Can I use `omp_get_thread_num()` on the GPU? - g++

Question

I have OpenMP code which works on the CPU by having each thread manage memory addressed by the thread's id number, accessible via omp_get_thread_num(). This works well on the CPU, but can it work on the GPU?
A MWE is:
#include <iostream>
#include <omp.h>
int main(){
const int SIZE = 400000;
int *m;
m = new int[SIZE];
#pragma omp target
{
#pragma omp parallel for
for(int i=0;i<SIZE;i++)
m[i] = omp_get_thread_num();
}
for(int i=0;i<SIZE;i++)
std::cout<<m[i]<<"\n";
}

Answer 1

It works fine on the GPU for me with GCC. You need to map m thoough e.g. like this
#pragma omp target map(tofrom:m[0:SIZE])
I compiled like this
g++ -O3 -Wall -fopenmp -fno-stack-protector so.cpp
You can see an example for system without offloading here
http://coliru.stacked-crooked.com/a/1e756410d6e2db61
A method I use to find out the number of teams and threads before doing work is this:
#pragma omp target teams defaultmap(tofrom:scalar)
{
nteams = omp_get_num_teams();
#pragma omp parallel
#pragma omp single
nthreads = omp_get_num_threads();
}
On my system with GCC 7.2, Ubuntu 17.10, and gcc-offload-nvptx with a GTX 1060 I get nteams = 30 and nthreads = 8. See this answer where I do a custom reduction for a target region using threads and teams. With -offload=disable nteams = 1 and nthreads = 8 (4 core/8 hardware thread CPU).
I added -fopt-info to the compile options and I get only the message
note: basic block vectorized

Answer 2

The answer seems to be no.
Compiling with PGI using:
pgc++ -fast -mp -ta=tesla,pinned,cc60 -Minfo=all test2.cpp
gives:
13, Parallel region activated
Parallel loop activated with static block schedule
Loop not vectorized/parallelized: contains call
14, Parallel region terminated
whereas compiling with GCC using
g++ -O3 test2.cpp -fopenmp -fopt-info
gives
test2.cpp:17: note: not vectorized: loop contains function calls or data references that cannot be analyzed
test2.cpp:17: note: bad data references.