Local Variables in a for loop openmp

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I've just started to program with openmp and I'm trying to parallelize a for loop with a variable that I need out of the loop. Something like this:

float a = 0;
for (int i = 0; i < x; i++)
    int x = algorithm();
    /* Each loop, x have a different value*/
    a = a + x;
cout << a;

I think the variable a has to be a local variable for each thread. After those thread have ended their job, all the local variables a should be added into one final result.

How can I do that?

There are many mechanisms how to achieve your goal, but the most simple is to employ OpenMP parallel reduction:

float a = 0.0f;
#pragma omp parallel for reduction(+:a)
for(int i = 0; i < x; i++) 
  a += algorithm();
cout << a;

18 OpenMP topic: Controlling thread data, Loop variables in an omp for are private; Local variables in the parallel region are private. The last parameter is the return type. In this case, the type is Int64 because that is the type we specified in the For type argument. That variable is named subtotal and is returned by the lambda expression. The return value is used to initialize subtotal on each subsequent iteration of the loop.

Use the #pragma omp parallel for reduction(+:a) clause before the for loop

variable declared within the for loop are local, as well as loop counters variable declared outside the #pragma omp parallel block are shared by default, unless otherwise specified (see shared, private, firstprivate clauses). Care should be taken when updating shared variables as a race condition may occur. In this case, the reduction(+:a) clause indicated that a is a shared variable on which an addition is performed at each loop. Threads will automatically keep track of the total amount to be added and safely increment a at the end of the loop.

Both codes below are equivalent:

float a = 0.0f;
int n=1000;
#pragma omp parallel shared(a) //spawn the threads
float acc=0;        // local accumulator to each thread
#pragma omp for     // iterations will be shared among the threads
for (int i = 0; i < n; i++){
      float x = algorithm(i); //do something
      acc += x;     //local accumulator increment
  } //for
#omp pragma atomic
a+=acc; //atomic global accumulator increment: done on thread at a time
} //end parallel region, back to a single thread
cout << a;

Is equivalent to:

float a = 0.0f;
int n=1000;
#pragma omp parallel for reduction(+:a)
for (int i = 0; i < n; i++){
    int x = algorithm(i);
    a += x;
    } //parallel for
cout << a;

Note that you can't make a for loop with a stop condition i<x where x is a local variable defined within the loop.

Local Variables in a for loop openmp - c++ - android, Use the #pragma omp parallel for reduction(+:a) clause before the for loop variable declared within the for loop are local, as well as loop counters variable  The author states that loop variable of a for loop shared via the for OpenMP directive should be declared as local. The situation seems to be equal to the one described above at fist sight. However, it not true.

You can use the following structure to perform parallel reduction with thread-private containers since your update is scalar associative.

float a = 0;//Global and will be shared.
#pragma omp parallel 
    float y = 0;//Private to each thread
#pragma omp for
    for(int i = 0; i < x; i++)
         y += algorithm();//Better practice is to not use same variable as loop termination variable.
//Still inside parallel
#pragma omp atomic
    a += y;
cout << a;

[PDF] openmp tips, tricks and gotchas, Make all loop temporary variables local to loopbody. • Pass the rest through argument list. • Much easier to test for correctness! • Then parallelise • C/C++​  In C, you can also redefine the variables inside the loop. Sometimes, even if you forget to declare these temporaries as private, the code may still give the correct output. That is because the compiler can sometimes eliminate them from the loop body, since it detects that their values are not otherwise used.

Shared and private variables in a parallel environment, Loop iteration variables are private within their loops. and cannot be privatized */ #pragma omp parallel firstprivate (p) { int b; /* private automatic */ static int s;  OpenMP creates a team of threads and then shares the iterations of the for loop between the threads. Each thread has its own local copy of the reduction variable. The thread modifies only the local copy of this variable.

[PDF] OpenMP Programming, Specifies variables local to each thread. – firstprivate Example. During parallel execution of the for loop, index “i” is a private variable, while “b”, “cptr” and. The loop index variable is automatically private, and not changes to it inside the loop are allowed. 16.2 Loop schedules crumb trail: > omp-loop > Loop schedules

Data-Sharing Attribute Rules, The loop iteration variable(s) in the associated for-loop(s) of a for, parallel for, Local variables declared in called routines in the region and that have the save  private: the variable is private to each thread, which means each thread will have its own local copy. A private variable is not initialized and the value is not maintained for use outside the parallel region. By default, the loop iteration counters in the OpenMP loop constructs are private.

  • Sorry for my english, I'm from Spain and I'm still learning :)
  • As long as they are declared inside the loop, they should be private by default.
  • OMG Thank you so much! And what happen if instead of 'a' being a float is an array? I mean, for example, if each time you do a loop, it modifies one position of the array, depending the position on the algorithm (in this case you can modify lots of time the same position)
  • Yes, it's possible since OpenMP 4.5. There have been many questions about this topic asked, see, e.g. Reducing on array in OpenMP.
  • @EndergirlPG BTW in case of very large arrays, creating a thread-local temporary array for each thread might not be possible. Then, you basically need to update the shared array (e.g., by using atomic updates). However, you should use some really clever (cache-blocked) access to this output array, mainly to prevent false sharing. Otherwise, the performance would be very low.
  • That's exactly what OpenMP reduction is for. BTW x inside the OP's loop is not the termination variable, its a new local variable.
  • Agreed on the x part and modified my answer accordingly. I think for a beginner, breaking this down into smaller parts makes for better understanding of what is going on instead of using all clauses in one go.