TEST_F( KokkosThreads, SerialInitialize)
{
// allocate a rank 2 array witn that is RUN_TIME_DIMENSION x COMPILE_TIME_DIMENSION
// View will default initialize all the values unless it is explicitly disabled, ie,
// Kokkos::View<unsigned*[COMPILE_TIME_DIMENSION], KOKKOS_THREAD_DEVICE> a("node views", RUN_TIME_DIMENSION);
// zero fills the array, but
// Kokkos::View<unsigned*[COMPILE_TIME_DIMENSION], KOKKOS_THREAD_DEVICE> a( Kokkos::ViewAllocateWithoutInitializing("node views"), RUN_TIME_DIMENSION);
// will allocate without initializing the array
Kokkos::View<unsigned*[COMPILE_TIME_DIMENSION], KOKKOS_THREAD_DEVICE> a( Kokkos::ViewAllocateWithoutInitializing("node views"), RUN_TIME_DIMENSION);
for (size_t i=0; i < a.dimension_0(); ++i) {
for (size_t x=0; x < a.dimension_1(); ++x) {
a(i,x) = i;
}
}
// get a const view to the same array
// this view shares the same memory as a, but cannot modify the values
Kokkos::View<const unsigned*[COMPILE_TIME_DIMENSION], KOKKOS_THREAD_DEVICE> b = a;
for (size_t i=0; i < b.dimension_0(); ++i) {
for (size_t x=0; x < b.dimension_1(); ++x) {
EXPECT_EQ(i, b(i,x));
}
}
}
TEST_F( KokkosThreads, LambdaInitialize)
{
Kokkos::View<unsigned*[COMPILE_TIME_DIMENSION], KOKKOS_THREAD_DEVICE> a( Kokkos::ViewAllocateWithoutInitializing("node views"), RUN_TIME_DIMENSION);
Kokkos::parallel_for<KOKKOS_THREAD_DEVICE>(
a.dimension_0() ,
[=](size_t i) {
for (size_t x=0; x < a.dimension_1(); ++x) {
a(i,x) = i;
}
}
);
Kokkos::View<const unsigned*[COMPILE_TIME_DIMENSION], KOKKOS_THREAD_DEVICE> b = a;
int num_error = 0;
// Cannot portably call a GTEST macro in parallel
// count the errors and test that they are equal to zero
Kokkos::parallel_reduce<KOKKOS_THREAD_DEVICE, int /*reduction value type */>(
b.dimension_0() ,
[](int & local_errors) // init lambda
{ local_errors = 0; } ,
[=](size_t i, int & local_errors) { // operator() lambda
for (size_t x=0; x < b.dimension_1(); ++x)
local_errors += i == b(i,x) ? 0 : 1;
} ,
[](volatile int & dst_err, volatile int const& src_err) // join lambda
{ dst_err += src_err; } ,
num_errors // where to store the result
);
EXPECT_EQ( 0, num_errors);
}
Teuchos::RCP<const Map<LocalOrdinal,GlobalOrdinal,Kokkos::Compat::KokkosDeviceWrapperNode<DeviceType> > >
Map<LocalOrdinal,GlobalOrdinal,Kokkos::Compat::KokkosDeviceWrapperNode<DeviceType> >::
replaceCommWithSubset (const Teuchos::RCP<const Teuchos::Comm<int> >& newComm) const
{
using Teuchos::ArrayView;
using Teuchos::outArg;
using Teuchos::RCP;
using Teuchos::REDUCE_MIN;
using Teuchos::reduceAll;
typedef global_size_t GST;
typedef LocalOrdinal LO;
typedef GlobalOrdinal GO;
typedef Map<LO, GO, node_type> map_type;
// mfh 26 Mar 2013: The lazy way to do this is simply to recreate
// the Map by calling its ordinary public constructor, using the
// original Map's data. This only involves O(1) all-reduces over
// the new communicator, which in the common case only includes a
// small number of processes.
// Create the Map to return.
if (newComm.is_null ()) {
return Teuchos::null; // my process does not participate in the new Map
} else {
// Map requires that the index base equal the global min GID.
// Figuring out the global min GID requires a reduction over all
// processes in the new communicator. It could be that some (or
// even all) of these processes contain zero entries. (Recall
// that this method, unlike removeEmptyProcesses(), may remove
// an arbitrary subset of processes.) We deal with this by
// doing a min over the min GID on each process if the process
// has more than zero entries, or the global max GID, if that
// process has zero entries. If no processes have any entries,
// then the index base doesn't matter anyway.
const GO myMinGid = (this->getNodeNumElements () == 0) ?
this->getMaxAllGlobalIndex () : this->getMinGlobalIndex ();
GO newIndexBase = this->getInvalidGlobalIndex ();
reduceAll<int, GO> (*newComm, REDUCE_MIN, myMinGid, outArg (newIndexBase));
// Make Map's constructor compute the global number of indices.
const GST globalNumInds = Teuchos::OrdinalTraits<GST>::invalid ();
if (mapDevice_.initialized ()) {
Kokkos::View<const GO*, DeviceType> myGIDs =
mapDevice_.getMyGlobalIndices ();
return rcp (new map_type (globalNumInds, myGIDs, newIndexBase,
newComm, this->getNode ()));
}
else {
Kokkos::View<const GO*, host_mirror_device_type> myGidsHostView =
mapHost_.getMyGlobalIndices ();
ArrayView<const GO> myGidsArrayView (myGidsHostView.ptr_on_device (),
myGidsHostView.dimension_0 ());
return rcp (new map_type (globalNumInds, myGidsArrayView, newIndexBase,
newComm, this->getNode ()));
}
}
}
static void GEMM(Teuchos::ETransp transA, Teuchos::ETransp transB, Scalar alpha,
Kokkos::View<Scalar***,Kokkos::LayoutLeft,Kokkos::DefaultExecutionSpace> A, Kokkos::View<Scalar***,Kokkos::LayoutLeft,Kokkos::DefaultExecutionSpace> B,
Scalar beta, Kokkos::View<Scalar***,Kokkos::LayoutLeft,Kokkos::DefaultExecutionSpace> C){
const int m = static_cast<int> (C.dimension_1()),
n = static_cast<int> (C.dimension_2 ()),
k = (transA == Teuchos::NO_TRANS ? A.dimension_2 () : A.dimension_1 ());
// printf("m:%d,n:%d,k:%d",m,n,k);
Kokkos::parallel_for(C.dimension(0),blasOpenMPBatchLeft<Scalar>(A,B,C,m,n,k,transA,transB,alpha,beta));
}
KOKKOS_INLINE_FUNCTION
void operator() (int i) const {
double tmp = 0.0;
for(int j = 0; j < idx.dimension_1(); j++) {
const double val = src(idx(i,j));
tmp += val*val + 0.5*(idx.dimension_0()*val -idx.dimension_1()*val);
}
dest(i) += tmp;
}
Teuchos::ArrayView<const GlobalOrdinal>
Map<LocalOrdinal,GlobalOrdinal,Kokkos::Compat::KokkosDeviceWrapperNode<DeviceType> >::
getNodeElementList () const
{
typedef GlobalOrdinal GO;
Kokkos::View<const GO*, host_mirror_device_type> myGlobalInds =
mapHost_.getMyGlobalIndices (); // creates it if it doesn't exist
return Teuchos::ArrayView<const GO> (myGlobalInds.ptr_on_device (),
myGlobalInds.dimension_0 ());
}
static void GEMM(Teuchos::ETransp transA, Teuchos::ETransp transB, Scalar alpha,
Kokkos::View<Scalar***,Kokkos::LayoutRight,Kokkos::DefaultExecutionSpace> A, Kokkos::View<Scalar***,Kokkos::LayoutRight,Kokkos::DefaultExecutionSpace> B,
Scalar beta, Kokkos::View<Scalar***,Kokkos::LayoutRight,Kokkos::DefaultExecutionSpace> C){
const int m = static_cast<int> (C.dimension_1()),
n = static_cast<int> (C.dimension_2 ()),
k = (transA == Teuchos::NO_TRANS ? A.dimension_2 () : A.dimension_1 ());
Teuchos::BLAS<int,Scalar>blas;
Kokkos::parallel_for(C.dimension_0(),KOKKOS_LAMBDA (const size_t i) {
blas.GEMM(transB, transA, n, m, k, alpha,
&B(i,0,0), n,
&A(i,0,0), k,
beta, &C(i,0,0), n);
});
void NearestNeighborOperator<DeviceType>::apply(
Kokkos::View<double const *, DeviceType> source_values,
Kokkos::View<double *, DeviceType> target_values ) const
{
// Precondition: check that the source and target are properly sized
DTK_REQUIRE( _indices.extent( 0 ) == target_values.extent( 0 ) );
DTK_REQUIRE( _size == source_values.extent_int( 0 ) );
auto values = Details::NearestNeighborOperatorImpl<DeviceType>::fetch(
_comm, _ranks, _indices, source_values );
Kokkos::deep_copy( target_values, values );
}
FieldContainer_Kokkos(Kokkos::View<ScalarPoindex_typeer,Kokkos::LayoutLeft,Kokkos::OpenMP>& InContainer){
dim0=dim[0]=InContainer.dimension(0);
dim1=dim[1]=InContainer.dimension(1);
dim2=dim[2]=InContainer.dimension(2);
dim3=dim[3]=InContainer.dimension(3);
dim4=dim[4]=InContainer.dimension(4);
dim5=dim[5]=InContainer.dimension(5);
dim6=dim[6]=InContainer.dimension(6);
dim7=dim[7]=InContainer.dimension(7);
rankValue=Kokkos::View<ScalarPoindex_typeer,Kokkos::LayoutLeft,Kokkos::OpenMP>::Rank;
intepidManaged=false;
switch(rankValue){
case 1:
sizeValue=dim0;
break;
case 2:
sizeValue=dim0*dim1;
break;
case 3:
sizeValue=dim0*dim1*dim2;
break;
case 4:
sizeValue=dim0*dim1*dim2*dim3;
break;
case 5:
sizeValue=dim0*dim1*dim2*dim3*dim4;
break;
case 6:
sizeValue=dim0*dim1*dim2*dim3*dim4*dim5;
break;
case 7:
sizeValue=dim0*dim1*dim2*dim3*dim4*dim5*dim6;
break;
case 8:
sizeValue=dim0*dim1*dim2*dim3*dim4*dim5*dim6*dim7;
break;
}
containerMemory=InContainer.ptr_on_device();
}
KOKKOS_INLINE_FUNCTION
NestedView & operator = ( const Kokkos::View<int*,Space> & lhs )
{
member = lhs ;
if ( member.dimension_0() ) Kokkos::atomic_add( & member(0) , 1 );
return *this ;
}
KOKKOS_INLINE_FUNCTION
~NestedView()
{
if ( member.dimension_0() ) {
Kokkos::atomic_add( & member(0) , -1 );
}
}
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);
});
}
void kk_inspector_matvec(AType A, XType x, YType y, int rows_per_thread, int team_size, int vector_length) {
typedef typename XType::non_const_value_type Scalar;
typedef typename AType::execution_space execution_space;
typedef KokkosSparse::CrsMatrix<const Scalar,int,execution_space,void,int> matrix_type ;
typedef typename Kokkos::View<Scalar*,Kokkos::LayoutLeft,execution_space> y_type;
typedef typename Kokkos::View<const Scalar*,Kokkos::LayoutLeft,execution_space,Kokkos::MemoryRandomAccess > x_type;
//int rows_per_team = launch_parameters<execution_space>(A.numRows(),A.nnz(),rows_per_thread,team_size,vector_length);
//static int worksets = (y.extent(0)+rows_per_team-1)/rows_per_team;
static int worksets = std::is_same<Schedule,Kokkos::Static>::value ?
team_size>0?execution_space::concurrency()/team_size:execution_space::concurrency() : //static
team_size>0?execution_space::concurrency()*32/team_size:execution_space::concurrency()*32 ; //dynamic
static Kokkos::View<int*> workset_offsets;
if(workset_offsets.extent(0) == 0) {
workset_offsets = Kokkos::View<int*> ("WorksetOffsets",worksets+1);
const size_t nnz = A.nnz();
int nnz_per_workset = (nnz+worksets-1)/worksets;
workset_offsets(0) = 0;
int ws = 1;
for(int row = 0; row<A.numRows(); row++) {
if(A.graph.row_map(row) > ws*nnz_per_workset) {
workset_offsets(ws) = row;
ws++;
}
}
if(workset_offsets(ws-1) < A.numRows()) {
workset_offsets(ws) = A.numRows();
}
printf("Worksets: %i %i\n",worksets,ws);
worksets = ws;
}
double s_a = 1.0;
double s_b = 0.0;
SPMV_Inspector_Functor<matrix_type,x_type,y_type,0,false,int> func (s_a,A,x,workset_offsets,s_b,y);
Kokkos::TeamPolicy<Kokkos::Schedule<Schedule> > policy(1,1);
if(team_size>0)
policy = Kokkos::TeamPolicy<Kokkos::Schedule<Schedule> >(worksets,team_size,vector_length);
else
policy = Kokkos::TeamPolicy<Kokkos::Schedule<Schedule> >(worksets,Kokkos::AUTO,vector_length);
Kokkos::parallel_for("KokkosSparse::PerfTest::SpMV_Inspector", policy,func);
}
/** Access the local IDs for an element. The local ordering is according to
* the <code>getOwnedAndSharedIndices</code> method. Note
*/
void getElementLIDs(Kokkos::View<const int*,PHX::Device> cellIds,
Kokkos::View<LocalOrdinalT**,PHX::Device> lids) const
{
CopyCellLIDsFunctor functor;
functor.cellIds = cellIds;
functor.global_lids = localIDs_k_;
functor.local_lids = lids; // we assume this array is sized correctly!
Kokkos::parallel_for(cellIds.dimension_0(),functor);
}
void multiply(const CrsMatrix< float , Kokkos::OpenMP >& A,
const Kokkos::View< float* , Kokkos::OpenMP >& x,
Kokkos::View< float* , Kokkos::OpenMP >& y,
MKLMultiply tag)
{
MKL_INT n = A.graph.row_map.dimension_0() - 1 ;
float *A_values = A.values.ptr_on_device() ;
MKL_INT *col_indices = A.graph.entries.ptr_on_device() ;
MKL_INT *row_beg = const_cast<MKL_INT*>(A.graph.row_map.ptr_on_device()) ;
MKL_INT *row_end = row_beg+1;
char matdescra[6] = { 'G', 'x', 'N', 'C', 'x', 'x' };
char trans = 'N';
float alpha = 1.0;
float beta = 0.0;
float *x_values = x.ptr_on_device() ;
float *y_values = y.ptr_on_device() ;
mkl_scsrmv(&trans, &n, &n, &alpha, matdescra, A_values, col_indices,
row_beg, row_end, x_values, &beta, y_values);
}
NearestNeighborOperator<DeviceType>::NearestNeighborOperator(
MPI_Comm comm, Kokkos::View<Coordinate const **, DeviceType> source_points,
Kokkos::View<Coordinate const **, DeviceType> target_points )
: _comm( comm )
, _indices( "indices" )
, _ranks( "ranks" )
, _size( source_points.extent_int( 0 ) )
{
// NOTE: instead of checking the pre-condition that there is at least one
// source point passed to one of the rank, we let the tree handle the
// communication and just check that the tree is not empty.
// Build distributed search tree over the source points.
DistributedSearchTree<DeviceType> search_tree( _comm, source_points );
// Tree must have at least one leaf, otherwise it makes little sense to
// perform the search for nearest neighbors.
DTK_CHECK( !search_tree.empty() );
// Query nearest neighbor for all target points.
auto nearest_queries = Details::NearestNeighborOperatorImpl<
DeviceType>::makeNearestNeighborQueries( target_points );
// Perform the actual search.
Kokkos::View<int *, DeviceType> indices( "indices" );
Kokkos::View<int *, DeviceType> offset( "offset" );
Kokkos::View<int *, DeviceType> ranks( "ranks" );
search_tree.query( nearest_queries, indices, offset, ranks );
// Check post-condition that we did find a nearest neighbor to all target
// points.
DTK_ENSURE( lastElement( offset ) == target_points.extent_int( 0 ) );
// Save results.
// NOTE: we don't bother keeping `offset` around since it is just `[0, 1, 2,
// ..., n_target_poins]`
_indices = indices;
_ranks = ranks;
}
static Kokkos::View<size_t *, DeviceType>
sortQueriesAlongZOrderCurve( Box const &scene_bounding_box,
Kokkos::View<Query *, DeviceType> queries )
{
auto const n_queries = queries.extent( 0 );
Kokkos::View<unsigned int *, DeviceType> morton_codes(
Kokkos::ViewAllocateWithoutInitializing( "morton" ), n_queries );
Kokkos::parallel_for(
ARBORX_MARK_REGION( "assign_morton_codes_to_queries" ),
Kokkos::RangePolicy<ExecutionSpace>( 0, n_queries ),
KOKKOS_LAMBDA( int i ) {
Point xyz = Details::returnCentroid( queries( i )._geometry );
translateAndScale( xyz, xyz, scene_bounding_box );
morton_codes( i ) = morton3D( xyz[0], xyz[1], xyz[2] );
} );
void pointInCell( double threshold,
Kokkos::View<Coordinate **, DeviceType> physical_points,
Kokkos::View<Coordinate ***, DeviceType> cells,
Kokkos::View<int *, DeviceType> coarse_search_output_cells,
Kokkos::View<Coordinate **, DeviceType> reference_points,
Kokkos::View<bool *, DeviceType> point_in_cell )
{
using ExecutionSpace = typename DeviceType::execution_space;
int const n_ref_pts = reference_points.extent( 0 );
Functor::PointInCell<CellType, DeviceType> search_functor(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
Kokkos::parallel_for( DTK_MARK_REGION( "point_in_cell" ),
Kokkos::RangePolicy<ExecutionSpace>( 0, n_ref_pts ),
search_functor );
}
static void GEMM(Teuchos::ETransp transA, Teuchos::ETransp transB, Scalar alpha,
Kokkos::View<Scalar**,Kokkos::LayoutRight,Kokkos::DefaultExecutionSpace> A, Kokkos::View<Scalar**,Kokkos::LayoutRight,Kokkos::DefaultExecutionSpace> B,
Scalar beta, Kokkos::View<Scalar**,Kokkos::LayoutRight,Kokkos::DefaultExecutionSpace> C){
Teuchos::BLAS<int,Scalar>blas;
const int m = static_cast<int> (C.dimension_0 ()),
n = static_cast<int> (C.dimension_1 ()),
k = (transA == Teuchos::NO_TRANS ? A.dimension_1 () : A.dimension_0 ());
blas.GEMM(transB, transA, n, m, k, alpha,
B.ptr_on_device(), n,
A.ptr_on_device(), k,
beta, C.ptr_on_device(), n);
}
KOKKOS_INLINE_FUNCTION
void operator()( size_t i ) const
{
if ( i < m_elem_node.dimension_0() * m_elem_node.dimension_1() ) {
const size_t ielem = i / ElemNode ;
const size_t inode = i % ElemNode ;
unsigned elem_grid[SpaceDim] ;
unsigned node_grid[SpaceDim] ;
m_box_part.uses_elem_coord( ielem , elem_grid );
enum { elem_node_scale = Order == BoxElemPart::ElemLinear ? 1 :
Order == BoxElemPart::ElemQuadratic ? 2 : 0
};
node_grid[0] = elem_node_scale * elem_grid[0] + m_elem_node_local[inode][0] ;
node_grid[1] = elem_node_scale * elem_grid[1] + m_elem_node_local[inode][1] ;
node_grid[2] = elem_node_scale * elem_grid[2] + m_elem_node_local[inode][2] ;
m_elem_node(ielem,inode) = m_box_part.local_node_id( node_grid );
}
if ( i < m_node_grid.dimension_0() ) {
unsigned node_grid[SpaceDim] ;
m_box_part.local_node_coord( i , node_grid );
m_node_grid(i,0) = node_grid[0] ;
m_node_grid(i,1) = node_grid[1] ;
m_node_grid(i,2) = node_grid[2] ;
m_coord_map( node_grid[0] ,
node_grid[1] ,
node_grid[2] ,
m_node_coord(i,0) ,
m_node_coord(i,1) ,
m_node_coord(i,2) );
}
if ( i < m_recv_node.dimension_0() ) {
m_recv_node(i,0) = m_box_part.recv_node_rank(i);
m_recv_node(i,1) = m_box_part.recv_node_count(i);
}
if ( i < m_send_node.dimension_0() ) {
m_send_node(i,0) = m_box_part.send_node_rank(i);
m_send_node(i,1) = m_box_part.send_node_count(i);
}
if ( i < m_send_node_id.dimension_0() ) {
m_send_node_id(i) = m_box_part.send_node_id(i);
}
}
KOKKOS_INLINE_FUNCTION
void operator()( const typename policy_type::member_type ind , value_type & error ) const
{
if ( 0 == ind.league_rank() && 0 == ind.team_rank() ) {
const long int thread_count = ind.league_size() * ind.team_size();
total() = ( thread_count * ( thread_count + 1 ) ) / 2 ;
}
// Team max:
const int long m = ind.team_reduce( (long int) ( ind.league_rank() + ind.team_rank() ) , JoinMax() );
if ( m != ind.league_rank() + ( ind.team_size() - 1 ) ) {
#ifndef __KALMAR_ACCELERATOR__
printf("ScanTeamFunctor[%d.%d of %d.%d] reduce_max_answer(%ld) != reduce_max(%ld)\n"
, ind.league_rank(), ind.team_rank()
, ind.league_size(), ind.team_size()
, (long int)(ind.league_rank() + ( ind.team_size() - 1 )) , m );
#endif
}
// Scan:
const long int answer =
( ind.league_rank() + 1 ) * ind.team_rank() +
( ind.team_rank() * ( ind.team_rank() + 1 ) ) / 2 ;
const long int result =
ind.team_scan( ind.league_rank() + 1 + ind.team_rank() + 1 );
const long int result2 =
ind.team_scan( ind.league_rank() + 1 + ind.team_rank() + 1 );
if ( answer != result || answer != result2 ) {
#ifndef __KALMAR_ACCELERATOR__
printf("ScanTeamFunctor[%d.%d of %d.%d] answer(%ld) != scan_first(%ld) or scan_second(%ld)\n",
ind.league_rank(), ind.team_rank(),
ind.league_size(), ind.team_size(),
answer,result,result2);
#endif
error = 1 ;
}
const long int thread_rank = ind.team_rank() +
ind.team_size() * ind.league_rank();
ind.team_scan( 1 + thread_rank , accum.ptr_on_device() );
}
BoxElemFixture( const BoxElemPart::Decompose decompose ,
const unsigned global_size ,
const unsigned global_rank ,
const unsigned elem_nx ,
const unsigned elem_ny ,
const unsigned elem_nz ,
const float bubble_x = 1.1f ,
const float bubble_y = 1.2f ,
const float bubble_z = 1.3f )
: m_box_part( Order , decompose , global_size , global_rank , elem_nx , elem_ny , elem_nz )
, m_coord_map( m_box_part.global_coord_max(0) ,
m_box_part.global_coord_max(1) ,
m_box_part.global_coord_max(2) ,
bubble_x ,
bubble_y ,
bubble_z )
, m_node_coord( "fixture_node_coord" , m_box_part.uses_node_count() )
, m_node_grid( "fixture_node_grid" , m_box_part.uses_node_count() )
, m_elem_node( "fixture_elem_node" , m_box_part.uses_elem_count() )
, m_recv_node( "fixture_recv_node" , m_box_part.recv_node_msg_count() )
, m_send_node( "fixture_send_node" , m_box_part.send_node_msg_count() )
, m_send_node_id( "fixture_send_node_id" , m_box_part.send_node_id_count() )
{
{
const hex_data elem_data ;
for ( unsigned i = 0 ; i < ElemNode ; ++i ) {
m_elem_node_local[i][0] = elem_data.eval_map[i][0] ;
m_elem_node_local[i][1] = elem_data.eval_map[i][1] ;
m_elem_node_local[i][2] = elem_data.eval_map[i][2] ;
m_elem_node_local[i][3] = 0 ;
}
}
const size_t nwork =
std::max( m_recv_node.dimension_0() ,
std::max( m_send_node.dimension_0() ,
std::max( m_send_node_id.dimension_0() ,
std::max( m_node_grid.dimension_0() ,
m_elem_node.dimension_0() * m_elem_node.dimension_1() ))));
Kokkos::parallel_for( nwork , *this );
}
void modified_gram_schmidt(
const Kokkos::View< ScalarQ ** ,
Kokkos::LayoutLeft ,
DeviceType ,
Management > & Q ,
const Kokkos::View< ScalarR ** ,
Kokkos::LayoutLeft ,
DeviceType ,
Management > & R ,
comm::Machine machine )
{
const Kokkos::ALL ALL ;
typedef Kokkos::View< ScalarQ * ,
Kokkos::LayoutLeft ,
DeviceType ,
Kokkos::MemoryUnmanaged >
vector_view_type ;
const typename
Kokkos::View< ScalarR** ,
Kokkos::LayoutLeft ,
DeviceType >::
HostMirror hostR = Kokkos::create_mirror_view( R );
const int length = Q.dimension_0();
const int count = Q.dimension_1();
for ( int j = 0 ; j < count ; ++j ) {
const vector_view_type Qj = Kokkos::subview< vector_view_type >( Q , ALL , j );
// reads += length
// writes += 0
// flops += 1 + 2 * length
const double norm_Qj = Kokkos::norm2( length , Qj , machine );
hostR(j,j) = norm_Qj ;
// reads += length
// writes += length
// flops += 1 + length
Kokkos::scale( length , 1.0 / norm_Qj , Qj );
for ( int k = j + 1 ; k < count ; ++k ) {
const vector_view_type Qk = Kokkos::subview< vector_view_type >( Q , ALL , k );
// reads += 2 * length
// writes += 0
// flops += 2 * length
const double Qj_dot_Qk =
Kokkos::dot( length , Qj , Qk , machine );
hostR(j,k) = Qj_dot_Qk ;
// reads += 2 * length
// writes += length
// flops += 2 * length
Kokkos::axpy( length , - Qj_dot_Qk , Qj , Qk );
}
}
// reads += 0
// writes += count * count
Kokkos::deep_copy( R , hostR );
}
void PointInCell<DeviceType>::search(
Kokkos::View<Coordinate **, DeviceType> physical_points,
Kokkos::View<Coordinate ***, DeviceType> cells,
Kokkos::View<int *, DeviceType> coarse_search_output_cells,
DTK_CellTopology cell_topo,
Kokkos::View<Coordinate **, DeviceType> reference_points,
Kokkos::View<bool *, DeviceType> point_in_cell )
{
// Check the size of the Views
DTK_REQUIRE( reference_points.extent( 0 ) == point_in_cell.extent( 0 ) );
DTK_REQUIRE( reference_points.extent( 0 ) == physical_points.extent( 0 ) );
DTK_REQUIRE( reference_points.extent( 1 ) == physical_points.extent( 1 ) );
DTK_REQUIRE( reference_points.extent( 1 ) == cells.extent( 2 ) );
// Perform the point in cell search. We hide the template parameters used by
// Intrepid2, using the CellType template.
// Note that if the Newton solver does not converge, Intrepid2 will just
// return the last results and there is no way to know that the coordinates
// in the reference frames where not found.
switch ( cell_topo )
{
case DTK_HEX_8:
{
internal::pointInCell<HEX_8, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
case DTK_HEX_27:
{
internal::pointInCell<HEX_27, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
case DTK_PYRAMID_5:
{
internal::pointInCell<PYRAMID_5, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
case DTK_QUAD_4:
{
internal::pointInCell<QUAD_4, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
case DTK_QUAD_9:
{
internal::pointInCell<QUAD_9, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
case DTK_TET_4:
{
internal::pointInCell<TET_4, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
case DTK_TET_10:
{
internal::pointInCell<TET_10, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
case DTK_TRI_3:
{
internal::pointInCell<TRI_3, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
case DTK_TRI_6:
{
internal::pointInCell<TRI_6, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
case DTK_WEDGE_6:
{
internal::pointInCell<WEDGE_6, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
case DTK_WEDGE_18:
{
internal::pointInCell<WEDGE_18, DeviceType>(
threshold, physical_points, cells, coarse_search_output_cells,
reference_points, point_in_cell );
break;
}
default:
{
throw DataTransferKitNotImplementedException();
}
}
Kokkos::fence();
}
KOKKOS_INLINE_FUNCTION
unsigned node_count() const {
return m_node_grid.dimension_0();
}
KOKKOS_INLINE_FUNCTION
unsigned elem_count() const {
return m_elem_node.dimension_0();
}
KOKKOS_INLINE_FUNCTION
void operator()(const int cell) const
{
for(int i=0;i<Teuchos::as<int>(local_lids.dimension_1());i++)
local_lids(cell,i) = global_lids(cellIds(cell),i);
}
// The functor needs to define how much shared memory it requests given a team_size.
size_t team_shmem_size( int team_size ) const {
return shared_1d_int::shmem_size(data.dimension_1());
}
SortView( const Kokkos::View<ValueType*,ExecSpace> v , int begin , int end )
{
std::sort( v.ptr_on_device() + begin , v.ptr_on_device() + end );
}
SortView( const Kokkos::View<ValueType*,Kokkos::Cuda> v , int begin , int end )
{
thrust::sort( thrust::device_ptr<ValueType>( v.ptr_on_device() + begin )
, thrust::device_ptr<ValueType>( v.ptr_on_device() + end ) );
}
