/openmp

name: openmp
description: OpenMP skill for shared-memory parallel programming. Use when writing parallel for loops, reductions, task parallelism, SIMD directives, GPU offloading, or profiling with Score-P/TAU. Activates on queries about OpenMP, pragma omp, schedule static dynamic, reduction, false sharing, or OMP_NUM_THREADS.

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++) { ... }

| Clause | Meaning | |--------|---------| | `private` | Uninitialized per-thread copy | | `shared` | One variable, all threads | | `reduction(op:var)` | Combine at end (+, *, max, &&, \|\|) | | `firstprivate` | Initialize from master | | `lastprivate` | Master gets last iteration value |

4. SIMD vectorization hint

#pragma omp simd
for (int i = 0; i < n; i++)
    c[i] = a[i] + b[i];

// SIMD + parallel
#pragma omp parallel for simd
for (int i = 0; i < n; i++)
    c[i] = a[i] * b[i];

Requires `-fopenmp-simd` or `-fopenmp` with compiler SIMD support. Check with `-fopt-info-vec`.

5. Task parallelism

#pragma omp parallel
{
    #pragma omp single
    {
        for (int i = 0; i < 10; i++) {
            #pragma omp task firstprivate(i)
            process_subtree(i);
        }
        #pragma omp taskwait
    }
}

Tasks suit recursive algorithms (quicksort, tree traversal) where loop parallelism doesn't fit.

6. Timing

double start = omp_get_wtime();
#pragma omp parallel for
for (int i = 0; i < n; i++) work(i);
double elapsed = omp_get_wtime() - start;
printf("elapsed: %f s\n", elapsed);

7. GPU target offloading (OpenMP 5.x)

#pragma omp target teams distribute parallel for map(to:a[0:n]) map(from:c[0:n])
for (int i = 0; i < n; i++)
    c[i] = a[i] * 2.0f;

# NVIDIA offload
gcc -fopenmp -foffload=-march=sm_80 -o offload offload.c

# Check device
export OMP_DEFAULT_TARGET_DEVICE=1

Requires compiler offload support (GCC offload, Clang/OpenMP, NVIDIA HPC SDK).

8. Environment variables

export OMP_NUM_THREADS=16
export OMP_PROC_BIND=close        # bind threads to nearby cores
export OMP_PLACES=cores
export GOMP_SPINCOUNT=2000        # spin before sleep
export OMP_WAIT_POLICY=active     # active vs passive waiting
export OMP_DISPLAY_ENV=true       # print config at startup

9. Profiling

# Score-P (compile with wrapper)
scorep gcc -fopenmp -o app app.c
export SCOREP_METRIC_MANAGER=1
scorep ./app
scorep-score -f scorep_*/profile.cubex

# TAU
tau_cc.sh -fopenmp -o app app.c
export TAU_TRACE=1
./app
pprof app profile.*

10. Pitfalls

**False sharing**: threads modify adjacent cache lines.

// Bad: sum_array[tid] on same cache line
#pragma omp parallel
{
    int tid = omp_get_thread_num();
    sum_array[tid] += local_sum;  // pad to 64 bytes between elements
}

// Fix: padding
double sum_padded[MAX_THREADS][8];  // 8 doubles = 64 bytes

**Nested parallelism**:

export OMP_MAX_ACTIVE_LEVELS=2
export OMP_NESTED=true   # deprecated; use MAX_ACTIVE_LEVELS

Common Problems

| Symptom | Cause | Fix | |---------|-------|-----| | No speedup | Loop too small | Increase work; check `if` clause threshold | | Wrong reduction result | Race on non-reduction var | Use `reduction` or `atomic` | | Slower with more threads | False sharing | Pad per-thread arrays | | GPU offload fails | No target device | Check `-foffload`; `nvidia-smi` | | Threads not bound | Default spread | `OMP_PROC_BIND=close` | | Nested deadlock | Oversubscription | Limit `OMP_NUM_THREADS` per level |

Related Skills

• `skills/hpc/mpi` — distributed memory complement
• `skills/low-level-programming/cpu-cache-opt` — false sharing deep dive
• `skills/gpu/cuda` — GPU programming alternative to target offload
• `skills/profilers/intel-vtune-amd-uprof` — OpenMP region analysis in VTune
• `skills/compilers/gcc` — `-fopenmp` flags
• `skills/allocators/numa-programming` — NUMA-aware thread binding