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PWR051: Consider applying multithreading parallelism to scalar reduction loop

Issue​

A loop containing the scalar reduction pattern can be sped up using multithreading.

Actions​

Implement a version of the scalar reduction loop using an Application Program Interface (API) that enables multithreading on the CPU. Codee assists the programmer by providing source code rewriting capabilities using OpenMP compiler directives.

Relevance​

Executing a loop using multithreading on the CPU is one of the ways to speed it up. Multicore CPUs are widely used in modern computers, but writing multithreaded code is not straightforward. Essentially, the programmer must explicitly specify how to execute the loop in vector mode on the hardware, as well as add the appropriate synchronization to avoid race conditions at runtime. Typically, minimizing the computational overhead of multithreading is the biggest challenge to speedup the code.

note

Executing scalar reduction loops using multithreading incurs an overhead due to the synchronization needed to avoid race conditions and ensure the correctness of the code. Note appropriate data scoping of shared and private variables is still a must.

Code example​

C​

double example(double *A, int n) {
  double sum = 0.0;

  for (int i = 0; i < n; ++i) {
    sum += A[i];
  }

  return sum;
}

The loop body has a scalar reduction pattern, meaning that each iteration of the loop reduces its computational result to a single value; in this case, sum. Thus, any two iterations of the loop executing concurrently can potentially update the value of the scalar sum at the same time. This creates a potential race condition that must be handled through appropriate synchronization.

The code snippet below shows an implementation that uses the OpenMP compiler directives for multithreading. Note the synchronization added to avoid race conditions:

double example(double *A, int n) {
  double sum = 0.0;

  #pragma omp parallel default(none) shared(A, n, sum) private(i)
  {
    #pragma omp for reduction(+: sum) schedule(auto)
    for (int i = 0; i < n; ++i) {
      sum += A[i];
    }
  } // end parallel

  return sum;
}
note

Executing scalar reduction loops using multithreading incurs a synchronization overhead. The example above shows a code that uses an efficient implementation balancing synchronization and memory overheads, by taking advantage of a reduction mechanism typically supported by the APIs for multithreading.

Fortran​

function example(A) result(sum)
  implicit none
  real(kind=8), intent(in) :: A(:)
  real(kind=8) :: sum
  integer :: i

  sum = 0.0
  do i = 1, size(A, 1)
    sum = sum + A(i)
  end do
end function example

The loop body has a scalar reduction pattern, meaning that each iteration of the loop reduces its computational result to a single value; in this case, sum. Thus, any two iterations of the loop executing concurrently can potentially update the value of the scalar sum at the same time. This creates a potential race condition that must be handled through appropriate synchronization.

The code snippet below shows an implementation that uses the OpenMP compiler directives for multithreading. Note the synchronization added to avoid race conditions:

function example(A) result(sum)
  implicit none
  real(kind=8), intent(in) :: A(:)
  real(kind=8) :: sum
  integer :: i

  sum = 0.0
  !$omp parallel do default(none) shared(A) private(i) reduction(+: sum) &
  !$omp& schedule(auto)
  do i = 1, size(A, 1)
    sum = sum + A(i)
  end do
end function example
note

Executing scalar reduction loops using multithreading incurs a synchronization overhead. The example above shows a code that uses an efficient implementation balancing synchronization and memory overheads, by taking advantage of a reduction mechanism typically supported by the APIs for multithreading.

References​