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C++ High Performance

C++ High Performance

By : Björn Andrist, Viktor Sehr
4.1 (21)
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C++ High Performance

C++ High Performance

4.1 (21)
By: Björn Andrist, Viktor Sehr

Overview of this book

C++ is a highly portable language and can be used to write both large-scale applications and performance-critical code. It has evolved over the last few years to become a modern and expressive language. This book will guide you through optimizing the performance of your C++ apps by allowing them to run faster and consume fewer resources on the device they're running on without compromising the readability of your code base. The book begins by helping you measure and identify bottlenecks in a C++ code base. It then moves on by teaching you how to use modern C++ constructs and techniques. You'll see how this affects the way you write code. Next, you'll see the importance of data structure optimization and memory management, and how it can be used efficiently with respect to CPU caches. After that, you'll see how STL algorithm and composable Range V3 should be used to both achieve faster execution and more readable code, followed by how to use STL containers and how to write your own specialized iterators. Moving on, you’ll get hands-on experience in making use of modern C++ metaprogramming and reflection to reduce boilerplate code as well as in working with proxy objects to perform optimizations under the hood. After that, you’ll learn concurrent programming and understand lock-free data structures. The book ends with an overview of parallel algorithms using STL execution policies, Boost Compute, and OpenCL to utilize both the CPU and the GPU.
Table of Contents (19 chapters)
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Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface
2
Index

What makes concurrent programming hard?


There are a couple of reasons why concurrent programming is hard, and, if you have been writing concurrent programs before, you have most likely already encountered the ones listed here:

  1. Sharing state between multiple threads in a safe manner is hard. Whenever we have data that can be read and written to at the same time, we need some way of protecting that data from data races. We will see a lot of examples of this later on.
  1. Concurrent programs are usually more complicated to reason about because of the multiple parallel execution flows.
  2. Concurrency complicates debugging. Bugs that occur because of data races can be very hard to debug since they are dependent on how threads are being scheduled. These kinds of bugs can be hard to reproduce and in the worst case, they cease to exist when running the program using a debugger. Sometimes an innocent debug trace to the console could change the way a multithreaded program behaves and make the bug temporarily...

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