[OpenMP][NVPTX] Take functions in `deviceRTLs` as `convergent`

OpenMP device compiler (similar to other SPMD compilers) assumes that
functions are convergent by default to avoid invalid transformations, such as
the bug (https://bugs.llvm.org/show_bug.cgi?id=49021).

Reviewed By: jdoerfert

Differential Revision: https://reviews.llvm.org/D95971
This commit is contained in:
Shilei Tian 2021-02-03 20:57:59 -05:00
parent 26b5be66f9
commit 0f0ce3c12e
3 changed files with 100 additions and 0 deletions

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@ -2441,6 +2441,8 @@ void CompilerInvocation::ParseLangArgs(LangOptions &Opts, ArgList &Args,
bool IsTargetSpecified =
Opts.OpenMPIsDevice || Args.hasArg(options::OPT_fopenmp_targets_EQ);
Opts.ConvergentFunctions = Opts.ConvergentFunctions || Opts.OpenMPIsDevice;
if (Opts.OpenMP || Opts.OpenMPSimd) {
if (int Version = getLastArgIntValue(
Args, OPT_fopenmp_version_EQ,

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@ -0,0 +1,13 @@
// RUN: %clang_cc1 -debug-info-kind=limited -verify -fopenmp -x c++ -triple nvptx64-unknown-unknown -fopenmp-targets=nvptx64-nvidia-cuda -emit-llvm %s -fopenmp-is-device -o - | FileCheck %s
// RUN: %clang_cc1 -debug-info-kind=limited -verify -fopenmp -x c++ -triple nvptx-unknown-unknown -fopenmp-targets=nvptx-nvidia-cuda -emit-llvm %s -fopenmp-is-device -o - | FileCheck %s
// expected-no-diagnostics
#pragma omp declare target
void foo() {}
#pragma omp end declare target
// CHECK: Function Attrs: {{.*}}convergent{{.*}}
// CHECK: define hidden void @_Z3foov() [[ATTRIBUTE_NUMBER:#[0-9]+]]
// CHECK: attributes [[ATTRIBUTE_NUMBER]] = { {{.*}}convergent{{.*}} }

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@ -0,0 +1,85 @@
// RUN: %libomptarget-compilexx-aarch64-unknown-linux-gnu -O3 && %libomptarget-run-aarch64-unknown-linux-gnu
// RUN: %libomptarget-compilexx-powerpc64-ibm-linux-gnu -O3 && %libomptarget-run-powerpc64-ibm-linux-gnu
// RUN: %libomptarget-compilexx-powerpc64le-ibm-linux-gnu -O3 && %libomptarget-run-powerpc64le-ibm-linux-gnu
// RUN: %libomptarget-compilexx-x86_64-pc-linux-gnu -O3 && %libomptarget-run-x86_64-pc-linux-gnu
// RUN: %libomptarget-compilexx-nvptx64-nvidia-cuda -O3 && %libomptarget-run-nvptx64-nvidia-cuda
#include <iostream>
template <typename T> int test_map() {
std::cout << "map(complex<>)" << std::endl;
T a(0.2), a_check;
#pragma omp target map(from : a_check)
{ a_check = a; }
if (a_check != a) {
std::cout << " wrong results";
return 1;
}
return 0;
}
template <typename T> int test_reduction() {
std::cout << "flat parallelism" << std::endl;
T sum(0), sum_host(0);
const int size = 100;
T array[size];
for (int i = 0; i < size; i++) {
array[i] = i;
sum_host += array[i];
}
#pragma omp target teams distribute parallel for map(to: array[:size]) \
reduction(+ : sum)
for (int i = 0; i < size; i++)
sum += array[i];
if (sum != sum_host)
std::cout << " wrong results " << sum << " host " << sum_host << std::endl;
std::cout << "hierarchical parallelism" << std::endl;
const int nblock(10), block_size(10);
T block_sum[nblock];
#pragma omp target teams distribute map(to \
: array[:size]) \
map(from \
: block_sum[:nblock])
for (int ib = 0; ib < nblock; ib++) {
T partial_sum = 0;
const int istart = ib * block_size;
const int iend = (ib + 1) * block_size;
#pragma omp parallel for reduction(+ : partial_sum)
for (int i = istart; i < iend; i++)
partial_sum += array[i];
block_sum[ib] = partial_sum;
}
sum = 0;
for (int ib = 0; ib < nblock; ib++) {
sum += block_sum[ib];
}
if (sum != sum_host) {
std::cout << " wrong results " << sum << " host " << sum_host << std::endl;
return 1;
}
return 0;
}
template <typename T> int test_complex() {
int ret = 0;
ret |= test_map<T>();
ret |= test_reduction<T>();
return ret;
}
int main() {
int ret = 0;
std::cout << "Testing float" << std::endl;
ret |= test_complex<float>();
std::cout << "Testing double" << std::endl;
ret |= test_complex<double>();
return ret;
}