50 Stokhos::create_product_tensor<Device>( *
setup.basis, *
setup.Cijk );
52 for (
int i=0; i<
setup.stoch_length; ++i) {
54 const int iEntryEnd = tensor.entry_end(i);
55 for (
int iEntry = iEntryBeg ; iEntry < iEntryEnd ; ++iEntry ) {
56 const int kj = tensor.coord( iEntry );
57 const int j = kj & 0x0ffff;
58 const int k = kj >> 16;
62 if (
j == k) c2 *= 2.0;
64 int ii =
setup.inv_perm[i];
65 int jj =
setup.inv_perm[
j];
66 int kk =
setup.inv_perm[k];
70 out <<
"(" << ii <<
"," << jj <<
"," << kk <<
"): " << c
71 <<
" == " << c2 <<
" failed!" << std::endl;
84 Teuchos::ParameterList params;
85 params.set(
"Tile Size",10);
86 params.set(
"Max Tiles",10000);
89 Stokhos::create_tiled_product_tensor<Device>( *
setup.basis, *
setup.Cijk,
97 for (
size_t tile = 0 ; tile < n_tile ; ++tile ) {
98 const size_t i_offset = tensor.offset(tile, 0);
99 const size_t j_offset = tensor.offset(tile, 1);
100 const size_t k_offset = tensor.offset(tile, 2);
101 const size_t n_row = tensor.num_rows(tile);
103 for (
size_t i=0; i<n_row; ++i) {
104 const size_t iEntryBeg = tensor.entry_begin(tile,i);
105 const size_t iEntryEnd = tensor.entry_end(tile,i);
106 for (
size_t iEntry = iEntryBeg ; iEntry < iEntryEnd ; ++iEntry ) {
107 const size_t j = tensor.coord(iEntry,0);
108 const size_t k = tensor.coord(iEntry,1);
110 int ii = i + i_offset;
111 int jj =
j + j_offset;
112 int kk = k + k_offset;
118 out <<
"(" << ii <<
"," << jj <<
"," << kk <<
"): " << c
119 <<
" == " << c2 <<
" failed!" << std::endl;
133 Teuchos::ParameterList params;
134 params.set(
"Tile Size",10);
137 Stokhos::create_simple_tiled_product_tensor<Device>(
142 for (
size_t i_tile = 0; i_tile<n_i_tile; ++i_tile) {
143 const size_t i_begin = tensor.i_begin(i_tile);
144 const size_t i_size = tensor.i_size(i_tile);
146 const size_t n_j_tile = tensor.num_j_tiles(i_tile);
147 for (
size_t j_tile = 0; j_tile<n_j_tile; ++j_tile) {
148 const size_t j_begin = tensor.j_begin(i_tile, j_tile);
151 const size_t n_k_tile = tensor.num_k_tiles(i_tile, j_tile);
152 for (
size_t k_tile = 0; k_tile<n_k_tile; ++k_tile) {
153 const size_t k_begin = tensor.k_begin(i_tile, j_tile, k_tile);
156 for (
size_t i=0; i<i_size; ++i) {
157 const size_t iEntryBeg = tensor.entry_begin(i_tile,j_tile,k_tile,i);
158 const size_t iEntryEnd = tensor.entry_end(i_tile,j_tile,k_tile,i);
159 for (
size_t iEntry = iEntryBeg ; iEntry < iEntryEnd ; ++iEntry ) {
160 const size_t j = tensor.coord(iEntry,0);
161 const size_t k = tensor.coord(iEntry,1);
163 int ii = i + i_begin;
164 int jj =
j + j_begin;
165 int kk = k + k_begin;
174 out <<
"(" << ii <<
"," << jj <<
"," << kk <<
"): " << c
175 <<
" == " << c2 <<
" failed!" << std::endl;
183 TEUCHOS_TEST_EQUALITY( num_entry,
setup.Cijk->num_entries(), out, success );
186 template <
typename Scalar,
typename Device,
bool Pack>
189 Teuchos::FancyOStream& out) {
196 Stokhos::create_coo_product_tensor<Device, Pack>(
199 const size_t nEntry = tensor.entry_count();
201 for (
size_t entry = 0 ; entry < nEntry ; ++entry ) {
202 tensor.coord(entry, i,
j, k);
204 if (
j == k) c2 *= 2.0;
208 out <<
"(" << i <<
"," <<
j <<
"," << k <<
"): " << c
209 <<
" == " << c2 <<
" failed!" << std::endl;
218 success = test_coo_product_tensor_cijk<Scalar,Device,true>(
setup, out);
222 success = test_coo_product_tensor_cijk<Scalar,Device,false>(
setup, out);
230 typedef size_t size_type;
233 Stokhos::create_flat_sparse_3_tensor<Device>( *
setup.basis, *
setup.Cijk );
235 for (
int i=0; i<
setup.stoch_length; ++i) {
236 const size_type nk = tensor.
num_k(i);
237 const size_type kBeg = tensor.k_begin(i);
238 const size_type kEnd = kBeg + nk;
239 for (size_type kEntry = kBeg; kEntry < kEnd; ++kEntry) {
240 const size_type k = tensor.k_coord(kEntry);
241 const size_type nj = tensor.num_j(kEntry);
242 const size_type jBeg = tensor.j_begin(kEntry);
243 const size_type jEnd = jBeg + nj;
244 for (size_type jEntry = jBeg; jEntry < jEnd; ++jEntry) {
245 const size_type
j = tensor.j_coord(jEntry);
247 if (
j == k) c2 *= 2.0;
250 out <<
"(" << i <<
"," <<
j <<
"," << k <<
"): " << c
251 <<
" == " << c2 <<
" failed!" << std::endl;
264 typedef size_t size_type;
267 Stokhos::create_flat_sparse_3_tensor_kji<Device>(*
setup.basis, *
setup.Cijk);
268 const size_type nk = tensor.
num_k();
270 for ( size_type k = 0; k < nk; ++k) {
271 const size_type nj = tensor.num_j(k);
272 const size_type jBeg = tensor.j_begin(k);
273 const size_type jEnd = jBeg + nj;
274 for (size_type jEntry = jBeg; jEntry < jEnd; ++jEntry) {
275 const size_type
j = tensor.j_coord(jEntry);
276 const size_type ni = tensor.num_i(jEntry);
277 const size_type iBeg = tensor.i_begin(jEntry);
278 const size_type iEnd = iBeg + ni;
279 for (size_type iEntry = iBeg; iEntry < iEnd; ++iEntry) {
280 const size_type i = tensor.i_coord(iEntry);
282 if (
j == k) c2 *= 2.0;
285 out <<
"(" << i <<
"," <<
j <<
"," << k <<
"): " << c
286 <<
" == " << c2 <<
" failed!" << std::endl;
294 #define UNIT_TEST_GROUP_SCALAR_HOST_DEVICE( SCALAR, DEVICE ) \ 295 TEUCHOS_UNIT_TEST_TEMPLATE_2_INSTANT( Kokkos_SG_SpMv, CrsProductTensorCijk, SCALAR, DEVICE ) \ 296 TEUCHOS_UNIT_TEST_TEMPLATE_2_INSTANT( Kokkos_SG_SpMv, TiledCrsProductTensorCijk, SCALAR, DEVICE ) \ 297 TEUCHOS_UNIT_TEST_TEMPLATE_2_INSTANT( Kokkos_SG_SpMv, SimpleTiledCrsProductTensorCijk, SCALAR, DEVICE ) \ 298 TEUCHOS_UNIT_TEST_TEMPLATE_2_INSTANT( Kokkos_SG_SpMv, CooProductTensorCijk_Packed, SCALAR, DEVICE ) \ 299 TEUCHOS_UNIT_TEST_TEMPLATE_2_INSTANT( Kokkos_SG_SpMv, CooProductTensorCijk_Unpacked, SCALAR, DEVICE ) \ 300 TEUCHOS_UNIT_TEST_TEMPLATE_2_INSTANT( Kokkos_SG_SpMv, FlatSparseCijk, SCALAR, DEVICE ) \ 301 TEUCHOS_UNIT_TEST_TEMPLATE_2_INSTANT( Kokkos_SG_SpMv, FlatSparseCijk_kji, SCALAR, DEVICE ) KOKKOS_INLINE_FUNCTION size_type num_k(size_type i) const
Number of k entries with a coordinate 'i'.
bool test_coo_product_tensor_cijk(const KokkosKernelsUnitTest::UnitTestSetup< Device > &setup, Teuchos::FancyOStream &out)
KOKKOS_INLINE_FUNCTION size_type num_i_tiles() const
Number i-tiles.
KOKKOS_INLINE_FUNCTION size_type num_tiles() const
Number tiles.
Sparse product tensor with replicated entries to provide subsets with a given coordinate.
Sparse product tensor with replicated entries to provide subsets with a given coordinate.
TEUCHOS_UNIT_TEST_TEMPLATE_2_DECL(Kokkos_SG_SpMv, CrsProductTensorCijk, Scalar, Device)
UnitTestSetup< int, double > setup
KOKKOS_INLINE_FUNCTION PCE< Storage > abs(const PCE< Storage > &a)
KOKKOS_INLINE_FUNCTION size_type entry_begin(size_type i) const
Begin entries with a coordinate 'i'.
Sparse product tensor with replicated entries to provide subsets with a given coordinate.
Sparse product tensor using 'COO'-like storage format.
KOKKOS_INLINE_FUNCTION size_type num_k() const
Number of k entries.