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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.
npx skill4agent add mohitmishra786/low-level-dev-skills openmp#pragma omp parallel fortarget#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#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 |
|---|---|
| Uniform work per iteration |
| Variable iteration cost |
| Decreasing iteration cost |
| Cache blocking with interleaved chunks |
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 |
|---|---|
| Uninitialized per-thread copy |
| One variable, all threads |
| Combine at end (+, *, max, &&, ||) |
| Initialize from master |
| Master gets last iteration value |
#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];-fopenmp-simd-fopenmp-fopt-info-vec#pragma omp parallel
{
#pragma omp single
{
for (int i = 0; i < 10; i++) {
#pragma omp task firstprivate(i)
process_subtree(i);
}
#pragma omp taskwait
}
}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);#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=1export 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# 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.*// 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 bytesexport OMP_MAX_ACTIVE_LEVELS=2
export OMP_NESTED=true # deprecated; use MAX_ACTIVE_LEVELS| Symptom | Cause | Fix |
|---|---|---|
| No speedup | Loop too small | Increase work; check |
| Wrong reduction result | Race on non-reduction var | Use |
| Slower with more threads | False sharing | Pad per-thread arrays |
| GPU offload fails | No target device | Check |
| Threads not bound | Default spread | |
| Nested deadlock | Oversubscription | Limit |
skills/hpc/mpiskills/low-level-programming/cpu-cache-optskills/gpu/cudaskills/profilers/intel-vtune-amd-uprofskills/compilers/gcc-fopenmpskills/allocators/numa-programming