What is your platform? You can look at OpenMP, though it's not a part of C++. But it is widely supported by compilers.

As for range-based for loops, see, e.g., Using OpenMP with C++11 range-based for loops?.

I've also seen few documents at that indicate some efforts to incorporate parallel constructs/algorithms into future C++, but don't know what's their current status.

UPDATE

Just adding some exemplary code:

template <typename RAIter>
void loop_in_parallel(RAIter first, RAIter last) {
   const size_t n = std::distance(first, last);

   #pragma omp parallel for
   for (size_t i = 0; i < n; i++) {
       auto& elem = *(first + i);
       // do whatever you want with elem
    }
}

The number of threads can be set at runtime via the OMP_NUM_THREADS environment variable.

With C++11 you can parallelize a for loop with only a few lines of codes.

My function parallel_for() (define later in the post) splits a for loop into smaller chunks (sub loops), and each chunk assigned to a thread. Here is the usage:

/// Say you want to parallelize this:
for(int i = 0; i < nb_elements; ++i)
    computation(i);    

/// Then you would do:
parallel_for(nb_elements, [&](int start, int end){ 
    for(int i = start; i < end; ++i)
        computation(i); 
});

My parallel_for() also works within a class:

struct My_obj {

    /// Replacing:
    void sequential_for(){
        for(int i = 0; i < nb_elements; ++i)
            computation(i);
    }

    /// By:
    void process_chunk(int start, int end)
    {
        for(int i = start; i < end; ++i)
            computation(i);
    }

    void threaded_for(){
        parallel_for(nb_elements, [this](int s, int e){ 
            this->process_chunk(s, e); 
        } );
    }

    
};

Finally here is the implementation of parallel_for(), just paste in a header file and use it at will:

#include <algorithm>
#include <thread>
#include <functional>
#include <vector>

/// @param[in] nb_elements : size of your for loop
/// @param[in] functor(start, end) :
/// your function processing a sub chunk of the for loop.
/// "start" is the first index to process (included) until the index "end"
/// (excluded)
/// @code
///     for(int i = start; i < end; ++i)
///         computation(i);
/// @endcode
/// @param use_threads : enable / disable threads.
///
///
static
void parallel_for(unsigned nb_elements,
                  std::function<void (int start, int end)> functor,
                  bool use_threads = true)
{
    // -------
    unsigned nb_threads_hint = std::thread::hardware_concurrency();
    unsigned nb_threads = nb_threads_hint == 0 ? 8 : (nb_threads_hint);

    unsigned batch_size = nb_elements / nb_threads;
    unsigned batch_remainder = nb_elements % nb_threads;

    std::vector< std::thread > my_threads(nb_threads);

    if( use_threads )
    {
        // Multithread execution
        for(unsigned i = 0; i < nb_threads; ++i)
        {
            int start = i * batch_size;
            my_threads[i] = std::thread(functor, start, start+batch_size);
        }
    }
    else
    {
        // Single thread execution (for easy debugging)
        for(unsigned i = 0; i < nb_threads; ++i){
            int start = i * batch_size;
            functor( start, start+batch_size );
        }
    }

    // Deform the elements left
    int start = nb_threads * batch_size;
    functor( start, start+batch_remainder);

    // Wait for the other thread to finish their task
    if( use_threads )
        std::for_each(my_threads.begin(), my_threads.end(), std::mem_fn(&std::thread::join));
}

Lastly you can define macros to get even more compact expression:

#define PARALLEL_FOR_BEGIN(nb_elements) parallel_for(nb_elements, [&](int start, int end){ for(int i = start; i < end; ++i)
#define PARALLEL_FOR_END()})

Now converting a sequential for:

for(int i = 0; i < nb_elements; ++i)
    computation(i);

Is only a matter of doing:

PARALLEL_FOR_BEGIN(nb_edges)
{
    computation(i);
}PARALLEL_FOR_END();

std::async may be a good fit here, if you are happy to let the C++ runtime control the parallelism.

Example from the cppreference.com:

#include <iostream>
#include <vector>
#include <algorithm>
#include <numeric>
#include <future>

template <typename RAIter>
int parallel_sum(RAIter beg, RAIter end)
{
    auto len = end - beg;
    if(len < 1000)
        return std::accumulate(beg, end, 0);

    RAIter mid = beg + len/2;
    auto handle = std::async(std::launch::async,
                              parallel_sum<RAIter>, mid, end);
    int sum = parallel_sum(beg, mid);
    return sum + handle.get();
}

int main()
{
    std::vector<int> v(10000, 1);
    std::cout << "The sum is " << parallel_sum(v.begin(), v.end()) << '\n';
}

This can be done using threads specifically pthreads library function that can be used to perform operations concurrently.

You can read more about them here :

std::thread can also be used :

Below is a code in which i use the thread id of each thread to split the array into two halves :

#include <iostream>
#include <cstdlib>
#include <pthread.h>

using namespace std;

#define NUM_THREADS 2

int arr[10] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};

void *splitLoop(void *threadid)
{
   long tid;
   tid = (long)threadid;
   //cout << "Hello World! Thread ID, " << tid << endl;
   int start = (tid * 5);
   int end = start + 5;
   for(int i = start;i < end;i++){
      cout << arr[i] << " ";
   }
   cout << endl;
   pthread_exit(NULL);
}

int main ()
{
   pthread_t threads[NUM_THREADS];
   int rc;
   int i;
   for( i=0; i < NUM_THREADS; i++ ){
      cout << "main() : creating thread, " << i << endl;
      rc = pthread_create(&threads[i], NULL, 
                          splitLoop, (void *)i);
      if (rc){
         cout << "Error:unable to create thread," << rc << endl;
         exit(-1);
      }
   }
   pthread_exit(NULL);
}

Also remember while compiling you have to use the -lpthread flag.

Link to solution on Ideone :

As this thread has been my answer almost every time I've looked for a method to parallelize something, I've decided to add a bit to it, based on the method by arkan (see his answer).

The two following methods are almost the same and allow a simple syntax. Simply include the header file in your project and call one of the parallel version:

Example:

#include "par_for.h"

int main() {
//replace - 
for(unsigned i = 0; i < 10; ++i){
    std::cout << i << std::endl;
}

//with -
//method 1:
pl::thread_par_for(0, 10, [&](unsigned i){
            std::cout << i << std::endl;   //do something here with the index i
        });   //changing the end to },false); will make the loop sequential

//or method 2:
pl::async_par_for(0, 10, [&](unsigned i){
            std::cout << i << std::endl;   //do something here with the index i
        });   //changing the end to },false); will make the loop sequential

return 0;
}

header file - par_for.h:

#include <thread>
#include <vector>
#include <functional>
#include <future>

namespace pl{

    void thread_par_for(unsigned start, unsigned end, std::function<void(unsigned i)> fn, bool par = true){

        //internal loop
        auto int_fn = [&fn](unsigned int_start, unsigned seg_size){
            for (unsigned j = int_start; j < int_start+seg_size; j++){
                fn(j);
            }
        };

        //sequenced for
        if(!par){
            return int_fn(start, end);
        }

        //get number of threads
        unsigned nb_threads_hint = std::thread::hardware_concurrency();
        unsigned nb_threads = nb_threads_hint == 0 ? 8 : (nb_threads_hint);

        //calculate segments
        unsigned total_length = end - start;
        unsigned seg = total_length/nb_threads;
        unsigned last_seg = seg + total_length%nb_threads;

        //launch threads - parallel for
        auto threads_vec = std::vector<std::thread>();
        threads_vec.reserve(nb_threads);
        for(int k = 0; k < nb_threads-1; ++k){
            unsigned current_start = seg*k;
            threads_vec.emplace_back(std::thread(int_fn, current_start, seg));
        }
        {
            unsigned current_start = seg*(nb_threads-1);
            threads_vec.emplace_back(std::thread(int_fn, current_start, last_seg));
        }
        for (auto& th : threads_vec){
            th.join();
        }
    }




    void async_par_for(unsigned start, unsigned end, std::function<void(unsigned i)> fn, bool par = true){

        //internal loop
        auto int_fn = [&fn](unsigned int_start, unsigned seg_size){
            for (unsigned j = int_start; j < int_start+seg_size; j++){
                fn(j);
            }
        };

        //sequenced for
        if(!par){
            return int_fn(start, end);
        }

        //get number of threads
        unsigned nb_threads_hint = std::thread::hardware_concurrency();
        unsigned nb_threads = nb_threads_hint == 0 ? 8 : (nb_threads_hint);

        //calculate segments
        unsigned total_length = end - start;
        unsigned seg = total_length/nb_threads;
        unsigned last_seg = seg + total_length%nb_threads;

        //launch threads - parallel for
        auto fut_vec = std::vector<std::future<void>>();
        fut_vec.reserve(nb_threads);
        for(int k = 0; k < nb_threads-1; ++k){
            unsigned current_start = seg*k;
            fut_vec.emplace_back(async(int_fn, current_start, seg));
        }
        {
            unsigned current_start = seg*(nb_threads-1);
            fut_vec.emplace_back(std::async(std::launch::async, int_fn, current_start, last_seg));
        }
        for (auto& th : fut_vec){
            th.get();
        }
    }
}

Some simple tests suggest the method with async is faster, probably because the standard library controls whether to actually launch a new thread or not.

The Concurrency::parallel_for (PPL) is also one of the nice opions to do task parallelism.

Taken from C++ Coding Exercise – Parallel For – Monte Carlo PI Calculation

int main() {
    srand(time(NULL)); // seed
    const int N1 = 1000;
    const int N2 = 100000;
    int n = 0;
    int c = 0;
    Concurrency::critical_section cs;
    // it is better that N2 >> N1 for better performance
    Concurrency::parallel_for(0, N1, [&](int i) {
        int t = monte_carlo_count_pi(N2);
        cs.lock(); // race condition
        n += N2;   // total sampling points
        c += t;    // points fall in the circle
        cs.unlock();
    });
    cout < < "pi ~= " << setprecision(9) << (double)c / n * 4.0 << endl;
    return 0;
}

Starting from C++17 std::for_each has overloads that allow parallel execution. In my case, however, my algorithm required specific number of threads for optimal execution, while std::for_each implementation from VS 2022 uses number of threads based on std::thread::hardware_concurrency.

For those who want to be able to control number of parallel workers this simple implementation should behave similarly to std::for_each without requiring to have C++17:

template <class Iter, class Func>
void parallel_for_each(unsigned threadCount, Iter first, Iter last, Func func)
{
    Iter it = first;
    if (it == last)
        return;
    if (++it == last)
    {
        func(*first);
        return;
    }

    if (threadCount == 0)
        threadCount = std::max(2u, std::thread::hardware_concurrency());

    std::mutex mx;
    std::vector<std::thread> threads;
    threads.reserve(threadCount - 1);

    auto func2 = [&]() {
        for (;;)
        {
            Iter it;
            {
                std::lock_guard<std::mutex> lock(mx);
                it = first;
                if (it == last)
                    break;
                ++first;
            }
            func(*it);
        }
    };
    for (unsigned i = 0; i < threadCount - 1; ++i, ++it)
    {
        if (it == last)
            break;
        threads.emplace_back(std::thread(func2));
    }
    func2();
    for (auto& th : threads)
        th.join();
}

template <class Iter, class Func>
void parallel_for_each(Iter first, Iter last, Func func)
{
    parallel_for_each(std::thread::hardware_concurrency(), first, last, func);
}