// This is a reduction operator which now takes as first argument the
// TeamPolicy member_type. Every member of the team contributes to the
// total sum.
// It is helpful to think of this operator as a parallel region for a team
// (i.e. every team member is active and will execute the code).
KOKKOS_INLINE_FUNCTION
void operator() ( const team_member & thread, int& sum) const {
sum+=1;
// The TeamPolicy<>::member_type provides functions to query the multi
// dimensional index of a thread as well as the number of thread-teams and the size
// of each team.
printf("Hello World: %i %i // %i %i\n",thread.league_rank(),thread.team_rank(),thread.league_size(),thread.team_size());
}
KOKKOS_INLINE_FUNCTION void
operator() (const team_member& dev) const
{
Kokkos::parallel_for(Kokkos::TeamThreadRange(dev,0,rows_per_team), [&] (const ordinal_type& loop) {
const ordinal_type iRow = static_cast<ordinal_type> ( dev.league_rank() ) * rows_per_team + loop;
if (iRow >= m_A.numRows ()) {
return;
}
const KokkosSparse::SparseRowViewConst<AMatrix> row = m_A.rowConst(iRow);
const ordinal_type row_length = static_cast<ordinal_type> (row.length);
value_type sum = 0;
Kokkos::parallel_reduce(Kokkos::ThreadVectorRange(dev,row_length), [&] (const ordinal_type& iEntry, value_type& lsum) {
const value_type val = conjugate ?
ATV::conj (row.value(iEntry)) :
row.value(iEntry);
lsum += val * m_x(row.colidx(iEntry));
},sum);
Kokkos::single(Kokkos::PerThread(dev), [&] () {
sum *= alpha;
if (dobeta == 0) {
m_y(iRow) = sum ;
} else {
m_y(iRow) = beta * m_y(iRow) + sum;
}
});
});
}
KOKKOS_INLINE_FUNCTION
void operator() ( const team_member & thread) const {
int i = thread.league_rank();
// Allocate a shared array for the team.
shared_1d_int count(thread.team_shmem(),data.dimension_1());
// With each team run a parallel_for with its threads
Kokkos::parallel_for(Kokkos::TeamThreadRange(thread,data.dimension_1()), [=] (const int& j) {
int tsum;
// Run a vector loop reduction over the inner dimension of data
// Count how many values are multiples of 4
// Every vector lane gets the same reduction value (tsum) back, it is broadcast to all vector lanes
Kokkos::parallel_reduce(Kokkos::ThreadVectorRange(thread,data.dimension_2()), [=] (const int& k, int & vsum) {
vsum+= (data(i,j,k) % 4 == 0)?1:0;
},tsum);
// Make sure only one vector lane adds the reduction value to the shared array, i.e. execute
// the next line only once PerThread
Kokkos::single(Kokkos::PerThread(thread),[=] () {
count(j) = tsum;
});
});
// Wait for all threads to finish the parallel_for so that all shared memory writes are done
thread.team_barrier();
// Check with one vector lane from each thread how many consecutive
// data segments have the same number of values divisible by 4
// The team reduction value is again broadcast to every team member (and every vector lane)
int team_sum = 0;
Kokkos::parallel_reduce(Kokkos::TeamThreadRange(thread, data.dimension_1()-1), [=] (const int& j, int& thread_sum) {
// It is not valid to directly add to thread_sum
// Use a single function with broadcast instead
// team_sum will be used as input to the operator (i.e. it is used to initialize sum)
// the end value of sum will be broadcast to all vector lanes in the thread.
Kokkos::single(Kokkos::PerThread(thread),[=] (int& sum) {
if(count(j)==count(j+1)) sum++;
},thread_sum);
},team_sum);
// Add with one thread and vectorlane of the team the team_sum to the global value
Kokkos::single(Kokkos::PerTeam(thread),[=] () {
Kokkos::atomic_add(&gsum(),team_sum);
});
}
KOKKOS_INLINE_FUNCTION
void operator()(const team_member& team) const
{
const int team_offset = (team.league_rank() + halo_depth)*y;
Kokkos::parallel_for(
Kokkos::TeamThreadRange(team, halo_depth, y-halo_depth),
[&] (const int &j)
{
const int index = team_offset + j;
p(index) = beta*p(index) + r(index);
});
}
KOKKOS_INLINE_FUNCTION
void operator()(const team_member& team, value_type& rrn) const
{
double rrn_team = 0.0;
const int team_offset = (team.league_rank() + halo_depth)*y;
Kokkos::parallel_reduce(
Kokkos::TeamThreadRange(team, halo_depth, y-halo_depth),
[&] (const int &j, double& rrn_thread)
{
const int index = team_offset + j;
u(index) += alpha*p(index);
r(index) -= alpha*w(index);
rrn_thread += r(index)*r(index);
}, rrn_team);
Kokkos::single(Kokkos::PerTeam(team), [&] ()
{
rrn += rrn_team;
});
}
KOKKOS_INLINE_FUNCTION
void operator()(const team_member& team, value_type& pw) const
{
double pw_team = 0.0;
const int team_offset = (team.league_rank() + halo_depth)*y;
Kokkos::parallel_reduce(
Kokkos::TeamThreadRange(team, halo_depth, y-halo_depth),
[&] (const int &j, double& pw_thread)
{
const int index = team_offset + j;
const double smvp = SMVP(p);
w(index) = smvp;
pw_thread += smvp*p(index);
}, pw_team);
Kokkos::single(Kokkos::PerTeam(team), [&] ()
{
pw += pw_team;
});
}
KOKKOS_INLINE_FUNCTION
void operator() ( const team_member & dev) const {
Kokkos::View<int**,Kokkos::MemoryUnmanaged> l_histogram(dev.team_shmem(),TEAM_SIZE,TEAM_SIZE);
Kokkos::View<int*,Kokkos::MemoryUnmanaged> l_data(dev.team_shmem(),chunk_size+1);
const int i = dev.league_rank() * chunk_size;
for(int j = dev.team_rank(); j<chunk_size+1; j+=dev.team_size())
l_data(j) = data(i+j);
for(int k = dev.team_rank(); k < TEAM_SIZE; k+=dev.team_size())
for(int l = 0; l < TEAM_SIZE; l++)
l_histogram(k,l) = 0;
dev.team_barrier();
for(int j = 0; j<chunk_size; j++) {
for(int k = dev.team_rank(); k < TEAM_SIZE; k+=dev.team_size())
for(int l = 0; l < TEAM_SIZE; l++) {
if((l_data(j) == k) && (l_data(j+1)==l))
l_histogram(k,l)++;
}
}
for(int k = dev.team_rank(); k < TEAM_SIZE; k+=dev.team_size())
for(int l = 0; l < TEAM_SIZE; l++) {
Kokkos::atomic_fetch_add(&histogram(k,l),l_histogram(k,l));
}
dev.team_barrier();
}
