Reduce Multithreading Caveats

Using std::reduce() in multi-threaded applications offers performance benefits, but there are several caveats to be aware of to ensure correct and efficient execution.

For std::reduce() to work correctly in a multi-threaded context, the operation used must be both commutative and associative. This means:

• Commutative: The order of the operands does not change the result. For example, addition is commutative because $a+b=b+a$.
• Associative: The grouping of operands does not change the result. For example, addition is associative because $(a+b)+c=a+(b+c)$.

If the operation is not commutative or associative, std::reduce() may produce different results each time it is run, leading to non-deterministic behavior.

Example of a commutative and associative operation:

1#include <execution>
2#include <iostream>
3#include <numeric>
4#include <vector>
5
6int main() {
7  std::vector<int> numbers{1, 2, 3, 4, 5};
8
9  int result = std::reduce(
10    std::execution::par,
11    numbers.begin(),
12    numbers.end(),
13    0,
14    std::plus<>{}
15  );
16
17  std::cout << "Result: " << result;
18}

1Result: 15

When using std::reduce() in a multi-threaded context, ensure that any shared resources are handled safely.

Access to shared resources should be synchronized to avoid race conditions and undefined behavior.

Parallel execution can introduce memory overhead due to the creation of multiple threads and the need to manage these threads.

This overhead might negate the performance benefits for smaller datasets or simpler operations.

Handling exceptions in a multi-threaded context can be challenging. If an exception is thrown during the execution of std::reduce(), it must be safely propagated to avoid crashing the application.

One way to handle this is to use a std::promise to communicate exceptions between threads. Here's an example that captures exceptions correctly:

1#include <execution>
2#include <future>
3#include <iostream>
4#include <numeric>
5#include <stdexcept>
6#include <vector>
7
8int safeAdd(int a, int b) {
9  if (a == 3)
10    throw std::runtime_error("Error occurred");
11  return a + b;
12}
13
14int main() {
15  std::vector<int> numbers{1, 2, 3, 4, 5};
16  int result = 0;
17  std::promise<void> promise;
18  std::future<void> future = promise.get_future();
19  std::atomic<bool> exceptionCaught(false);
20
21  try {
22    std::for_each(
23      std::execution::par, numbers.begin(),
24      numbers.end(), [&](int n) {
25        try {
26          result = std::reduce( numbers.begin(),
27            numbers.end(), 0, safeAdd);
28        } catch (...) {
29          if (!exceptionCaught.exchange(true)) {
30            promise.set_exception(
31              std::current_exception());
32          }
33        }
34      });
35    if (!exceptionCaught.load()) {
36      promise.set_value();
37    }
38  } catch (...) {
39    // This block is for any exceptions not
40    // caught in the parallel section
41    std::cerr << "Exception caught in main block\n";
42  }
43
44  try {
45    future.get();
46  } catch (const std::exception& e) {
47    std::cerr << "Exception caught: "
48      << e.what() << '\n';
49  }
50
51  std::cout << "Result: " << result;
52}

1Exception caught: Error occurred
2Result: 0

• Ensure the operation is commutative and associative.
• Handle shared resources safely to avoid race conditions.
• Be mindful of memory overhead from parallel execution.
• Design with exception safety in mind.

By considering these caveats, you can leverage std::reduce() effectively in multi-threaded applications while avoiding common pitfalls.