openmp

OpenMP

Purpose

Guide agents through OpenMP shared-memory parallelism: #pragma omp parallel for with scheduling clauses, reductions, data-sharing attributes, SIMD hints, task parallelism, OpenMP 5.x GPU target offloading, common pitfalls (false sharing, data races), environment tuning, and profiling with Score-P or TAU.

When to Use

• Parallelizing C/C++/Fortran loops on multicore CPUs
• Implementing reductions (sum, max, custom)
• Task parallelism for irregular workloads
• Offloading compute to GPU with OpenMP target directives
• Diagnosing scaling failures (false sharing, load imbalance)
• Tuning thread count and spin behavior

Workflow

1. Basic parallel for

#include <omp.h>
#include <stdio.h>

int main(void) {
    const int n = 1000000;
    double sum = 0.0;

    #pragma omp parallel for reduction(+:sum)
    for (int i = 0; i < n; i++)
        sum += i * 0.001;

    printf("sum = %f, threads = %d\n", sum, omp_get_max_threads());
    return 0;
}

gcc -fopenmp -O3 -o omp_sum omp_sum.c
export OMP_NUM_THREADS=8
./omp_sum

2. Schedule clauses

#pragma omp parallel for schedule(static)          // equal chunks, low overhead
#pragma omp parallel for schedule(dynamic, 64)     // dynamic chunks of 64
#pragma omp parallel for schedule(guided)          // decreasing chunk size
#pragma omp parallel for schedule(auto)            // compiler/runtime decides

Schedule	Best for
static	Uniform work per iteration
dynamic	Variable iteration cost
guided	Decreasing iteration cost
static,1	Cache blocking with interleaved chunks

3. Data sharing attributes

int shared_var = 0;
#pragma omp parallel private(i) shared(shared_var)
{
    int i = omp_get_thread_num();
    #pragma omp atomic
    shared_var += i;
}

// firstprivate — copy in; lastprivate — copy out after loop
#pragma omp parallel for firstprivate(offset) lastprivate(result)
for (int i = 0; i < n; i++) { ... }