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// RUN: %libomptarget-compilexx-and-run-generic
// RUN: %libomptarget-compileoptxx-and-run-generic
// FIXME: This is a bug in host offload, this should run fine.
// REQUIRES: gpu
// This test validates that the OpenMP target reductions to find a minimum work
// as indended for a few common data types.
#include <algorithm>
#include <cassert>
#include <limits>
#include <vector>
template <class Tp> void test_min_idx_reduction() {
const Tp length = 1000;
const Tp nminimas = 8;
std::vector<float> a(length, 3.0f);
const Tp step = length / nminimas;
for (Tp i = 0; i < nminimas; i++) {
a[i * step] -= 1.0f;
}
for (Tp i = 0; i < nminimas; i++) {
Tp idx = a.size();
float *b = a.data();
#pragma omp target teams distribute parallel for reduction(min : idx) \
map(always, to : b[0 : length])
for (Tp j = 0; j < length - 1; j++) {
if (b[j] < b[j + 1]) {
idx = std::min(idx, j);
}
}
assert(idx == i * step &&
"#pragma omp target teams distribute parallel for "
"reduction(min:<identifier list>) does not work as intended.");
a[idx] += 1.0f;
}
}
template <class Tp> void test_min_val_reduction() {
const int length = 1000;
const int half = length / 2;
std::vector<Tp> a(length, (Tp)3);
a[half] -= (Tp)1;
Tp min_val = std::numeric_limits<Tp>::max();
Tp *b = a.data();
#pragma omp target teams distribute parallel for reduction(min : min_val) \
map(always, to : b[0 : length])
for (int i = 0; i < length; i++) {
min_val = std::min(min_val, b[i]);
}
assert(std::abs(((double)a[half + 1]) - ((double)min_val) - 1.0) < 1e-6 &&
"#pragma omp target teams distribute parallel for "
"reduction(min:<identifier list>) does not work as intended.");
}
int main() {
// Reducing over indices
test_min_idx_reduction<int>();
test_min_idx_reduction<unsigned int>();
test_min_idx_reduction<long>();
// Reducing over values
test_min_val_reduction<int>();
test_min_val_reduction<unsigned int>();
test_min_val_reduction<long>();
test_min_val_reduction<float>();
test_min_val_reduction<double>();
return 0;
}
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