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Modern C++23 Design Patterns for High-Performance Code

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Modern C++23 Design Patterns

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Introduction

In high-performance C++, design patterns are not just academic exercises – they’re essential tools. They structure complex systems, improve maintainability, and document intent. But modern C++ (C++20/23) has so many new features that many classic patterns can be reimagined. For example, as Rainer Grimm observes, “thanks to the ranges library in C++20, the Pipes-and-Filters Pattern is directly supported in C++.”. In this article I’ll walk through eight patterns – from pipelines to observers – and show how C++23 features let us write them more succinctly and safely, with real-world examples. I’ll cover the problem each pattern solves, highlight the C++23 features that help, show annotated code, discuss portability and compiler support, and share use-cases from industries like games, finance, and systems. I’ll also note when you might combine these ideas. Let’s dive in.

Range-Based Pipeline (Pipes-and-Filters)

Problem: In data-intensive code we often want to process a sequence through multiple stages – e.g. filtering, transforming, aggregating – without writing low-level loops. In older C++ we’d either write nested loops or use libraries like Boost.Range to chain algorithms. That code was often verbose and error-prone (bounds checks, intermediate containers).

Modern C++ Solution: The <ranges> library (introduced in C++20, with enhancements in C++23) lets us build lazy pipelines by chaining views with the pipe (|) operator. This directly implements the Pipes-and-Filters pattern: each stage is a filter or transformer that takes a range and returns a new range, all composed naturally. For example, the code below generates the first 10 prime numbers after 1000 by pipelining iota, filter, and take views:

#include <ranges>
#include <vector>
// (Assume isPrime(int) is defined)
int main() {
    // 1) Start from 1000
    // 2) Keep odd numbers only
    // 3) Keep primes only
    // 4) Take first 10 of those
    // 5) Collect into std::vector (std::ranges::to is C++23)
    auto primes = std::views::iota(1000)
                | std::views::filter([](int n){ return n % 2 != 0; })
                | std::views::filter(isPrime)
                | std::views::take(10)
                | std::ranges::to<std::vector>();  // (C++23)
    // primes now contains the 10 primes ≥1000
}

Here we use std::views::iota, filter, take, and finally std::ranges::to (a C++23 convenience) to funnel data through a pipeline. Each | stage is lazy: no work is done until we iterate or call to, so no temporary containers are created.

“The ranges-based implementation has several advantages over the loop-based one: the size of the window is easily changed… the original code would have needed bounds checking… the ranges code does that automatically…”. In fact, as shown by Nicolai Greitemann, a range-based “sliding window” function can avoid manual bounds checks and even be returned as a lazy range for further processing. The pipe-and-filter style makes the code more declarative: you read left-to-right what happens to each element, rather than juggling indices.

C++23 Features: - The Ranges library (<ranges>) provides std::views:: adaptors like filter, transform, take, drop, and (new in C++23) std::views::slide for sliding windows. - The pipe operator (|) is a C++20 feature that composes range adaptors. - std::ranges::to<Container> (C++23) converts a range into a container (like std::vector) without writing an explicit loop. - We often use C++23 lambdas (with auto parameters) to define filters inline.

Annotated Example: In the code above, each pipeline stage is marked by a comment:

Caveats: Pipelines require relatively recent compilers: C++20 ranges are widely supported (gcc 10+, Clang 10+, MSVC 19.28+), but some adaptors (like views::slide) and std::ranges::to need C++23 support (gcc 14+, MSVC 19.36+, Clang head). If you can’t use C++23 yet, Boost.Range or Range-v3 provides similar functionality. Also remember: lazy ranges don’t pre-allocate, so if you read the same range twice, work is redone (you’d need to to once, or cache results).

Use Cases: Pipelines shine in data processing tasks. For instance, in finance I’ve used range pipelines to filter and transform streaming market data (e.g. “take all trades over $1000, convert to EUR, then accumulate”). In game dev, one might pipeline entity lists (views::filter, views::transform) for culling and rendering. In systems code, pipelines simplify tasks like log processing or data mining, because they avoid manual index math. Pipelines can also be used with infinite streams (e.g. procedural generation).

Related: This is basically the Pipes-and-Filters pattern. For more, see Rainer Grimm’s blog on pipelines or Range-v3 documentation. C++23’s std::ranges::to came from WG21 proposal [P2300], improving ergonomics for pipelines.

Lazy Generator (Coroutines with co_yield)

Problem: Often you want to define a sequence of values on-the-fly (e.g. numbers, events, file lines) without computing them all upfront or storing them in memory. In older C++ we wrote iterators or used external libraries for generators, which was tedious.

Modern C++ Solution: C++20 introduced coroutines, and C++23 adds std::generator (in <generator>). A coroutine can co_yield values lazily whenever the caller requests them. This implements the Generator pattern (like Python’s generators) natively. You write a function with co_yield, and it automatically becomes an iterable range. For example, here’s an infinite Fibonacci generator:

#include <generator>
#include <ranges>
#include <iostream>

std::generator<int> fib() {
    int a = 0, b = 1;
    while (true) {
        co_yield a;         // (1) yield current value
        int next = a + b;
        a = b;
        b = next;
    }
}

int main() {
    // Print first 10 Fibonacci numbers
    for (int x : fib() | std::views::take(10)) {
        std::cout << x << ' ';  // outputs: 0 1 1 2 3 5 8 13 21 34
    }
}

Here (1) we co_yield a each time through the loop. The function fib() is a coroutine: each co_yield suspends it and produces a value. The std::generator<int> return type means the coroutine is a range of int. We can then use range adaptors (take(10)) and a range-based for-loop to consume it lazily.

“std::generator in C++23 is the first concrete coroutine. A std::generator generates a sequence of elements by repeatedly resuming the coroutine from which it was paused.”. In practice, this means you can write stateful loops naturally: fib() contains its own state (a,b), and calling it yields values on demand. The caller never has to manage that state manually.

C++23 Features: - Coroutines (co_yield) and the new <generator> header. - std::generator<T> is a C++23 standard library type that wraps a coroutine producing T values. - We can still use ranges with it (co_yield+std::generator models std::ranges::input_range). - (Also related: if consteval we’ll mention later can differentiate code paths inside coroutines if needed.)

Annotated Example: In the code above, line (1) shows co_yield a;, which suspends the coroutine and returns a. When the caller’s loop asks for the next value, execution resumes after the co_yield. This loop never terminates on its own (infinite sequence), but we safely take only 10 values.

Caveats: As of 2025, support for std::generator is spotty. Microsoft’s standard library (MSVC 17.13+) implements it, and newer GCC/Clang versions are catching up (see cppreference’s compiler support). If unavailable, you’d use a library (e.g. cppcoro or Boost) or write a custom range type. Also, coroutines can be confusing if you’re not familiar, and debugging can be tricky since the control flow is “split” between caller and coroutine.

Use Cases: Lazy generators are great when dealing with large or infinite sequences. In game dev you might generate procedural content or enemy spawn waves lazily. In finance, maybe process a stream of ticks or market events one by one without buffering them all. In systems, you could lazily read lines from a file or stream events from the OS. Anywhere you want “on-demand” data, a generator simplifies the code (no manual iterator struct). For example, the Microsoft blog shows summing a portion of a Fibonacci stream using ranges, illustrating how coroutines fit into pipelines.

Link: See the WG21 proposal [P2502R2] (via Microsoft) for std::generator and Sy Brand’s C++ Team Blog for details. Compared to writing custom range-iterator classes, std::generator requires far less boilerplate and ties directly into <ranges>.

Fluent Builder (with Designated Init and CTAD)

Problem: Some objects (e.g. configuration objects, complex structs) have many parameters. A constructor with a long parameter list is error-prone and hard to use. The classic Builder pattern (method chaining) helps, but often involved verbose code or separate “builder” classes.

Modern C++ Solution: C++20’s designated initializers and C++17’s Class Template Argument Deduction (CTAD) make builders more concise. For aggregates (plain structs), you can now write:

struct Config {
    std::string host = "localhost";
    int port = 8080;
    bool useTLS = true;
};

// Designated initializer (C++20)
Config cfg { .host="example.com", .port=443 };
// Only override what's needed; order doesn’t matter.

This feels like “named parameters” and often obviates a Builder class entirely. But for non-aggregate classes or fluent interfaces, we can still do a builder with CTAD to infer types. For example:

struct Rectangle {
    int width{0}, height{0};
    std::string color{"white"};
};

// A simple fluent builder using CTAD (C++17) and method chaining
template<typename T>
struct Builder {
    T obj;
    Builder(T init) : obj(init) {}

    auto& setWidth(int w)   { obj.width = w; return *this; }
    auto& setHeight(int h)  { obj.height = h; return *this; }
    auto& setColor(std::string c) { obj.color = std::move(c); return *this; }

    // Allow conversion to T (end of chain)
    operator T() &&        { return obj; }
};

int main() {
    // Deduction guides make this infer Builder<Rectangle>
    auto rect = Builder{Rectangle{}}  // CTAD deduces Builder<Rectangle>
                  .setWidth(100)
                  .setHeight(50)
                  .setColor("red");
    // rect is a Rectangle with the given fields set
}

Here we use CTAD so that writing Builder{Rectangle{}} deduces the T=Rectangle. The methods return *this so calls chain. We then convert the builder to Rectangle via the converting operator. This “fluent builder” feels very natural, and we didn’t have to spell Builder<Rectangle> thanks to CTAD. In contrast, pre-C++17 one often wrote Builder<Rectangle> builder(Rectangle{}); or used a static create().

C++23 Features: - Designated initializers (C++20) let us do Config{ .host="x", .port=123 }, improving readability and avoiding the need for many constructors. - CTAD (C++17) lets Builder{Type{}} infer the template. - We also rely on move semantics (from C++11+) in builder code, but no new C++23 language feature is strictly needed here beyond these.

Annotated Example: In the builder code above, note how CTAD infers the template, and how each setX(...) returns *this so you can chain calls. Compared to older code, you no longer need a separate build() call if you use the converting operator, and you don’t have to specify template args.

Caveats: Designated initializers only work on aggregates (no user constructors). If your type has invariants to enforce, you might still need a custom builder. Also, CTAD only works if the constructor signature is unambiguous. Some older compilers may not fully support designated init (e.g. MSVC < VS2019 16.8 needed updates). But by C++23 all major compilers handle these.

Use Cases: Fluent builders are common for configuration or policy objects. For instance, in finance I might have a ChartOptions builder to set colors, styles, etc. In game engines, builders create complex objects (like scene nodes) with many optional parameters. In systems code, builders or designated init make structs like network Request or graphics Vertex much clearer to initialize. For small objects, even designated initializers can replace builders entirely.

Alternate View: Sometimes, if your class is just an aggregate of options, you can skip builders and rely on Type{ .a=x, .b=y }. Still, method-chaining builders remain useful when constructing an object involves non-trivial logic or when the type has non-public members.

Scoped Cleanup (Scope Guards with <scope>)

Problem: In C++, managing resources (locks, files, memory, etc.) safely is crucial. We want to ensure cleanup code runs on every exit path from a scope (normal exit, return, exceptions). Pre-C++23 idioms included writing custom RAII classes or using hacks (try/finally-like macros, or std::unique_ptr with deleters).

Modern C++ Solution: C++23 introduced the <scope> header (Library Fundamentals TS v3) with std::scope_exit, std::scope_fail, and std::scope_success. These are standardized scope guards. The simplest is std::scope_exit, which executes a lambda when its own object is destroyed at scope exit. For example:

#include <scope>   // C++23 header
#include <iostream>
#include <mutex>

void example() {
    std::mutex m;
    m.lock();
    // Create a guard that unlocks 'm' when we leave the block
    auto unlocker = std::scope_exit([&]{ m.unlock(); });

    std::cout << "Critical section\n";
    if (some_error) {
        return;  // even on early return, m.unlock() will run
    }
    // ...
}  // m.unlock() is automatically called here

In this snippet, unlocker is a local object whose destructor calls the lambda, releasing the mutex. No matter how we leave the scope (normal exit or exception), the guard runs. This is basically a standard finally/defer mechanism.

std::scope_exit is a nice feature to do some cleanup job when control flow will leave a current block. It’s a much easier alternative to creating RAII classes on your own..

C++23 Features: - The <scope> header and std::scope_exit (new in C++23, based on Library Fundamentals TS v3). - Optionally, std::scope_fail (only runs the lambda if leaving by exception) and std::scope_success (only on normal exit). - These are standard now; previously one might use Boost.ScopeGuard or implement a small RAII guard manually.

Annotated Example: In the code above, the comment shows that unlocker will call m.unlock() on destruction. We bind the lambda at creation time. This is often clearer and less error-prone than writing a dedicated RAII class or remembering to unlock in each return branch.

Caveats: Because these are in C++23, you need an up-to-date compiler with the <scope> header. As of 2025, major compilers (GCC 14+, Clang 17+, MSVC 19.x+) implement it. If not available, use std::unique_ptr with a custom deleter, or Boost’s BOOST_SCOPE_EXIT. Also, lambdas in scope guards should be noexcept-safe, since throwing in a destructor leads to std::terminate. Be mindful of what you capture in the lambda (e.g. avoid capturing objects by reference if they might expire first).

Use Cases: In systems programming, std::scope_exit is great for releasing locks or file handles. For example, a database transaction object could use it to commit() or rollback() at scope end. In game dev, one might use it to reset graphics state or restore rendering targets after a block. Anywhere you have “clean up on exit” code, scope guards shine. Nikoli Kutiavin notes it’s perfect “where exceptions or early returns could otherwise leave resources dangling” – like unlocking a mutex or closing a file.

Related: This is essentially the Scope Guard idiom made standard. It combines well with RAII (zero overhead). Contrast: before C++23, one often wrote constructs like:

auto guard = std::unique_ptr<void, std::function<void(void*)>>(nullptr, [&](void*){ m.unlock(); });

or a custom struct. Now it’s one line of standard library.

Compile-Time Policy Selection (concepts + consteval)

Problem: Sometimes we want flexible behavior (policies) but determined at compile-time for efficiency. For example, a numeric algorithm might have different strategies (slow-but-general vs fast-special-case). We want to pick which code path at compile-time based on types or flags. In the past, we’d use template specialization or tag dispatch or even runtime branching. In prehistoric times, we used #if preprocessor conditionals—shudder.

Modern C++ Solution: C++20 concepts allow us to define policy interfaces and constrain templates, and C++23’s if consteval helps branch at compile-time evaluation. Essentially, we can write code like:

// Define a policy concept (interface requirements)
template<typename P>
concept Policy = requires(P p, int x) {
    { p.process(x) } -> std::convertible_to<int>;
};

// A consteval function (must be evaluated at compile-time if reached)
consteval int compileTimeCompute(int x) {
    return x * x;  // must be compile-time
}

template<Policy P>
constexpr int run(const P& policy, int value) {
    if consteval {
        // In a constexpr context, do extra compile-time work
        return compileTimeCompute(policy.process(value));
    } else {
        // Regular runtime path
        return policy.process(value);
    }
}

Here, Policy is a concept (since C++20) that says what methods a policy type must have. The function run() is constexpr; inside it we use C++23’s if consteval (from proposal P0592) to choose a branch if the function call is being evaluated at compile-time. This lets us combine consteval functions with normal code: the compileTimeCompute is only invoked in a constant-evaluation context (e.g. in a constexpr initializer), while the else branch runs at runtime.

Conceptually, this is a “compile-time strategy pattern”: we select which code to run based on template parameters and whether we’re in a constant-expression. The compiler can eliminate branches not taken at compile-time, leading to zero overhead dispatch. And concepts ensure any P we pass meets the requirements.

C++23 Features: - Concepts (C++20) let us name and enforce requirements on policies (e.g. template<Policy P>). - if consteval (C++23) is a new statement that checks at compile-time whether the function is being evaluated as a constant expression. - consteval (C++20) marks a function to be run only at compile-time. - Combined, these let us write functions that do different things in compile-time vs runtime contexts seamlessly.

Annotated Example: In run() above, note:

Caveats: This pattern requires fairly modern compilers. if consteval is new as of C++23, so ensure your toolchain supports it. Also, heavy use of constexpr and consteval can lead to very long compile times if overused. And since we often combine templates, compilation errors on unmet concepts can be cryptic. Finally, consteval functions must be evaluated at compile-time; misuse will cause hard errors.

Use Cases: Compile-time policy selection appears in template libraries and high-performance code. For example, in finance or simulation, we might have a Policy for math operations that is either a generic (slower) or SSE-optimized (faster) path. We could write something like:

struct GenericPolicy { int process(int x) { return x+1; } };
struct FastPolicy    { int process(int x) { /* some fast math */ } };

// At compile time, pick policy and compute a constant:
constexpr int result = run<FastPolicy>(FastPolicy{}, 41);
static_assert(result == 42);

This guarantees at compile time we hit the if consteval branch. In game engines, one might choose different graphics shading policies (e.g. compile-time flags to enable VR or HDR code) and use if consteval in meta-functions to peel off path overhead.

Related: This idea is a form of static strategy/policy pattern. C++20’s concepts are basically named constraints – e.g. from cppreference:

template<class T, class U>
concept Derived = std::is_base_of_v<U,T>;
template<Derived<Base> T> void f(T);

shows how a concept is applied to a template parameter. And the new if consteval is a much cleaner way to do what std::is_constant_evaluated() did before.

Monostate Singleton (with Inline Variables)

Problem: The Singleton pattern ensures one instance of a class, but it has drawbacks (lazy init, thread-safety, global state). An alternative is the Monostate (Borg) pattern: make all member data static, so every instance shares the same state. This gives the illusion of a singleton but with simpler implementation.

Modern C++ Solution: Use inline static members (C++17+) to define a monostate class entirely in a header. For example, consider a simple phone book:

#include <iostream>
#include <string>
#include <unordered_map>

class PhoneBook {
public:
    void addEntry(const std::string& name, int number) {
        teleBook[name] = number;
    }
    void printAll() const {
        for (auto& [n,num] : teleBook)
            std::cout << n << ": " << num << "\n";
    }
private:
    inline static std::unordered_map<std::string,int> teleBook;  // shared by all
};

All instances of PhoneBook use the same teleBook map. Users of PhoneBook don’t even need to know about the static state; they just create objects normally. The key here is inline static; since C++17 we can define and initialize static members in-class without a separate .cpp definition. This makes it easy to have header-only monostates.

In effect, every method of PhoneBook behaves as if it were a singleton’s method, but we didn’t have to write a private constructor or a getInstance().

Rainer Grimm notes that “in the Monostate Pattern, all data members are static. Consequentially, all instances of the class use the same data.”.

C++23 Features: Actually, monostate uses inline variables (C++17) – so C++23 compilers fully support it. The novelty in C++23 is minimal here, but inline static is the key enabler. (In pre-C++17, you would define std::unordered_map<std::string,int> PhoneBook::teleBook; in a .cpp file; now it’s in-class.)

Annotated Example: In the PhoneBook above, teleBook is inline static. You can add entries through any PhoneBook object, and printAll() from a different object will see them too, because it’s the same teleBook. For instance:

PhoneBook p1, p2;
p1.addEntry("Alice", 42);
p2.addEntry("Bob", 99);
p1.printAll();  // Prints both Alice and Bob

Caveats: Monostate effectively creates global state. Initialization order of inline static variables follows the usual rules (static initialization order fiasco can still occur across translation units). Also, like singletons, it introduces hidden coupling (the code reading teleBook depends on it existing). You must ensure thread-safety manually (multiple threads mutating the static data will need locks, etc.). Use monostate sparingly for true “global” services.

Use Cases: Monostate is often used for logging, configuration, or other global services. For example, a Logger class could hold an inline static std::ofstream or a settings map. In game dev, one might have a global Settings monostate for graphics options accessible everywhere. In finance, maybe a global MarketDataCache. Monostate is safer than a typical singleton (no explicit getInstance() call site), but it’s still “global” state under the hood. Remember Dijkstra’s warning: global state is error-prone, so use dependency injection where possible; monostate is just one way to minimize boilerplate if you do need it.

Observer Pattern with Type Erasure

Problem: The Observer pattern involves objects (observers) registering to be notified by a subject when events occur. A common implementation stores callbacks. In old C++ you might define an abstract Observer interface and require derived classes. This is inflexible (only one method signature) and causes coupling.

Modern C++ Solution: Use type erasure with std::function (or similar) to store arbitrary callables as observers. C++23’s improvements to lambdas (deducing this) also help in writing observer callbacks elegantly. For example:

#include <functional>
#include <vector>
#include <iostream>

class Button {
    std::vector<std::function<void(int)>> observers;
public:
    void subscribe(std::function<void(int)> obs) {
        observers.push_back(std::move(obs));
    }
    void click(int x) {
        for (auto& o : observers) o(x);
    }
};

// Some listener class
class Listener {
public:
    void onClick(int x) {
        std::cout << "Got click with value " << x << "\n";
    }
};

int main() {
    Button button;
    Listener lst;

    // Subscribe using a lambda that calls the member function
    button.subscribe([&lst](int val) { lst.onClick(val); });

    button.click(7);  // prints "Got click with value 7"
}

Here Button’s subscribe takes a std::function<void(int)>, which can wrap anything callable with that signature. We pass a lambda capturing &lst; it forwards to lst.onClick(val). This is type-erased: the Button doesn’t care if the observer is a lambda, free function, function object, or bound method.

In C++23 we can even use deducing this in lambdas for interesting patterns. For instance, a self-referential or recursive callback can name its own closure object. As Sy Brand’s blog shows, you could write:

auto recursiveObserver = [&](this auto& self, int val) {
    if (val > 0) {
        std::cout << "Val = " << val << "\n";
        self(val-1);  // call itself (enabled by deducing-this)
    }
};
button.subscribe(recursiveObserver);
button.click(3);
// Output:
// Val = 3
// Val = 2
// Val = 1

Here (this auto& self) (C++23 syntax) gives the lambda a name self for its own closure, enabling it to recurse. This is a neat trick when observers need to re-subscribe or process in steps.

C++23 Features: - std::function (C++11, but widely used) for type-erased callbacks. - Deducing this (C++23, P0847R6) allows lambdas and member functions to treat the object as an explicit parameter. In lambdas, writing (this auto& self) lets you refer to the closure as self. This isn’t needed for basic observer use, but is useful in advanced cases (recursive lambda handlers, perfect-forwarding callbacks, etc.).

Annotated Example: In the above code, subscribe uses std::function<void(int)>. We attach a lambda [&lst](int val){ lst.onClick(val); }. If we wanted a member function more directly, C++23 lets you even do:

button.subscribe([&lst](auto&& self, int val) {
    // Using deducing this: 'self' is the closure object
    std::invoke(&Listener::onClick, lst, val);
});

(though the simple example above without deducing this suffices for most cases).

Caveats: std::function adds some overhead (type-erasure and possible heap allocation) compared to raw function pointers. But it’s usually negligible for observer lists of moderate size. Also, be careful with object lifetimes: if an observer captures a raw this, make sure the object lives as long as the subscription (or use std::weak_ptr). Deducing-this feature is C++23, so if using it ensure compiler support; otherwise stick to normal lambdas.

Use Cases: Observer/type-erasure is everywhere: GUI event handlers (buttons, sliders), game event systems, or event buses in server applications. For example, a game engine might have an EventDispatcher where any game object can subscribe a lambda to respond to “collision” or “input” events. In finance, you might have market data feeds where listener objects subscribe to price updates via callbacks. In systems, OS signals or hardware interrupts could be handled by an observer-like callback system. Type erasure means the subject code doesn’t need to know about observer classes or interfaces – just a callable signature.

Concurrency Barrier (Thread Coordination with <barrier>)

Problem: In multithreaded programs, a common need is to synchronize a group of threads at certain points (barriers). For example, in a simulation we may want all worker threads to finish one phase before any proceed to the next. Before C++20, you had to build such synchronization manually (using atomics, condition variables, or third-party libraries).

Modern C++ Solution: C++20 introduced std::barrier (in <barrier>, reused in C++23) for exactly this purpose. A std::barrier is initialized with the number of threads (or participants). Each thread calls arrive_and_wait() when it reaches the sync point. The barrier blocks them all until the last arrives, then optionally runs a completion function exactly once, and finally releases all threads to continue (and the barrier resets for reuse).

#include <barrier>
#include <thread>
#include <iostream>

void worker(std::barrier<> &bar, int id) {
    std::cout << "Thread " << id << " before barrier\n";
    bar.arrive_and_wait();  // block until all threads reach here
    std::cout << "Thread " << id << " after barrier\n";
}

int main() {
    constexpr int N = 3;
    std::barrier barrier(N);  // barrier for 3 threads

    std::thread t1(worker, std::ref(barrier), 1);
    std::thread t2(worker, std::ref(barrier), 2);
    std::thread t3(worker, std::ref(barrier), 3);

    t1.join(); t2.join(); t3.join();
}

All three threads will print “before barrier”, then the barrier lets them all go, then each prints “after barrier”. We could also pass a lambda to barrier’s constructor to do some action once per phase (like update a shared counter or print a status) before the threads continue.

C++23 Features: - std::barrier (since C++20, in <barrier>) provides a reusable barrier with a phase completion callback. - std::latch is similar but one-shot. (Strictly speaking, barrier is a C++20 feature; it’s available in C++23 as part of the standard concurrency library.) No new C++23-specific changes here beyond library availability.

Caveats: You must know the number of participants upfront. Spawning or dropping threads dynamically requires careful use of arrive_and_drop(). Also, barriers don’t by themselves handle thread termination – make sure all expected threads call arrive. Old compilers (pre-C++20) lack std::barrier; you would then use std::condition_variable or platform-specific barriers.

Use Cases: Barriers are vital in parallel algorithms. In high-performance computing (HPC), they synchronize steps in parallel loops or stages of an algorithm. In game development, a barrier might sync physics threads at the end of each simulation tick before rendering. In video encoding or signal processing, you often have pipeline stages where each thread must finish its part before the next stage starts – a barrier fits perfectly.

Summary: std::barrier gives a clear, deadlock-free way to coordinate threads. As cppreference describes, “barriers are reusable: once a group of arriving threads are unblocked, the barrier can be reused. Unlike std::latch, barriers execute a possibly empty callable before unblocking threads.”. This greatly simplifies multithreaded stage synchronization.

Each of these patterns leverages modern C++. They can also be combined. For example, a multithreaded pipeline might use a range-based sequence processed by worker threads, with a barrier at each pipeline stage. Or a coroutine generator could be an observer of an async event. The key is: when you recognize a classic pattern need, ask “What C++23 features can simplify this?”. Often the answer is: significantly cleaner and more performant code.

Sources: Above explanations draw on expert blogs and the standard library. For more details, see cppreference and WG21 proposals (e.g. P2502 for coroutines, P0847 for deducing-this). And of course, experiment with real code on the latest compilers to see these patterns in action. Happy coding!

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