programmers for cross-platform or multilanguage projects.
This book contains discussion of some of the most typical mistakes made by programmers in C++ and also some recipes for how to avoid each of these mistakes.
Far too many programmers and software designers consider efficient C++ to be an oxymoron. They regard C++ as inherently slow and inappropriate for performance-critical applications. Consequently, C++ has had little success penetrating domains such as networking, operating system kernels, device drivers, and others. "Efficient C++" explodes that myth. Written by two authors with first-hand experience wringing the last ounce of performance from commercial C++ applications, this book demonstrates the potential of C++ to produce highly efficient programs. The book reveals practical, everyday object-oriented design principles and C++ coding techniques that can yield large performance improvements. It points out common pitfalls in both design and code that generate hidden operating costs. This book focuses on combining C++'s power and flexibility with high performance and scalability, resulting in the best of both worlds. Specific topics include temporary objects, memory management, templates, inheritance, virtual functions, inlining, reference-counting, STL, and much more. With this book, you will have a valuable compendium of the best performance techniques at your fingertips. 0201379503B04062001
C++ Recipes: A Problem-Solution Approach is a handy code cookbook reference guide that cover the latest C++ 14 as well as some of the code templates available in the latest Standard Template Library (STL). In this handy reference, you'll find numbers, strings, dates, times, classes, exceptions, streams, flows, pointers and more. Also, you'll see various code samples, templates for C++ algorithms, parallel processing, multithreading and numerical processes. These have many applications including game development, big data analytics, financial engineering and analysis, enterprise applications and more. A wealth of STL templates on function objects, adapters, allocators, and extensions are also available. This is a "must have", contemporary reference for your technical library.
Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of reusable classes that simplifies the implementation of any data clustering algorithm. Readers can follow the development of the base data clustering classes and several popular data clustering algorithms. Additional topics such as data pre-processing, data visualization, cluster visualization, and cluster interpretation are briefly covered. This book is divided into three parts-- Data Clustering and C++ Preliminaries: A review of basic concepts of data clustering, the unified modeling language, object-oriented programming in C++, and design patterns A C++ Data Clustering Framework: The development of data clustering base classes Data Clustering Algorithms: The implementation of several popular data clustering algorithms A key to learning a clustering algorithm is to implement and experiment the clustering algorithm. Complete listings of classes, examples, unit test cases, and GNU configuration files are included in the appendices of this book as well as in the CD-ROM of the book. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.

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