A loop containing the sparse reduction pattern can be sped up using multithreading.
Implement a version of the sparse 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.
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 sparse 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.
Have a look at the following code snippet:
void example(double *A, int *nodes, int n) {
for (int nel = 0; nel < n; ++nel) {
A[nodes[nel]] += nel * 1;
}
}The loop body has a sparse reduction pattern, meaning that each iteration of
the loop reduces its computational result to a value, but the place where the
value is stored is known at runtime only. Thus, two different iterations can
potentially update the same element of the array A, which 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.
void example(double *A, int *nodes, int n) {
#pragma omp parallel default(none) shared(A, n, nodes)
{
#pragma omp for schedule(auto)
for (int nel = 0; nel < n; ++nel) {
#pragma omp atomic update
A[nodes[nel]] += nel * 1;
}
} // end parallel
}Note
Executing sparse reduction loops using multithreading incurs a synchronization overhead. The example above shows an implementation that uses atomic protection. Other implementations reduce this high overhead taking advantage of privatization, which increases the memory requirements of the code. An efficient implementation that balances synchronization and memory overheads must be explored for each particular code.