Reduce vs Accumulate Performance

std::reduce() and std::accumulate() are both algorithms in the C++ Standard Library used to combine elements in a range, but they have different performance characteristics due to their design.

std::reduce() is designed for parallel execution, which can lead to significant performance improvements on multi-core processors.

By default, it is executed in a way that allows it to combine elements in any order, making it suitable for multi-threaded environments.

This can result in non-deterministic behavior if the operation used is not associative or commutative. The potential for parallel execution makes std::reduce() generally faster for large datasets.

1#include <iostream>
2#include <numeric>
3#include <vector>
4
5int main() {
6  std::vector<int> numbers{1, 2, 3, 4, 5};
7
8  int result = std::reduce(
9    numbers.begin(), numbers.end(), 0);
10  std::cout << "Result: " << result;
11}

1Result: 15

std::accumulate(), on the other hand, processes elements sequentially from left to right.

This ensures deterministic results regardless of the operation, but it cannot take advantage of multiple cores in the same way std::reduce() can.

Consequently, std::accumulate() might be slower for large datasets because it runs on a single thread.

1#include <iostream>
2#include <numeric>
3#include <vector>
4
5int main() {
6  std::vector<int> numbers{1, 2, 3, 4, 5};
7
8  int result = std::accumulate(
9    numbers.begin(), numbers.end(), 0);
10  std::cout << "Result: " << result;
11}

1Result: 15

In summary:

• std::reduce() can be faster on large datasets due to potential parallel execution.
• std::accumulate() is always sequential and thus may be slower but guarantees deterministic results.

Choose std::reduce() for performance on large datasets if you don't need deterministic order, and std::accumulate() for consistent, predictable results.