C++ Performance Optimization: Avoiding Common Pitfalls and Best Practices Guide
Tags: cpp, mobile, development, ai
Published: 2024-03-18 15:01:09

## 1. Preface
In modern C++ programming practices, performance optimization is a crucial area. As the C++ language continues to evolve, it provides developers with an increasing array of tools and features to better control program performance. However, these powerful features also bring additional complexity, making performance optimization a task that requires careful consideration. While pursuing code efficiency, developers must be vigilant against traps that may lead to performance degradation.

This article will delve into some common issues that C++ engineers may encounter while performing performance optimizations and provide corresponding solutions. We will start with the problem of abusing `std::shared_ptr` and discuss how to use smart pointers correctly to reduce performance overhead. Next, we will discuss the performance costs of type-erasure tools `std::function` and `std::any`, and how to use them only when necessary. The article will also cover improper use of `std::optional` and `std::string_view`, pitfalls of `std::async`, and misuse of `std::move`, among other issues.

Additionally, we will discuss the impact of hidden copies and destruction on performance, the performance overhead of virtual functions, unnecessary copying caused by structured bindings, and the importance of tail recursion optimization. New features introduced in C++20, such as concepts and constraints, coroutines, `std::span`, and modules, will also be examined to ensure they are used appropriately and do not become a burden on performance.

Through this article, we hope to help C++ developers better understand and address the challenges in performance optimization, thereby writing code that is both efficient and robust.

## 2. Misusing std::shared_ptr

**Problem Description**:
`std::shared_ptr` provides a convenient reference counting mechanism, but its construction, copying, and destruction all involve atomic operations, which can be costly in terms of performance.

**Solution**:
- Prefer `std::unique_ptr` whenever possible, and use `std::shared_ptr` only when shared ownership is truly needed.
- Use `std::make_shared` to construct `std::shared_ptr` instances to reduce the number of memory allocations.

**Code Example**:
```cpp
// Using std::unique_ptr
std::unique_ptr<MyClass> ptr1 = std::make_unique<MyClass>();

// Using std::shared_ptr
std::shared_ptr<MyClass> ptr2 = std::make_shared<MyClass>();
```

## 3. Type Erasure: std::function and std::any

**Problem Description**:
`std::function` and `std::any` provide flexible type-erasure capabilities, but they also come with performance costs.

**Solution**:
- Use `std::function` and `std::any` only when you need to store functions or objects of uncertain types.
- Consider using templates and static polymorphism as an alternative to `std::function`.

**Code Example**:
```cpp
// Using std::function
std::function<void(int)> func = [](int value) { /* ... */ };

// Using templates and static polymorphism
template <typename Callable>
void callFunction(Callable&& func, int value) {
    func(value);
}
```

## 4. Improper Use of std::optional

**Problem Description**:
`std::optional` can lead to performance issues, especially when the contained type has significant construction, copying, or moving costs.

**Solution**:
- Use `std::optional` only when you need to represent an optional value.
- Consider returning an empty container or a special value to represent a "no value" situation.

**Code Example**:
```cpp
// Using std::optional
std::optional<std::vector<int>> getOptionalVector(bool condition) {
    if (condition) {
        return std::vector<int>{1, 2, 3};
    }
    return std::nullopt;
}
```

## 5. std::string_view Lifetime Issues

**Problem Description**:
`std::string_view` does not own the string it refers to; if the original string is released or modified, `std::string_view` may refer to invalid memory.

**Solution**:
- Ensure the lifetime of `std::string_view` does not exceed that of the string it references.
- Be particularly mindful of ownership and lifetime issues when using `std::string_view`.

**Code Example**:
```cpp
std::string_view getStringView() {
    std::string str = "Hello, World!";
    return std::string_view(str); // Dangerous: str is destroyed after the function returns
}
```

## 6. Pitfalls of std::async

**Problem Description**:
`std::async` may lead to unexpected synchronous calls; if the returned `std::future` object is not saved, its destructor will wait for the asynchronous operation to complete, causing the code to execute synchronously.

**Solution**:
- Save the `std::future` object returned by `std::async` to ensure asynchronous execution.

**Code Example**:
```cpp
// Correct use of std::async
auto future1 = std::async(std::launch::async, []() { /* ... */ });
auto future2 = std::async(std::launch::async, []() { /* ... */ });
```

## 7. Misusing std::move

**Problem Description**:
In some cases, misusing `std::move` is not only unhelpful but may also lead to performance degradation. Particularly in scenarios where (Named Return Value Optimization) NRVO might be triggered, using `std::move` can result in additional copy or move operations.

**Solution**:
- Use `std::move` only when you need to transfer ownership.
- Avoid using `std::move` when returning local objects.

**Code Example**:
```cpp
// Avoid misusing std::move
MyClass createObject() {
    MyClass obj;
    // ... modify obj ...
    return obj; // Do not use std::move
}
```

## 8. Hidden Copies

**Problem Description**:
Object copying in C++ can lead to performance issues, especially in the following scenarios:
- Constructors not using initializer lists or not using `std::move`.
- Range-based for loops not using references.
- Lambda expressions capturing by value without using `std::move` or capturing by reference.
- Implicit type conversions leading to unnecessary copies.

**Solution**:
- Use initializer lists and `std::move` to avoid unnecessary copies.
- Use references in range-based for loops.
- Use reference capture or `std::move` in lambda expressions.

**Code Example**:
```cpp
// Using initializer lists and std::move
class MyClass {
    std::vector<int> data;
public:
    MyClass(std::vector<int>&& d) : data(std::move(d)) {}
};

// Avoiding copies by using references
std::vector<std::string> vec;
for (const std::string& s : vec) {
    // ...
}

// Using reference capture or std::move
std::string str = "example";
auto lambda = [&str]() { /* ... */ };
auto lambda_move = [s = std::move(str)]() { /* ... */ };
```

## 9. Hidden Destruction

**Problem Description**:
Destruction of complex types can be very time-consuming. If an object's destructor takes a long time to execute, it can inadvertently add to the function's execution time.

**Solution**:
- Avoid creating and destroying complex objects on the hot path.
- Use object pools to manage the lifecycle of complex objects.

**Code Example**:
```cpp
// Assuming ComplexType is a type with significant destruction overhead
class ComplexType {
    // ...
public:
    ~ComplexType() { /* ... */ }
};

// Using object pools
class ComplexTypePool {
    std::list<ComplexType> pool;
public:
    ComplexType& acquire() {
        if (pool.empty()) {
            pool.emplace_back();
        }
        auto& obj = pool.front();
        pool.pop_front();
        return obj;
    }
    void release(ComplexType& obj) {
        pool.push_back(std::move(obj));
    }
};
```

## 10. Virtual Functions

**Problem Description**:
Virtual functions provide the ability for runtime polymorphism, but they come with additional performance overhead:
- Extra addressing operations: the specific function address needs to be found through the virtual function table.
- Disrupting the CPU pipeline: virtual function calls are indirect calls, which require branch prediction.
- Hindering compiler inlining: in most cases, virtual functions cannot be inlined.

**Solution**:
- When polymorphism is not a necessity, consider using non-virtual member functions.
- Use templates and static polymorphism (such as CRTP) to replace runtime polymorphism.

**Code Example**:
```cpp
// Traditional polymorphism using virtual functions
class Base {
public:
    virtual void doWork() { /* ... */ }
    virtual ~Base() {}
};

class Derived : public Base {
public:
    void doWork() override { /* ... */ }
};

// Static polymorphism using CRTP
template <typename Derived>
class BaseCRTP {
public:
    void doWork() {
        static_cast<Derived*>(this)->doWorkImpl();
    }
};

class DerivedCRTP : public BaseCRTP<DerivedCRTP> {
public:
    void doWorkImpl() { /* ... */ }
};
```

## 11. Unnecessary Copies with Structured Bindings

**Problem Description**:
C++17 introduced structured bindings, which allow you to conveniently unpack tuples or structures. However, if not careful, structured bindings can lead to unnecessary object copying.

**Solution**:
- Use references to avoid copying, for example, `auto& [x, y] = my_pair;`.

**Code Example**:
```cpp
std::pair<int, std::vector<int>> getPair() {
    return {1, {1, 2, 3}};
}

// May cause copying
auto [id, vec] = getPair();

// Using references to avoid copying
auto& [id_ref, vec_ref] = getPair();
```

## 12. Tail Recursion Optimization

**Problem Description**:
Tail recursion optimization can reduce the stack space usage of recursive functions, but hidden operations in C++ (such as destruction) may hinder this optimization.

**Solution**:
- Use trivially destructible objects, such as `std::string_view`, to help the compiler implement tail recursion optimization.

**Code Example**:
```cpp
// Implementing tail recursion optimization with std::string_view
unsigned btd_tail(std::string_view input, int v) {
    if (input.empty()) {
        return v;
    } else {
        v = v * 2 + (input.front() - '0');
        return btd_tail(input.substr(1), v);
    }
}
```

## 13. Misuse of Concepts and Constraints

**Problem Description**:
C++20 introduced concepts and constraints, which provide a more powerful way to specify template parameter requirements. However, overly complex concepts and constraints can increase compilation time and make error messages difficult to understand.

**Solution**:
- Use concepts and constraints only when you need to clearly express interface requirements.
- Avoid creating overly complex concepts; keep them simple and clear.

**Code Example**:
```cpp
template<typename T>
concept Addable = requires(T a, T b) {
    { a + b } -> std::convertible_to<T>;
};

// Using concepts
template<Addable T>
T add(T a, T b) {
    return a + b;
}
```

## 14. Improper Use of Coroutines

**Problem Description**:
C++20 introduced coroutines, which are a powerful tool for asynchronous programming. However, improper use of coroutines can lead to performance issues, such as excessive coroutine switching causing performance degradation.

**Solution**:
- Use coroutines in I/O-intensive or asynchronous operation scenarios.
- Avoid frequent starting and suspending of coroutines in performance-critical code paths.

**Code Example**:
```cpp
std::future<int> asyncComputation() {
    co_return 42; // Simplifying asynchronous operations with coroutines
}
```

## 15. Improper Use of std::span

**Problem Description**:
`std::span` is a lightweight container view introduced in C++20, providing access to a contiguous region of an array or container. However, if the original data is released or modified, `std::span` may refer to invalid memory.

**Solution**:
- Ensure the lifetime of `std::span` does not exceed that of the data it references.
- Be particularly mindful of ownership and lifetime issues when using `std::span`.

**Code Example**:
```cpp
std::span<int> getSpan(std::vector<int>& vec) {
    return std::span<int>(vec); // Ensure the vec's lifetime is long enough
}
```

## 16. Improper Use of Modules

**Problem Description**:
C++20 introduced modules, intended to replace the traditional header and source file model, improving compilation efficiency. However, improper module partitioning can lead to increased compilation time, especially when there are complex dependencies between modules.

**Solution**:
- Reasonably partition modules to avoid over-segmentation.
- Manage dependencies between modules well to reduce unnecessary imports.

**Code Example**:
```cpp
// my_module.cppm
export module my_module;

export int add(int a, int b) {
    return a + b;
}

// Using modules
import my_module;

int result = add(1, 2);
```

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