C++17性能优化：提升代码速度的实战指南

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本资源是一本名为《C++高性能编程：提升C++17代码性能》的专业书籍，由Viktor Sehr和Björn Andrist合著，由Packt Publishing出版于2018年。本书专注于C++17版本，旨在帮助读者理解和掌握如何通过优化技术来提高程序的执行效率。作者们在书中详细探讨了各种高级性能优化策略，包括但不限于内存管理、并发编程、算法优化以及利用C++17新特性进行代码重构。书中的内容涵盖了以下几个关键知识点： 1. **C++17语言特性**：介绍C++17标准的新特性和改进，如范围-based for循环、初始化列表、fold表达式、structured bindings等，这些特性有助于编写更简洁且高效的代码。 2. **内存优化**：讲解如何避免内存泄漏，合理使用RAII（Resource Acquisition Is Initialization）和智能指针，以及如何利用现代内存管理和STL容器来减少内存开销。 3. **并发编程最佳实践**：讨论线程池、条件变量、锁和互斥量的使用，以及如何设计无锁数据结构和并发算法，以降低竞态条件和死锁的风险。 4. **算法优化**：提供性能基准测试和分析技巧，介绍如何选择正确的排序算法、查找算法，以及如何使用模板元编程和编译时计算提高代码效率。 5. **编译器优化技巧**：讲解如何利用编译器提供的各种优化选项，如-O2、-O3级别优化，以及如何编写可编译器友好的代码，以便充分利用硬件加速。 6. **性能分析与调试**：介绍如何使用工具如gprof、Valgrind、Intel VTune等进行性能剖析，识别瓶颈并提出针对性的优化建议。 7. **案例研究与实战项目**：书中可能包含实际项目案例，展示如何将理论知识应用到实际问题中，帮助读者在实践中提升性能优化能力。 8. **版权和法律声明**：提醒读者关于版权和使用条款的信息，确保所有内容的合法使用，同时也说明了出版社对错误或遗漏的责任界限。《C++高性能编程：提升C++17代码性能》是一本实用的指南，适合C++开发者，特别是那些希望提升代码效率、适应现代C++标准和处理复杂性能挑战的专业人士。通过学习本书，读者可以深入理解C++语言，并学会一系列性能优化方法，从而打造更高效、更具竞争力的软件。

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Returning data and handling errors
Tasks
Atomic support in C++
Using shared_ptr in a multithreaded environment
C++ memory model
Instruction reordering
Atomics and memory orders
Lock-free programming
Lock-free queue example
Performance guidelines
Avoid contention
Avoid blocking operations
Number of threads/CPU cores
Thread priorities
Thread affinity
False sharing
Summary
11.  Parallel STL
Importance of parallelism
Parallel algorithms
Implementing parallel std::transform()
Naive implementation
Performance evaluation
Shortcomings of the naive implementation
Divide and conquer
Implementation
Performance evaluation
Implementing parallel std::count_if
Implementing parallel std::copy_if
Approach one – Use a synchronized write position
Inner function
Outer function
Approach two – Split algorithm into two parts
Part one – Copy elements in parallel into the destination range
Part two – Move the sparse range sequentially into a continuous range
Performance evaluation
Parallel STL
Execution policies
Sequenced policy
Parallel policy
Parallel unsequenced policy
Parallel modifications of algorithm
std::accumulate and std::reduce
std::transform_reduce
std::for_each
Parallelizing an index-based for-loop
Combining std::for_each with linear range
Simplifying construction via a wrapper
Executing STL algorithms on the GPU
GPU APIs and parallel operations
Programmable GPUs
Shader programs
STL algorithms and the GPU
Boost Compute
Basic concepts of Boost Compute
OpenCL
Initializing Boost Compute
Transfer a simple transform-reduce algorithm to Boost Compute
The algorithm in standard C++
Transforming the algorithm to Boost Compute
Adapting the circle struct for use with Boost Compute
Converting circle_area_cpu to Boost Compute
The BOOST_COMPUTE_FUNCTION macro
Implementing the transform-reduction algorithm on the GPU
Using predicates with Boost Compute
Using a custom kernel in Boost Compute
Box filter
Implementing the kernel
Parallelizing for two dimensions
Verify GPU computation on the CPU
Summary
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What this book covers
Chapter 1  ,  A  Brief  Introduction  to  C++,  introduces  some  important  properties  of  C++  such  as  zero-cost  abstractions,  value  semantics,  const
correctness, explicit ownership, and error handling. It also discusses the drawbacks of C++.
Chapter 2  ,  Modern  C++  Concepts  ,  outlines  automatic  type  deduction  using auto  ,  lambda  functions,  move  semantics, std::optional  ,  and
std::any .
Chapter 3 , Measuring Performance , discusses asymptotic complexity and big O notation, practical performance testing, and how to profile your
code to find hotspots.
Chapter 4 , Data Structures ,  takes you through the importance of structuring the data so that it can be accessed quickly. STL containers such as
std::vector , std::list , std::unordered_map , and std::priority_queue are introduced. Finally, we describe how to iterate over parallel arrays.
Chapter 5 , A Deeper Look  at Iterators , dives  into the  concept of iterators, and  shows how  iterators can go  beyond just referring  to objects in
containers.
Chapter 6 , STL Algorithms and Beyond , shows the obvious, and the not so obvious, advantages of STL algorithms over hand rolled for loops. It
also takes a look at the limitations of STL algorithms and how the new Ranges library overcomes these limits.
Chapter  7  ,  Memory  Management  ,  focuses  on  safe  and  efficient  memory  management.  This  includes  memory  ownership,  RAII,  smart
pointers, stack memory, dynamic memory, and custom memory allocators.
Chapter  8  ,  Metaprogramming  and  Compile-Time  Evaluation   ,  explains  metaprogramming  concepts  such  as constexpr  ,  heterogeneous
containers, type_traits  , std::enable_if  ,  and std::is_detected  .  It  also  gives  practical  examples  of  metaprogramming  use  cases,  such  as
reflection.
Chapter 9 , Proxy Objects and Lazy Evaluation , explores how proxy objects can be used to perform under-the-hood optimizations while preserving
clean syntax. Additionally, some creative uses of operator-overloading are demonstrated.
Chapter 10  ,  Concurrency  ,  covers  the  fundamentals  of  concurrent  programming,  including  parallel  execution,  shared  memory,  data  races,  and
deadlocks. It also includes a n introduction to the C++ thread support library, the atomic library, and the C++ memory model.
Chapter 11 , Parallel STL , starts by showing the complexity of writing parallel algorithms. It then demonstrates how to utilize STL algorithms in a
parallel context using the parallel extensions for STL and Boost Compute.