Issue #
The loop containing the scalar reduction pattern can be made faster by offloading it to an accelerator.
Actions #
Implement a version of the scalar reduction loop using an Application Program Interface (API) that enables offloading to accelerators. Codee assists the programmer by providing source code rewriting capabilities using OpenMP and OpenACC compiler directives.
Relevance #
Offloading a loop to an accelerator is one of the ways to speed it up. Accelerators offer a huge computational power, but writing code for accelerators is not straightforward. Essentially, the programmer must explicitly manage the data transfers between the host and the accelerator, specify how to execute the loop in parallel on the accelerator, as well as add the appropriate synchronization to avoid race conditions at runtime.
Typically, minimizing the computational overhead of offloading is the biggest challenge to speedup the code using accelerators.
Note
Offloading scalar reduction loops 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 examples #
Have a look at the following code snippet:
#include <stdio.h>
double example(double *A, int n) {
double sum = 0;
for (int i = 0; i < n; ++i) {
sum += A[i];
}
printf("%f", sum);
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, two different iterations can potentially update the value of the scalar sum, which creates a potential race condition that must be handled through appropriate synchronization.
The code snippet below shows an implementation that uses the OpenACC compiler directives to offload the loop to an accelerator. Note the synchronization added to avoid race conditions and the data transfer clauses that manage the data movement between the host memory and the accelerator memory.
#pragma acc data copyin(A[0:n], n) copy(sum)
#pragma acc parallel
#pragma acc loop reduction(+: sum)
for (int i = 0; i < n; ++i) {
sum += A[i];
}
