Issue #

A variable subject to a reduction operation is not properly handled in a multithreading environment. This introduces a race condition making the result of the code unpredictable.

Relevance #

A reduction operation is a typical computation pattern where multiple computations are merged into a single value using a mathematical operation. When those computations are executed in parallel, a race condition exists. To prevent this, the variable where the reduction is performed must be protected.

Actions #

Protect the reduction operation.

Code example #

In the following code, there is a race condition for the sum variable since its read/write operations are not properly protected.

void foo() {
 int array[10] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
 int sum = 0;

 #pragma omp parallel for default(none) shared(array, sum)
 for (int i = 0; i < 10; i++) {
   sum += array[i];
 }
}

Shared data scoping should not be used in this case without protecting the reduction operation. To do so, add an atomic directive, which ensures that only one thread performs the read/write operation at the same time.

void foo() {
  int array[10] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
  int sum = 0;

  #pragma omp parallel for default(none) shared(array, sum)
  for (int i = 0; i < 10; i++) {
    #pragma omp atomic update
    sum += array[i];
  }
}

Alternatively, the scalar reduction OpenMP clause can be used. This will automatically privatize the specified variable so that each thread performs the computation safely. Once all threads have finished, their results are reduced into the variable using the specified reduction operator.

void foo() {
  int array[10] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
  int sum = 0;

  #pragma omp parallel for default(none) shared(array) reduction(+: sum)
  for (int i = 0; i < 10; i++) {
    sum += array[i];
  }
}

Related resources #

• PWD002 examples at GitHub
• OpenMP 4.5 Complete Specifications, November 2015 [last checked May 2019]

References #

• Race condition