5 Common Performance Pitfalls in C++20 (And How to Avoid Them)

Performance is often the reason developers choose C++, but with great power comes great responsibility.

C++20 introduced many modern conveniences — ranges, coroutines, smart pointers, structured bindings — but if misused, they can lead to subtle and painful performance regressions.

In this post, we’ll look at five common C++20 performance pitfalls, why they happen, and what you can do to avoid them — with examples along the way.

Suggested Reads : Co-Routines in C++20, Concept and Requires In C++20, Latches and Barriers In C++20

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⚙️ 1. Copying Instead of Moving

One of the easiest mistakes in modern C++ is accidentally triggering deep copies instead of moves.

❌ The Pitfall

std::vector<std::string> names = getNames();
std::vector<std::string> copy = names; // Copies all elements

Even if getNames() returns a temporary, a careless assignment or a missing std::move() can lead to unnecessary heap allocations and data duplication.

✅ The Fix

Use move semantics intentionally when ownership transfer is clear.

std::vector<std::string> names = getNames();
std::vector<std::string> copy = std::move(names);

Or, if you return large containers from functions, rely on RVO (Return Value Optimization) instead of forcing copies.

std::vector<std::string> getNames() {
    std::vector<std::string> data = { "Alice", "Bob", "Charlie" };
    return data; // RVO will elide copies in modern compilers
}

👉 Pro Tip: Always check compiler optimization reports (-fno-elide-constructors) if you suspect unnecessary copies.

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🧩 2. Overusing Smart Pointers

Smart pointers like std::shared_ptr and std::unique_ptr make memory management safe — but not free.

❌ The Pitfall

std::shared_ptr<User> user = std::make_shared<User>();
auto copy = user; // atomic ref count increment/decrement

Every copy of std::shared_ptr involves atomic reference counting, which is significantly slower than raw pointer operations.

✅ The Fix

Use std::shared_ptr only when ownership is shared.
Otherwise, prefer std::unique_ptr or even raw pointers for non-owning references.

void process(const User* user); // non-owning

Or if you’re passing ownership:

void store(std::unique_ptr<User> user);

👉 Pro Tip: Shared ownership is rare — avoid it unless you really need it.

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🔁 3. Using Ranges and Views Without Understanding Laziness

C++20’s Ranges are elegant, composable, and readable — but they can also be tricky for performance.

❌ The Pitfall

auto numbers = std::views::iota(1, 1'000'000)
             | std::views::filter([](int n){ return n % 2 == 0; })
             | std::views::transform([](int n){ return n * n; });

std::vector<int> result(numbers.begin(), numbers.end());

Each chained view introduces another layer of indirection, and when materialized, these can degrade cache locality and add function call overheads.

✅ The Fix

Use views for streaming pipelines, not as precomputation containers.

If you actually need a container:

std::vector<int> result;
result.reserve(500'000);
for (int i = 1; i <= 1'000'000; ++i)
    if (i % 2 == 0)
        result.push_back(i * i);

👉 Pro Tip: std::ranges are perfect for composing filters — but measure before replacing all loops with them.

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🧵 4. Overhead from Coroutines

C++20 coroutines are elegant for async programming — but they come with hidden state-machine overhead.

❌ The Pitfall

task<int> computeAsync() {
    co_return 42;
}

Even a trivial coroutine allocates a frame (often on the heap) to store state and locals.
When used in high-frequency paths (e.g., game loops or real-time systems), that overhead adds up.

✅ The Fix

• Use coroutines only where async or suspend/resume behavior is required.

• For synchronous logic, prefer normal functions or std::future.

• When possible, use custom allocators or stack-based coroutine frames.

👉 Pro Tip: Coroutines shine in I/O-bound workloads, not CPU-bound loops.

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🧮 5. Missing constexpr Opportunities

C++20 expanded the power of constexpr, allowing compile-time computations for containers, algorithms, and even lambdas.
Yet, many developers forget to use it.

❌ The Pitfall

int factorial(int n) {
    return n <= 1 ? 1 : n * factorial(n - 1);
}

constexpr int val = factorial(10); // Not constexpr - evaluated at runtime

✅ The Fix

Just mark functions constexpr wherever possible:

constexpr int factorial(int n) {
    return n <= 1 ? 1 : n * factorial(n - 1);
}

Now, the compiler evaluates it at compile time, reducing runtime instructions.

👉 Pro Tip: Combine constexpr with consteval for guaranteed compile-time execution where appropriate.

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⚡ Bonus Tip: Measure, Don’t Assume

The biggest performance pitfall is assuming something is fast because it looks elegant.
Even C++20 abstractions can surprise you.

Use:

• std::chrono for microbenchmarking small blocks.

• Compiler Explorer (godbolt.org) to inspect generated assembly.

• Perf / VTune / Instruments for real profiling.

“What you don’t measure, you can’t optimize.”

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🧠 Final Thoughts

C++20 gives developers incredible expressive power — but with that comes complexity.
Features like ranges, coroutines, and smart pointers improve readability, but if misused, they silently tax performance.

The key takeaway?

✅ Know your tools. Measure often. Optimize intentionally.

Mastering these habits separates code that works from code that flies