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PWR053: Consider applying vectorization to forall loop

Issue​

The loop containing a forall pattern can be sped up using vectorization.

Actions​

Implement a version of the forall loop using an Application Program Interface (API) that enables vectorization. Codee assists the programmer by providing source code rewriting capabilities using compiler directives provided by OpenMP standard, GNU compiler, LLVM compiler and Intel compiler.

Relevance​

Vectorizing a loop is one of the ways to speed it up. Vectorization is widely used in modern computers, but writing vectorized 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, the compiler does a good job in vectorization, so the biggest challenge is to vectorize loops manually beyond the capabilities of the compiler.

note

Vectorizing forall loops typically incurs less overhead than vectorizing scalar reduction loops. The main reason is that no synchronization is 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​

void example(double *D, double *X, double *Y, int n, double a) {
  for (int i = 0; i < n; ++i) {
    D[i] = a * X[i] + Y[i];
  }
}

The loop body has a forall pattern, meaning that each iteration of the loop can be executed independently and the result in each iteration is written to an independent memory location. Thus, no race conditions can appear at runtime related to array D, so no specific synchronization is needed.

The code snippet below shows an implementation that uses the OpenMP compiler directives to vectorize the loop explicitly. Note how no synchronization is required to avoid race conditions:

void example(double *D, double *X, double *Y, int n, double a) {
  #pragma omp simd
  for (int i = 0; i < n; ++i) {
    D[i] = a * X[i] + Y[i];
  }
}

Fortran​

subroutine example(D, X, Y, a)
  implicit none
  real(kind=8), intent(out) :: D(:)
  real(kind=8), intent(in) :: X(:), Y(:)
  real(kind=8), intent(in) :: a
  integer :: i

  do i = 1, size(D, 1)
    D(i) = a * X(i) + Y(i)
  end do
end subroutine example

The loop body has a forall pattern, meaning that each iteration of the loop can be executed independently and the result in each iteration is written to an independent memory location. Thus, no race conditions can appear at runtime related to array D, so no specific synchronization is needed.

The code snippet below shows an implementation that uses the OpenMP compiler directives to vectorize the loop explicitly. Note how no synchronization is required to avoid race conditions:

subroutine example(D, X, Y, a)
  implicit none
  real(kind=8), intent(out) :: D(:)
  real(kind=8), intent(in) :: X(:), Y(:)
  real(kind=8), intent(in) :: a
  integer :: i

  !$omp simd
  do i = 1, size(D, 1)
    D(i) = a * X(i) + Y(i)
  end do
end subroutine example

References​