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→OpenMp vs C++ 11 Threads
* <contition_variable> - a synchronization primitive that can be used to block a thread, or multiple threads at the same time, until another thread both modifies a shared variable (the condition), and notifies the condition_variable.
* <future> - Describes components that a C++ program can use to retrieve in one thread the result (value or exception) from a function that has run in the same thread or another thread.
===Creating and executing Threads===
Inside a declared OpenMp parallel region, if not specified via an environment variable OMP_NUM_THREADS or the library routine omp_get_thread_num() , OpenMp will automatically decide how many threads are needed to execute parallel code.
An issue with this approach is that OpenMp is unaware how many threads a CPU can support. A result of this can be OpenMp creating 4 threads for a single core processor which may result in a degradation of performance.
C++ 11 Threads on the contrary always required to specify the number of threads required for a parallel region. If not specified by user input or hardcoding, the number of threads supported by a CPU can also be accurately via the std::thread::hardware_concurrency(); function.
OpenMp automatically decides what order threads will execute. C++ 11 Threads require the developer to specify in what order threads will execute. This is typically done within a for loop block.
====OpenMp====
Automatic thread creation
#pragma omp parallel
{
int tid = omp_get_thread_num();
std::cout << "Hi from thread "
<< tid << '\n';
}
Programmer Specified thread creation
int numThreads = 4;
omp_set_num_threads(numThreads);
#pragma omp parallel
{
int tid = omp_get_thread_num();
std::cout << "Hi from thread "
<< tid << '\n';
}
====STD Threads====
int numThreads = std::thread::hardware_concurrency();
std::vector<std::thread> threads(numThreads);
for (int ID = 0; ID < numThreads; ID++) {
threads[ID] = std::thread(function);
}
===Programming Models===
