Parallel Algorithms
Modern C++ offers robust tools for writing concurrent and parallel code, enabling efficient use of multicore processors for better performance.
Since C++17, the Standard Library includes parallel algorithms like std::for_each, which can run in parallel using execution policies. This lets you write high-level, thread-aware code without managing threads directly.
main.cpp
1234567891011121314151617181920#include <iostream> #include <vector> #include <algorithm> #include <execution> int main() { std::vector<int> data = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}; // Process each element in parallel: double each value std::for_each(std::execution::par, data.begin(), data.end(), [](int& n) { n *= 2; }); std::cout << "Processed data: "; for (const auto& n : data) std::cout << n << " "; std::cout << std::endl; }
Performance
Parallel algorithms can greatly speed up large data processing by distributing work across multiple CPU cores. Sequential algorithms handle elements one at a time and donβt use multiple cores.
Determinism
Parallel execution can cause non-deterministic operation order, which may affect results if operations depend on each other. Sequential algorithms process elements in order, ensuring deterministic results.
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Modern C++ offers robust tools for writing concurrent and parallel code, enabling efficient use of multicore processors for better performance.
Since C++17, the Standard Library includes parallel algorithms like std::for_each, which can run in parallel using execution policies. This lets you write high-level, thread-aware code without managing threads directly.
main.cpp
1234567891011121314151617181920#include <iostream> #include <vector> #include <algorithm> #include <execution> int main() { std::vector<int> data = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}; // Process each element in parallel: double each value std::for_each(std::execution::par, data.begin(), data.end(), [](int& n) { n *= 2; }); std::cout << "Processed data: "; for (const auto& n : data) std::cout << n << " "; std::cout << std::endl; }
Performance
Parallel algorithms can greatly speed up large data processing by distributing work across multiple CPU cores. Sequential algorithms handle elements one at a time and donβt use multiple cores.
Determinism
Parallel execution can cause non-deterministic operation order, which may affect results if operations depend on each other. Sequential algorithms process elements in order, ensuring deterministic results.
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