In modern C++, performance is no longer a luxury—it’s a necessity. With C++20’s introduction of execution policies, the Standard Library has given developers a clean, type-safe way to harness parallelism without delving into low‑level thread management. The std::execution::par policy, in particular, opens up a new paradigm for writing concise, high‑performance code that scales across multicore processors. This article dives deep into how to use par effectively, best practices, pitfalls to avoid, and real‑world examples that illustrate its true power.
1. What is std::execution::par?
std::execution::par is an execution policy that instructs the algorithm to execute its work in parallel, if the implementation chooses to do so. It is part of a family of policies (seq, par, par_unseq) that provide different execution guarantees:
seq – Sequential execution (default).par – Parallel execution (data‑parallel, typically using threads).par_unseq – Parallel and unsequenced execution (vectorized + multithreaded, where order is irrelevant).
When you pass std::execution::par to an algorithm like std::for_each, the compiler or the library implementation can split the range into subranges and schedule them on a thread pool. The result is that each subrange is processed concurrently, dramatically reducing wall‑clock time for compute‑heavy workloads.
2. Basic Usage
#include <algorithm>
#include <execution>
#include <vector>
#include <iostream>
int main() {
std::vector <int> data(100'000'000, 1);
std::for_each(std::execution::par, data.begin(), data.end(),
[](int& x) { x *= 2; });
std::cout << "First element: " << data.front() << '\n';
}
This simple example doubles each element in a huge vector. On a quad‑core machine, you might see near‑four‑fold speedup compared to the sequential version.
2.1. Passing a Custom Policy
You can also provide a custom std::execution::parallel_policy that configures thread count:
std::execution::parallel_policy par_policy{ std::execution::par, 8 };
std::transform(par_policy, data.begin(), data.end(), data.begin(),
[](int x){ return x + 1; });
This explicitly limits the library to eight threads, which can help avoid oversubscription on systems with fewer cores.
3. Thread Safety and Data Races
Parallel algorithms assume the work function is side‑effect free or at least non‑interfering. That means:
- Each invocation must operate on distinct data.
- No shared mutable state unless protected by synchronization primitives.
- The algorithm ensures that no data race can occur.
Example of safe usage:
std::for_each(std::execution::par, data.begin(), data.end(),
[&](int& x) { x += thread_local_offset(); });
Example of unsafe usage:
int global_counter = 0;
std::for_each(std::execution::par, data.begin(), data.end(),
[&](int& x) { ++global_counter; }); // Data race!
To avoid data races, consider using std::atomic, thread‑local storage, or redesign the algorithm to eliminate shared state.
4. Common Pitfalls
| Pitfall |
Description |
Remedy |
| Ignoring exception handling |
Parallel algorithms propagate the first exception they encounter. |
Wrap the algorithm in a try‑catch block; use std::for_each’s overload that accepts an exception handler. |
| Unnecessary copying |
Passing large objects by value to the work function causes expensive copies. |
Use reference wrappers or std::ref. |
| Wrong range type |
Some algorithms require random access iterators for parallel execution. |
Ensure containers support random access (std::vector, std::deque). |
| Not checking performance |
Parallelism adds overhead; for small ranges, par may be slower. |
Benchmark before deployment; use a threshold or policy like std::execution::seq for small ranges. |
5. Real‑World Use Cases
5.1. Image Processing
std::for_each(std::execution::par, pixels.begin(), pixels.end(),
[](Pixel& p){ p = brighten(p); });
Processing each pixel is embarrassingly parallel; par can deliver near‑linear speedup.
5.2. Financial Simulations
Monte Carlo simulations for option pricing:
std::transform(std::execution::par, paths.begin(), paths.end(), results.begin(),
[](const Path& p){ return payoff(p); });
Each path evaluation is independent, making it an ideal candidate for parallel execution.
5.3. Data Analytics
Aggregating large logs:
std::unordered_map<std::string, int> freq;
std::for_each(std::execution::par, logs.begin(), logs.end(),
[&](const LogEntry& e){
std::lock_guard<std::mutex> lg(freq_mutex);
++freq[e.key];
});
While the lambda acquires a lock, the overall algorithm still benefits from parallel dispatch, especially if the lock contention is minimal.
6. Measuring Performance
#include <chrono>
auto start = std::chrono::steady_clock::now();
std::for_each(std::execution::par, data.begin(), data.end(), [](int& x){ x *= 2; });
auto end = std::chrono::steady_clock::now();
std::cout << "Parallel time: " << std::chrono::duration<double>(end - start).count() << "s\n";
Benchmark against the sequential version and note the speedup factor. Remember that real-world performance also depends on cache locality, NUMA effects, and I/O bandwidth.
7. Future Directions
C++23 continues to refine execution policies, adding more granular control over scheduling and thread pooling. The upcoming std::experimental::parallelism library aims to provide user‑defined execution contexts, allowing developers to plug in custom schedulers (e.g., for GPU backends or distributed systems). Keep an eye on these features to stay ahead of the curve.
8. Takeaway
std::execution::par is a powerful tool that lets C++ developers write parallel code that is both concise and maintainable. By understanding the guarantees, pitfalls, and best practices, you can unlock significant performance gains with minimal effort. Happy parallelizing!
