diff --git a/master/annotated.html b/master/annotated.html index 810d95cfce..6ea3f91223 100644 --- a/master/annotated.html +++ b/master/annotated.html @@ -257,9 +257,10 @@  CMemoryView  ►Nmultiplication  ►Ninternal - CGeneralSub - CHermitian - CTriangular + CGeneral + CGeneralSub + CHermitian + CTriangular  ►Npermutations  ►Ninternal  CPermutations diff --git a/master/classes.html b/master/classes.html index f606daa7b5..819bed646d 100644 --- a/master/classes.html +++ b/master/classes.html @@ -87,7 +87,7 @@
FormatShort (dlaf::internal)
G
-
gemmSizes (dlaf::tile::internal)
GenEigensolver (dlaf::eigensolver::internal)
GeneralSub (dlaf::multiplication::internal)
GenToStd (dlaf::eigensolver::internal)
getter_random (dlaf::matrix::util::internal)
getter_random< std::complex< T > > (dlaf::matrix::util::internal)
GivensRotation (dlaf::eigensolver::internal)
+
gemmSizes (dlaf::tile::internal)
GenEigensolver (dlaf::eigensolver::internal)
General (dlaf::multiplication::internal)
GeneralSub (dlaf::multiplication::internal)
GenToStd (dlaf::eigensolver::internal)
getter_random (dlaf::matrix::util::internal)
getter_random< std::complex< T > > (dlaf::matrix::util::internal)
GivensRotation (dlaf::eigensolver::internal)
H
Helpers (dlaf::eigensolver::internal::bt_red_band)
Helpers (dlaf::factorization::internal::tfactor_l)
Helpers< Backend::GPU > (dlaf::eigensolver::internal::bt_red_band)
Helpers< Backend::GPU, Device::GPU, T > (dlaf::factorization::internal::tfactor_l)
Helpers< Backend::MC > (dlaf::eigensolver::internal::bt_red_band)
Helpers< Backend::MC, Device::CPU, T > (dlaf::factorization::internal::tfactor_l)
hemmSizes (dlaf::tile::internal)
her2kSizes (dlaf::tile::internal)
herkSizes (dlaf::tile::internal)
Hermitian (dlaf::multiplication::internal)
HHManager (dlaf::eigensolver::internal::bt_tridiag)
HHManager< Backend::GPU, Device::GPU, T > (dlaf::eigensolver::internal::bt_tridiag)
HHManager< Backend::MC, Device::CPU, T > (dlaf::eigensolver::internal::bt_tridiag)
diff --git a/master/hierarchy.html b/master/hierarchy.html index 69b23f0270..dc1419f580 100644 --- a/master/hierarchy.html +++ b/master/hierarchy.html @@ -153,148 +153,149 @@  Cdlaf::internal::FormatShort< T >  Cdlaf::tile::internal::gemmSizes  Cdlaf::eigensolver::internal::GenEigensolver< backend, device, T > - Cdlaf::multiplication::internal::GeneralSub< B, D, T > - Cdlaf::eigensolver::internal::GenToStd< backend, device, T > - ►Cdlaf::matrix::util::internal::getter_random< T >Callable that returns random values in the range [-1, 1] - Cdlaf::matrix::util::internal::getter_random< std::complex< T > >Callable that returns random complex numbers whose absolute values are less than 1 - Cdlaf::eigensolver::internal::GivensRotation< T > - Cdlaf::eigensolver::internal::bt_red_band::Helpers< B > - Cdlaf::factorization::internal::tfactor_l::Helpers< backend, device, T > - Cdlaf::eigensolver::internal::bt_red_band::Helpers< Backend::GPU > - Cdlaf::factorization::internal::tfactor_l::Helpers< Backend::GPU, Device::GPU, T > - Cdlaf::eigensolver::internal::bt_red_band::Helpers< Backend::MC > - Cdlaf::factorization::internal::tfactor_l::Helpers< Backend::MC, Device::CPU, T > - Cdlaf::tile::internal::hemmSizes - Cdlaf::tile::internal::her2kSizes - Cdlaf::tile::internal::herkSizes - Cdlaf::multiplication::internal::Hermitian< B, D, T > - Cdlaf::eigensolver::internal::bt_tridiag::HHManager< B, D, T > - Cdlaf::eigensolver::internal::bt_tridiag::HHManager< Backend::GPU, Device::GPU, T > - Cdlaf::eigensolver::internal::bt_tridiag::HHManager< Backend::MC, Device::CPU, T > - ►Cstd::integral_constant - Cdlaf::internal::IsFalse< T > - Cdlaf::common::is_data< Data > - ►Cstd::is_floating_point - ►Cdlaf::internal::IsFloatingPointOrComplex< T > - Cdlaf::internal::IsFloatingPointOrComplex< std::complex< T > > - Cdlaf::common::IterableRange2D< IndexT, Tag >An Iterable representing a 2D range - Cdlaf::common::IteratorRange2D< IndexT, Tag >An Iterator returning indices in column-major order - Cdlaf::matrix::LayoutInfo - Cdlaf::matrix::Matrix< T, D > - Cdlaf::matrix::Matrix< const T, Source > - Cdlaf::matrix::Matrix< const T, SourceTarget > - Cdlaf::matrix::Matrix< T, Source > - Cdlaf::matrix::Matrix< T, SourceTarget > - Cdlaf::matrix::Matrix< T, Target > - ►Cdlaf::matrix::internal::MatrixBase - Cdlaf::matrix::Matrix< const T, D > - Cdlaf::matrix::internal::MatrixRef< const T, D > - Cdlaf::matrix::MatrixMirror< T, Target, Source > - ►Cdlaf::matrix::MatrixMirror< const T, SourceTarget, SourceTarget > - Cdlaf::matrix::MatrixMirror< T, SourceTarget, SourceTarget > - Cdlaf::matrix::MatrixMirror< const T, Target, Source > - Cdlaf::matrix::internal::MatrixRef< T, D > - Cdlaf::memory::MemoryChunk< T, D >The class MemoryChunk represents a layer of abstraction over the underlying device memory - Cdlaf::memory::MemoryView< T, D > - Cdlaf::memory::MemoryView< T, Device::CPU > - Cdlaf::comm::Message< Data > - Cdlaf::internal::moveNonConstTile< T > - ►Cdlaf::comm::mpi_datatype< T >Mapper between language types and basic MPI_Datatype - Cdlaf::comm::mpi_datatype< const T >Helper for mapping also const types - Cdlaf::comm::mpi_init - Cdlaf::comm::internal::MPICallHelper< F > - Cdlaf::auxiliary::internal::Norm< backend, device, T > - Cdlaf::auxiliary::internal::Norm< Backend::MC, Device::CPU, T > - Cdlaf::format::numpy - Cdlaf::matrix::internal::numpy_datatype< T > - Cdlaf::matrix::internal::numpy_datatype< std::complex< T > > - Cdlaf::matrix::Panel< axis, T, D, Storage > - Cdlaf::matrix::Panel< axis, const T, D, StoreTransposed::No > - ►Cdlaf::matrix::Panel< orthogonal(axis), const T, D, StoreTransposed::No > - Cdlaf::matrix::Panel< axis, const T, D, StoreTransposed::Yes > - ►Cdlaf::internal::PartialTransformBase< Tag, B, F > - Cdlaf::internal::PartialTransform< Tag, B, F > - Cdlaf::internal::PartialTransformDetach< Tag, B, F > - ►Cdlaf::comm::internal::PartialTransformMPIBase< F > - Cdlaf::comm::internal::PartialTransformMPI< F > - Cdlaf::comm::internal::PartialTransformMPIDetach< F > - Cdlaf::permutations::internal::Permutations< B, D, T, coord > - Cdlaf::common::Pipeline< T > - Cdlaf::internal::Policy< B > - Cdlaf::internal::PrependPack< Pack, T > - Cdlaf::internal::PrependPack< Pack< Ts... >, T > - Cdlaf::factorization::internal::QR< backend, device, T > - Cdlaf::factorization::internal::QR_Tfactor< backend, device, T > - Cdlaf::eigensolver::internal::ReductionToBand< B, D, T > - Cdlaf::common::RoundRobin< T > - Cdlaf::common::RoundRobin< dlaf::matrix::Panel< Coord::Col, T, Device::CPU > > - Cdlaf::ScopedInitializer - Cdlaf::eigensolver::internal::ScopedSenderWait - Cdlaf::internal::SenderSingleValueTypeImpl< ValueTypes > - Cdlaf::internal::SenderSingleValueTypeImpl< TypeList< TypeList< pika::execution::experimental::async_rw_mutex_access_wrapper< RWType, RType, pika::execution::experimental::async_rw_mutex_access_type::read > > > > - Cdlaf::internal::SenderSingleValueTypeImpl< TypeList< TypeList< pika::execution::experimental::async_rw_mutex_access_wrapper< RWType, RType, pika::execution::experimental::async_rw_mutex_access_type::readwrite > > > > - Cdlaf::internal::SenderSingleValueTypeImpl< TypeList< TypeList< std::reference_wrapper< T > > > > - Cdlaf::internal::SenderSingleValueTypeImpl< TypeList< TypeList< T > > > - Cdlaf::common::internal::SingleThreadedBlasScope - Cdlaf::common::internal::source_location - Cdlaf::matrix::SubDistributionSpecContains information to create a sub-distribution - Cdlaf::matrix::Matrix< const T, D >::SubPipelineTag - Cdlaf::matrix::SubTileSpecContains the information to create a subtile - ►Cdlaf::eigensolver::internal::SweepWorker< T > - Cdlaf::eigensolver::internal::SweepWorkerDist< T > - Cdlaf::matrix::Tile< T, D > - Cdlaf::matrix::Tile< const T, D > - Cdlaf::eigensolver::internal::bt_tridiag::TileAccessHelper - Cdlaf::eigensolver::internal::TileCollector - Cdlaf::matrix::internal::TileData< T, D > - Cdlaf::matrix::internal::TilePipeline< T, D > - Cdlaf::common::Timer< clock > - Cdlaf::internal::TransformPack< Pack, Transformer > - Cdlaf::internal::TransformPack< Pack< Ts... >, Transformer > - Cdlaf::multiplication::internal::Triangular< backend, device, T > - Cdlaf::solver::internal::Triangular< backend, device, T > - Cdlaf::eigensolver::internal::TridiagResult< T, D > - Cdlaf::eigensolver::internal::TridiagSolver< backend, device, T > - Cdlaf::tile::internal::trmmSizes - Cdlaf::tile::internal::trsmSizes - ►Cstd::true_type - Cdlaf::common::internal::is_coord< Index2D< T, Tag > > - Cdlaf::common::internal::is_coord< Size2D< T, Tag > > - Cdlaf::internal::Contains< T, T, Ts... > - Cdlaf::TuneParameters - ►Cstd::conditional::type - Cdlaf::internal::UniqueHelper< Pack< Ts... >, Pack< U, Us... > > + Cdlaf::multiplication::internal::General< B, D, T > + Cdlaf::multiplication::internal::GeneralSub< B, D, T > + Cdlaf::eigensolver::internal::GenToStd< backend, device, T > + ►Cdlaf::matrix::util::internal::getter_random< T >Callable that returns random values in the range [-1, 1] + Cdlaf::matrix::util::internal::getter_random< std::complex< T > >Callable that returns random complex numbers whose absolute values are less than 1 + Cdlaf::eigensolver::internal::GivensRotation< T > + Cdlaf::eigensolver::internal::bt_red_band::Helpers< B > + Cdlaf::factorization::internal::tfactor_l::Helpers< backend, device, T > + Cdlaf::eigensolver::internal::bt_red_band::Helpers< Backend::GPU > + Cdlaf::factorization::internal::tfactor_l::Helpers< Backend::GPU, Device::GPU, T > + Cdlaf::eigensolver::internal::bt_red_band::Helpers< Backend::MC > + Cdlaf::factorization::internal::tfactor_l::Helpers< Backend::MC, Device::CPU, T > + Cdlaf::tile::internal::hemmSizes + Cdlaf::tile::internal::her2kSizes + Cdlaf::tile::internal::herkSizes + Cdlaf::multiplication::internal::Hermitian< B, D, T > + Cdlaf::eigensolver::internal::bt_tridiag::HHManager< B, D, T > + Cdlaf::eigensolver::internal::bt_tridiag::HHManager< Backend::GPU, Device::GPU, T > + Cdlaf::eigensolver::internal::bt_tridiag::HHManager< Backend::MC, Device::CPU, T > + ►Cstd::integral_constant + Cdlaf::internal::IsFalse< T > + Cdlaf::common::is_data< Data > + ►Cstd::is_floating_point + ►Cdlaf::internal::IsFloatingPointOrComplex< T > + Cdlaf::internal::IsFloatingPointOrComplex< std::complex< T > > + Cdlaf::common::IterableRange2D< IndexT, Tag >An Iterable representing a 2D range + Cdlaf::common::IteratorRange2D< IndexT, Tag >An Iterator returning indices in column-major order + Cdlaf::matrix::LayoutInfo + Cdlaf::matrix::Matrix< T, D > + Cdlaf::matrix::Matrix< const T, Source > + Cdlaf::matrix::Matrix< const T, SourceTarget > + Cdlaf::matrix::Matrix< T, Source > + Cdlaf::matrix::Matrix< T, SourceTarget > + Cdlaf::matrix::Matrix< T, Target > + ►Cdlaf::matrix::internal::MatrixBase + Cdlaf::matrix::Matrix< const T, D > + Cdlaf::matrix::internal::MatrixRef< const T, D > + Cdlaf::matrix::MatrixMirror< T, Target, Source > + ►Cdlaf::matrix::MatrixMirror< const T, SourceTarget, SourceTarget > + Cdlaf::matrix::MatrixMirror< T, SourceTarget, SourceTarget > + Cdlaf::matrix::MatrixMirror< const T, Target, Source > + Cdlaf::matrix::internal::MatrixRef< T, D > + Cdlaf::memory::MemoryChunk< T, D >The class MemoryChunk represents a layer of abstraction over the underlying device memory + Cdlaf::memory::MemoryView< T, D > + Cdlaf::memory::MemoryView< T, Device::CPU > + Cdlaf::comm::Message< Data > + Cdlaf::internal::moveNonConstTile< T > + ►Cdlaf::comm::mpi_datatype< T >Mapper between language types and basic MPI_Datatype + Cdlaf::comm::mpi_datatype< const T >Helper for mapping also const types + Cdlaf::comm::mpi_init + Cdlaf::comm::internal::MPICallHelper< F > + Cdlaf::auxiliary::internal::Norm< backend, device, T > + Cdlaf::auxiliary::internal::Norm< Backend::MC, Device::CPU, T > + Cdlaf::format::numpy + Cdlaf::matrix::internal::numpy_datatype< T > + Cdlaf::matrix::internal::numpy_datatype< std::complex< T > > + Cdlaf::matrix::Panel< axis, T, D, Storage > + Cdlaf::matrix::Panel< axis, const T, D, StoreTransposed::No > + ►Cdlaf::matrix::Panel< orthogonal(axis), const T, D, StoreTransposed::No > + Cdlaf::matrix::Panel< axis, const T, D, StoreTransposed::Yes > + ►Cdlaf::internal::PartialTransformBase< Tag, B, F > + Cdlaf::internal::PartialTransform< Tag, B, F > + Cdlaf::internal::PartialTransformDetach< Tag, B, F > + ►Cdlaf::comm::internal::PartialTransformMPIBase< F > + Cdlaf::comm::internal::PartialTransformMPI< F > + Cdlaf::comm::internal::PartialTransformMPIDetach< F > + Cdlaf::permutations::internal::Permutations< B, D, T, coord > + Cdlaf::common::Pipeline< T > + Cdlaf::internal::Policy< B > + Cdlaf::internal::PrependPack< Pack, T > + Cdlaf::internal::PrependPack< Pack< Ts... >, T > + Cdlaf::factorization::internal::QR< backend, device, T > + Cdlaf::factorization::internal::QR_Tfactor< backend, device, T > + Cdlaf::eigensolver::internal::ReductionToBand< B, D, T > + Cdlaf::common::RoundRobin< T > + Cdlaf::common::RoundRobin< dlaf::matrix::Panel< Coord::Col, T, Device::CPU > > + Cdlaf::ScopedInitializer + Cdlaf::eigensolver::internal::ScopedSenderWait + Cdlaf::internal::SenderSingleValueTypeImpl< ValueTypes > + Cdlaf::internal::SenderSingleValueTypeImpl< TypeList< TypeList< pika::execution::experimental::async_rw_mutex_access_wrapper< RWType, RType, pika::execution::experimental::async_rw_mutex_access_type::read > > > > + Cdlaf::internal::SenderSingleValueTypeImpl< TypeList< TypeList< pika::execution::experimental::async_rw_mutex_access_wrapper< RWType, RType, pika::execution::experimental::async_rw_mutex_access_type::readwrite > > > > + Cdlaf::internal::SenderSingleValueTypeImpl< TypeList< TypeList< std::reference_wrapper< T > > > > + Cdlaf::internal::SenderSingleValueTypeImpl< TypeList< TypeList< T > > > + Cdlaf::common::internal::SingleThreadedBlasScope + Cdlaf::common::internal::source_location + Cdlaf::matrix::SubDistributionSpecContains information to create a sub-distribution + Cdlaf::matrix::Matrix< const T, D >::SubPipelineTag + Cdlaf::matrix::SubTileSpecContains the information to create a subtile + ►Cdlaf::eigensolver::internal::SweepWorker< T > + Cdlaf::eigensolver::internal::SweepWorkerDist< T > + Cdlaf::matrix::Tile< T, D > + Cdlaf::matrix::Tile< const T, D > + Cdlaf::eigensolver::internal::bt_tridiag::TileAccessHelper + Cdlaf::eigensolver::internal::TileCollector + Cdlaf::matrix::internal::TileData< T, D > + Cdlaf::matrix::internal::TilePipeline< T, D > + Cdlaf::common::Timer< clock > + Cdlaf::internal::TransformPack< Pack, Transformer > + Cdlaf::internal::TransformPack< Pack< Ts... >, Transformer > + Cdlaf::multiplication::internal::Triangular< backend, device, T > + Cdlaf::solver::internal::Triangular< backend, device, T > + Cdlaf::eigensolver::internal::TridiagResult< T, D > + Cdlaf::eigensolver::internal::TridiagSolver< backend, device, T > + Cdlaf::tile::internal::trmmSizes + Cdlaf::tile::internal::trsmSizes + ►Cstd::true_type + Cdlaf::common::internal::is_coord< Index2D< T, Tag > > + Cdlaf::common::internal::is_coord< Size2D< T, Tag > > + Cdlaf::internal::Contains< T, T, Ts... > + Cdlaf::TuneParameters  ►Cstd::conditional::type - Cdlaf::internal::UniquePackHelper< Pack< Ts... >, Pack< U, Us... > > - Cdlaf::comm::internal::type_handler< T > - ►Cdlaf::TypeInfo< T > - Cdlaf::TypeInfo< const T > - Cdlaf::TypeInfo< std::complex< T > > - Cdlaf::internal::TypeList<... > - Cdlaf::internal::Unique< Pack > - Cdlaf::internal::UniqueHelper< PackUnique, PackRest > - Cdlaf::internal::UniqueHelper< Pack< Ts... >, Pack<> > - ►Cdlaf::internal::UniqueHelper< Pack<>, Pack< Ts... > > - Cdlaf::internal::Unique< Pack< Ts... > > - Cdlaf::internal::UniquePack< Pack > - Cdlaf::internal::UniquePackHelper< PackUnique, PackRest > - Cdlaf::internal::UniquePackHelper< Pack< Ts... >, Pack<> > - ►Cdlaf::internal::UniquePackHelper< Pack<>, Pack< Ts... > > - Cdlaf::internal::UniquePack< Pack< Ts... > > - Cdlaf::common::internal::Unwrapper< T > - Cdlaf::common::internal::Unwrapper< pika::execution::experimental::async_rw_mutex_access_wrapper< T1, T2, at > > - Cdlaf::common::internal::Unwrapper< std::reference_wrapper< T > > - Cdlaf::common::internal::Unwrapping< F > - Cdlaf::eigensolver::internal::VAccessHelper - ►Cstd::vector - Cdlaf::common::internal::vector< ReadOnlySenderType > - Cdlaf::common::internal::vector< T > - ►Cdlaf::matrix::internal::View - Cdlaf::matrix::SubMatrixView - Cdlaf::matrix::SubPanelView - Cdlaf::eigensolver::internal::WorkSpace< T, D > - Cdlaf::eigensolver::internal::WorkSpaceHost< T > - Cdlaf::eigensolver::internal::WorkSpaceHostMirror< T, D > + Cdlaf::internal::UniqueHelper< Pack< Ts... >, Pack< U, Us... > > + ►Cstd::conditional::type + Cdlaf::internal::UniquePackHelper< Pack< Ts... >, Pack< U, Us... > > + Cdlaf::comm::internal::type_handler< T > + ►Cdlaf::TypeInfo< T > + Cdlaf::TypeInfo< const T > + Cdlaf::TypeInfo< std::complex< T > > + Cdlaf::internal::TypeList<... > + Cdlaf::internal::Unique< Pack > + Cdlaf::internal::UniqueHelper< PackUnique, PackRest > + Cdlaf::internal::UniqueHelper< Pack< Ts... >, Pack<> > + ►Cdlaf::internal::UniqueHelper< Pack<>, Pack< Ts... > > + Cdlaf::internal::Unique< Pack< Ts... > > + Cdlaf::internal::UniquePack< Pack > + Cdlaf::internal::UniquePackHelper< PackUnique, PackRest > + Cdlaf::internal::UniquePackHelper< Pack< Ts... >, Pack<> > + ►Cdlaf::internal::UniquePackHelper< Pack<>, Pack< Ts... > > + Cdlaf::internal::UniquePack< Pack< Ts... > > + Cdlaf::common::internal::Unwrapper< T > + Cdlaf::common::internal::Unwrapper< pika::execution::experimental::async_rw_mutex_access_wrapper< T1, T2, at > > + Cdlaf::common::internal::Unwrapper< std::reference_wrapper< T > > + Cdlaf::common::internal::Unwrapping< F > + Cdlaf::eigensolver::internal::VAccessHelper + ►Cstd::vector + Cdlaf::common::internal::vector< ReadOnlySenderType > + Cdlaf::common::internal::vector< T > + ►Cdlaf::matrix::internal::View + Cdlaf::matrix::SubMatrixView + Cdlaf::matrix::SubPanelView + Cdlaf::eigensolver::internal::WorkSpace< T, D > + Cdlaf::eigensolver::internal::WorkSpaceHost< T > + Cdlaf::eigensolver::internal::WorkSpaceHostMirror< T, D > diff --git a/master/matrix__base_8h_source.html b/master/matrix__base_8h_source.html index 8b1de2b19c..3f394467a3 100644 --- a/master/matrix__base_8h_source.html +++ b/master/matrix__base_8h_source.html @@ -223,17 +223,19 @@
168  << ", tile_size=" << matrix.tile_size()
169  << ", tiles_grid=" << matrix.nr_tiles()
170  << ", rank_index=" << matrix.rank_index()
-
171  << ", comm_grid=" << matrix.grid_size();
-
172  // clang-format on
-
173  }
-
174 
-
175 private:
-
176  Distribution distribution_;
-
177 };
-
178 
-
179 }
-
180 }
+
171  << ", comm_grid=" << matrix.grid_size()
+
172  << ", src_rank=" << matrix.distribution().source_rank_index()
+
173  << ", offset=" << matrix.distribution().offset();
+
174  // clang-format on
+
175  }
+
176 
+
177 private:
+
178  Distribution distribution_;
+
179 };
+
180 
181 }
+
182 }
+
183 }
dlaf::common::Index2D< IndexT_MPI, TAG_MPI >
dlaf::common::Size2D< SizeType, matrix::LocalTile_TAG >
dlaf::matrix::Distribution
Definition: distribution.h:111
diff --git a/master/multiplication_2general_2api_8h_source.html b/master/multiplication_2general_2api_8h_source.html index dbbf98b32e..9340329d79 100644 --- a/master/multiplication_2general_2api_8h_source.html +++ b/master/multiplication_2general_2api_8h_source.html @@ -85,44 +85,55 @@
14 
15 #include <dlaf/common/pipeline.h>
16 #include <dlaf/matrix/matrix.h>
-
17 #include <dlaf/types.h>
-
18 
-
19 namespace dlaf::multiplication {
-
20 namespace internal {
-
21 
-
22 template <Backend B, Device D, class T>
-
23 struct GeneralSub {
-
24  static void callNN(const SizeType i_tile_from, const SizeType i_tile_to, const blas::Op opA,
-
25  const blas::Op opB, const T alpha, Matrix<const T, D>& mat_a,
-
26  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c);
-
27  static void callNN(common::Pipeline<comm::Communicator>& row_task_chain,
-
28  common::Pipeline<comm::Communicator>& col_task_chain, const SizeType i_tile_from,
-
29  const SizeType i_tile_to, const T alpha, Matrix<const T, D>& mat_a,
-
30  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c);
-
31 };
-
32 
-
33 // ETI
-
34 #define DLAF_MULTIPLICATION_GENERAL_ETI(KWORD, BACKEND, DEVICE, DATATYPE) \
-
35  KWORD template struct GeneralSub<BACKEND, DEVICE, DATATYPE>;
-
36 
-
37 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::MC, Device::CPU, float)
-
38 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::MC, Device::CPU, double)
-
39 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::MC, Device::CPU, std::complex<float>)
-
40 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::MC, Device::CPU, std::complex<double>)
-
41 
-
42 #ifdef DLAF_WITH_GPU
-
43 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::GPU, Device::GPU, float)
-
44 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::GPU, Device::GPU, double)
-
45 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::GPU, Device::GPU, std::complex<float>)
-
46 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::GPU, Device::GPU, std::complex<double>)
-
47 #endif
-
48 
-
49 }
-
50 }
+
17 #include <dlaf/matrix/matrix_ref.h>
+
18 #include <dlaf/types.h>
+
19 
+
20 namespace dlaf::multiplication {
+
21 namespace internal {
+
22 using dlaf::matrix::internal::MatrixRef;
+
23 
+
24 template <Backend B, Device D, class T>
+
25 struct General {
+
26  static void callNN(const T alpha, MatrixRef<const T, D>& mat_a, MatrixRef<const T, D>& mat_b,
+
27  const T beta, MatrixRef<T, D>& mat_c);
+
28 };
+
29 
+
30 template <Backend B, Device D, class T>
+
31 struct GeneralSub {
+
32  static void callNN(const SizeType i_tile_from, const SizeType i_tile_to, const blas::Op opA,
+
33  const blas::Op opB, const T alpha, Matrix<const T, D>& mat_a,
+
34  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c);
+
35  static void callNN(common::Pipeline<comm::Communicator>& row_task_chain,
+
36  common::Pipeline<comm::Communicator>& col_task_chain, const SizeType i_tile_from,
+
37  const SizeType i_tile_to, const T alpha, Matrix<const T, D>& mat_a,
+
38  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c);
+
39 };
+
40 
+
41 #define DLAF_MULTIPLICATION_GENERAL_ETI(KWORD, BACKEND, DEVICE, DATATYPE) \
+
42  KWORD template struct General<BACKEND, DEVICE, DATATYPE>; \
+
43  KWORD template struct GeneralSub<BACKEND, DEVICE, DATATYPE>;
+
44 
+
45 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::MC, Device::CPU, float)
+
46 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::MC, Device::CPU, double)
+
47 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::MC, Device::CPU, std::complex<float>)
+
48 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::MC, Device::CPU, std::complex<double>)
+
49 
+
50 #ifdef DLAF_WITH_GPU
+
51 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::GPU, Device::GPU, float)
+
52 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::GPU, Device::GPU, double)
+
53 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::GPU, Device::GPU, std::complex<float>)
+
54 DLAF_MULTIPLICATION_GENERAL_ETI(extern, Backend::GPU, Device::GPU, std::complex<double>)
+
55 #endif
+
56 
+
57 }
+
58 }
dlaf::common::Pipeline
Definition: pipeline.h:31
+
dlaf::matrix::internal::MatrixRef
Definition: matrix_ref.h:108
matrix.h
+
matrix_ref.h
pipeline.h
-
dlaf::multiplication::internal::GeneralSub
Definition: api.h:23
+
dlaf::multiplication::internal::General
Definition: api.h:25
+
dlaf::multiplication::internal::GeneralSub
Definition: api.h:31
types.h
diff --git a/master/multiplication_2general_2impl_8h_source.html b/master/multiplication_2general_2impl_8h_source.html index 6589daf26c..ae55ee4c7f 100644 --- a/master/multiplication_2general_2impl_8h_source.html +++ b/master/multiplication_2general_2impl_8h_source.html @@ -82,164 +82,198 @@
11 #pragma once
12 
13 #include <dlaf/blas/tile.h>
-
14 #include <dlaf/common/assert.h>
-
15 #include <dlaf/common/index2d.h>
-
16 #include <dlaf/common/pipeline.h>
-
17 #include <dlaf/common/round_robin.h>
-
18 #include <dlaf/communication/broadcast_panel.h>
-
19 #include <dlaf/communication/communicator_grid.h>
-
20 #include <dlaf/matrix/distribution.h>
-
21 #include <dlaf/matrix/index.h>
-
22 #include <dlaf/matrix/panel.h>
-
23 #include <dlaf/multiplication/general/api.h>
-
24 #include <dlaf/sender/when_all_lift.h>
-
25 
-
26 namespace dlaf::multiplication {
-
27 namespace internal {
-
28 
-
29 template <Backend B, Device D, class T>
-
30 void GeneralSub<B, D, T>::callNN(const SizeType idx_begin, const SizeType idx_end, const blas::Op opA,
-
31  const blas::Op opB, const T alpha, Matrix<const T, D>& mat_a,
-
32  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c) {
-
33  namespace ex = pika::execution::experimental;
-
34 
-
35  for (SizeType j = idx_begin; j < idx_end; ++j) {
-
36  for (SizeType i = idx_begin; i < idx_end; ++i) {
-
37  for (SizeType k = idx_begin; k < idx_end; ++k) {
-
38  ex::start_detached(
-
39  dlaf::internal::whenAllLift(opA, opB, alpha, mat_a.read(GlobalTileIndex(i, k)),
-
40  mat_b.read(GlobalTileIndex(k, j)), k == idx_begin ? beta : T(1),
-
41  mat_c.readwrite(GlobalTileIndex(i, j))) |
-
42  tile::gemm(dlaf::internal::Policy<B>()));
-
43  }
-
44  }
-
45  }
-
46 }
-
47 
-
48 // This implementation is based on
-
49 //
-
50 // Van De Geijn, Robert A., and Jerrell Watts.
-
51 // SUMMA: Scalable universal matrix multiplication algorithm.
-
52 // Concurrency: Practice and Experience 9.4 (1997): 255-274
-
53 template <Backend B, Device D, class T>
-
54 void GeneralSub<B, D, T>::callNN(common::Pipeline<comm::Communicator>& row_task_chain,
-
55  common::Pipeline<comm::Communicator>& col_task_chain,
-
56  const SizeType idx_begin, const SizeType idx_end, const T alpha,
-
57  Matrix<const T, D>& mat_a, Matrix<const T, D>& mat_b, const T beta,
-
58  Matrix<T, D>& mat_c) {
-
59  namespace ex = pika::execution::experimental;
-
60 
-
61  if (idx_begin == idx_end)
-
62  return;
-
63 
-
64  const auto& dist_a = mat_a.distribution();
-
65  const auto rank = dist_a.rankIndex();
-
66 
-
67  // which rank has the last tile involved
-
68  const bool rankHasLastRow = rank.row() == dist_a.template rankGlobalTile<Coord::Row>(idx_end - 1);
-
69  const bool rankHasLastCol = rank.col() == dist_a.template rankGlobalTile<Coord::Col>(idx_end - 1);
-
70 
-
71  // translate from global to local indices
-
72  const SizeType i_beg = dist_a.template nextLocalTileFromGlobalTile<Coord::Row>(idx_begin);
-
73  const SizeType i_end = dist_a.template nextLocalTileFromGlobalTile<Coord::Row>(idx_end);
-
74 
-
75  const SizeType j_beg = dist_a.template nextLocalTileFromGlobalTile<Coord::Col>(idx_begin);
-
76  const SizeType j_end = dist_a.template nextLocalTileFromGlobalTile<Coord::Col>(idx_end);
-
77 
-
78  const SizeType mb = dist_a.blockSize().rows();
-
79  const SizeType lastTileElement = std::min(idx_end * mb - 1, dist_a.size().rows() - 1);
-
80  const SizeType nrefls = lastTileElement - idx_begin * mb + 1;
+
14 #include <dlaf/blas/tile_extensions.h>
+
15 #include <dlaf/common/assert.h>
+
16 #include <dlaf/common/index2d.h>
+
17 #include <dlaf/common/pipeline.h>
+
18 #include <dlaf/common/round_robin.h>
+
19 #include <dlaf/communication/broadcast_panel.h>
+
20 #include <dlaf/communication/communicator_grid.h>
+
21 #include <dlaf/matrix/distribution.h>
+
22 #include <dlaf/matrix/index.h>
+
23 #include <dlaf/matrix/matrix.h>
+
24 #include <dlaf/matrix/matrix_ref.h>
+
25 #include <dlaf/matrix/panel.h>
+
26 #include <dlaf/multiplication/general/api.h>
+
27 #include <dlaf/sender/when_all_lift.h>
+
28 #include <dlaf/util_matrix.h>
+
29 
+
30 namespace dlaf::multiplication {
+
31 namespace internal {
+
32 
+
33 template <Backend B, Device D, class T>
+
34 void General<B, D, T>::callNN(const T alpha, MatrixRef<const T, D>& mat_a, MatrixRef<const T, D>& mat_b,
+
35  const T beta, MatrixRef<T, D>& mat_c) {
+
36  namespace ex = pika::execution::experimental;
+
37 
+
38  if (mat_a.nr_tiles().cols() == 0) {
+
39  // Note: if beta == 1, we optimize by not even scheduling anything
+
40  if (beta != T(1)) {
+
41  for (SizeType j = 0; j < mat_c.distribution().local_nr_tiles().cols(); ++j)
+
42  for (SizeType i = 0; i < mat_c.distribution().local_nr_tiles().rows(); ++i)
+
43  ex::start_detached(dlaf::internal::whenAllLift(beta, mat_c.readwrite(LocalTileIndex(i, j))) |
+
44  tile::scal(dlaf::internal::Policy<B>()));
+
45  }
+
46  return;
+
47  }
+
48 
+
49  for (SizeType j = 0; j < mat_c.distribution().local_nr_tiles().cols(); ++j) {
+
50  for (SizeType i = 0; i < mat_c.distribution().local_nr_tiles().rows(); ++i) {
+
51  for (SizeType k = 0; k < mat_a.distribution().local_nr_tiles().cols(); ++k) {
+
52  ex::start_detached(dlaf::internal::whenAllLift(blas::Op::NoTrans, blas::Op::NoTrans, alpha,
+
53  mat_a.read(LocalTileIndex(i, k)),
+
54  mat_b.read(LocalTileIndex(k, j)),
+
55  k == 0 ? beta : T(1),
+
56  mat_c.readwrite(LocalTileIndex(i, j))) |
+
57  tile::gemm(dlaf::internal::Policy<B>()));
+
58  }
+
59  }
+
60  }
+
61 }
+
62 
+
63 template <Backend B, Device D, class T>
+
64 void GeneralSub<B, D, T>::callNN(const SizeType idx_begin, const SizeType idx_end, const blas::Op opA,
+
65  const blas::Op opB, const T alpha, Matrix<const T, D>& mat_a,
+
66  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c) {
+
67  namespace ex = pika::execution::experimental;
+
68 
+
69  for (SizeType j = idx_begin; j < idx_end; ++j) {
+
70  for (SizeType i = idx_begin; i < idx_end; ++i) {
+
71  for (SizeType k = idx_begin; k < idx_end; ++k) {
+
72  ex::start_detached(
+
73  dlaf::internal::whenAllLift(opA, opB, alpha, mat_a.read(GlobalTileIndex(i, k)),
+
74  mat_b.read(GlobalTileIndex(k, j)), k == idx_begin ? beta : T(1),
+
75  mat_c.readwrite(GlobalTileIndex(i, j))) |
+
76  tile::gemm(dlaf::internal::Policy<B>()));
+
77  }
+
78  }
+
79  }
+
80 }
81 
-
82  // Note: if last tile is incomplete, compute the size of it
-
83  const bool isEndRangePartial = nrefls % mb != 0;
-
84  const SizeType partialSize = (nrefls % mb);
-
85 
-
86  // Note:
-
87  // Workspace needed is limited to the range [i_begin:i_end). Its allocation is obtained by creating an
-
88  // ad hoc distribution that starts in the origin of the matrix and with a size covering all needed
-
89  // elements. This would lead to a [0:i_end) range, but by using panel offset at initialization, the
-
90  // part before the range will be left out from allocation, actually getting [i_begin:i_end)
-
91  const GlobalTileIndex panel_offset(idx_begin, idx_begin);
-
92  const matrix::Distribution dist_panel({lastTileElement + 1, lastTileElement + 1}, dist_a.blockSize(),
-
93  dist_a.commGridSize(), dist_a.rankIndex(),
-
94  dist_a.sourceRankIndex());
-
95 
-
96  constexpr std::size_t n_workspaces = 2;
-
97  common::RoundRobin<matrix::Panel<Coord::Col, T, D>> panelsA(n_workspaces, dist_panel, panel_offset);
-
98  common::RoundRobin<matrix::Panel<Coord::Row, T, D>> panelsB(n_workspaces, dist_panel, panel_offset);
-
99 
-
100  // This loops over the global indices for k, because every rank have to participate in communication
-
101  for (SizeType k = idx_begin; k < idx_end; ++k) {
-
102  auto& panelA = panelsA.nextResource();
-
103  auto& panelB = panelsB.nextResource();
+
82 // This implementation is based on
+
83 //
+
84 // Van De Geijn, Robert A., and Jerrell Watts.
+
85 // SUMMA: Scalable universal matrix multiplication algorithm.
+
86 // Concurrency: Practice and Experience 9.4 (1997): 255-274
+
87 template <Backend B, Device D, class T>
+
88 void GeneralSub<B, D, T>::callNN(common::Pipeline<comm::Communicator>& row_task_chain,
+
89  common::Pipeline<comm::Communicator>& col_task_chain,
+
90  const SizeType idx_begin, const SizeType idx_end, const T alpha,
+
91  Matrix<const T, D>& mat_a, Matrix<const T, D>& mat_b, const T beta,
+
92  Matrix<T, D>& mat_c) {
+
93  namespace ex = pika::execution::experimental;
+
94 
+
95  if (idx_begin == idx_end)
+
96  return;
+
97 
+
98  const auto& dist_a = mat_a.distribution();
+
99  const auto rank = dist_a.rankIndex();
+
100 
+
101  // which rank has the last tile involved
+
102  const bool rankHasLastRow = rank.row() == dist_a.template rankGlobalTile<Coord::Row>(idx_end - 1);
+
103  const bool rankHasLastCol = rank.col() == dist_a.template rankGlobalTile<Coord::Col>(idx_end - 1);
104 
-
105  const bool isKPartial = k == idx_end - 1 && isEndRangePartial;
-
106  const SizeType kSize = isKPartial ? partialSize : mb;
-
107  if (isKPartial) {
-
108  panelA.setWidth(kSize);
-
109  panelB.setHeight(kSize);
-
110  }
+
105  // translate from global to local indices
+
106  const SizeType i_beg = dist_a.template nextLocalTileFromGlobalTile<Coord::Row>(idx_begin);
+
107  const SizeType i_end = dist_a.template nextLocalTileFromGlobalTile<Coord::Row>(idx_end);
+
108 
+
109  const SizeType j_beg = dist_a.template nextLocalTileFromGlobalTile<Coord::Col>(idx_begin);
+
110  const SizeType j_end = dist_a.template nextLocalTileFromGlobalTile<Coord::Col>(idx_end);
111 
-
112  const auto rank_k = dist_a.rankGlobalTile({k, k});
-
113 
-
114  // Setup the column workspace for the root ranks, i.e. the ones in the current col
-
115  if (rank_k.col() == rank.col()) {
-
116  const auto k_local = dist_a.template localTileFromGlobalTile<Coord::Col>(k);
-
117  for (SizeType i = i_beg; i < i_end; ++i) {
-
118  const LocalTileIndex ik(i, k_local);
-
119  const bool isRowPartial = (i == i_end - 1 && isEndRangePartial && rankHasLastRow);
-
120  const SizeType nrows = isRowPartial ? partialSize : mb;
-
121  panelA.setTile(ik, (isRowPartial || isKPartial)
-
122  ? splitTile(mat_a.read(ik), {{0, 0}, {nrows, kSize}})
-
123  : mat_a.read(ik));
-
124  }
-
125  }
-
126  // Setup the row workspace for the root ranks, i.e. the ones in the current row
-
127  if (rank_k.row() == rank.row()) {
-
128  const auto k_local = dist_a.template localTileFromGlobalTile<Coord::Row>(k);
-
129  for (SizeType j = j_beg; j < j_end; ++j) {
-
130  const LocalTileIndex kj(k_local, j);
-
131  const bool isColPartial = (j == j_end - 1 && isEndRangePartial && rankHasLastCol);
-
132  const SizeType ncols = isColPartial ? partialSize : mb;
-
133  panelB.setTile(kj, (isKPartial || isColPartial)
-
134  ? splitTile(mat_b.read(kj), {{0, 0}, {kSize, ncols}})
-
135  : mat_b.read(kj));
-
136  }
-
137  }
+
112  const SizeType mb = dist_a.blockSize().rows();
+
113  const SizeType lastTileElement = std::min(idx_end * mb - 1, dist_a.size().rows() - 1);
+
114  const SizeType nrefls = lastTileElement - idx_begin * mb + 1;
+
115 
+
116  // Note: if last tile is incomplete, compute the size of it
+
117  const bool isEndRangePartial = nrefls % mb != 0;
+
118  const SizeType partialSize = (nrefls % mb);
+
119 
+
120  // Note:
+
121  // Workspace needed is limited to the range [i_begin:i_end). Its allocation is obtained by creating an
+
122  // ad hoc distribution that starts in the origin of the matrix and with a size covering all needed
+
123  // elements. This would lead to a [0:i_end) range, but by using panel offset at initialization, the
+
124  // part before the range will be left out from allocation, actually getting [i_begin:i_end)
+
125  const GlobalTileIndex panel_offset(idx_begin, idx_begin);
+
126  const matrix::Distribution dist_panel({lastTileElement + 1, lastTileElement + 1}, dist_a.blockSize(),
+
127  dist_a.commGridSize(), dist_a.rankIndex(),
+
128  dist_a.sourceRankIndex());
+
129 
+
130  constexpr std::size_t n_workspaces = 2;
+
131  common::RoundRobin<matrix::Panel<Coord::Col, T, D>> panelsA(n_workspaces, dist_panel, panel_offset);
+
132  common::RoundRobin<matrix::Panel<Coord::Row, T, D>> panelsB(n_workspaces, dist_panel, panel_offset);
+
133 
+
134  // This loops over the global indices for k, because every rank have to participate in communication
+
135  for (SizeType k = idx_begin; k < idx_end; ++k) {
+
136  auto& panelA = panelsA.nextResource();
+
137  auto& panelB = panelsB.nextResource();
138 
-
139  // Broadcast both column and row panel from root to others (row-wise and col-wise, respectively)
-
140  broadcast(rank_k.col(), panelA, row_task_chain);
-
141  broadcast(rank_k.row(), panelB, col_task_chain);
-
142 
-
143  // This is the core loop where the k step performs the update over the entire local matrix using
-
144  // the col and row workspaces.
-
145  // Everything needed for the update is available locally thanks to previous broadcasts.
-
146  for (SizeType i = i_beg; i < i_end; ++i) {
-
147  const bool isRowPartial = (i == i_end - 1 && isEndRangePartial && rankHasLastRow);
-
148  const SizeType nrows = isRowPartial ? partialSize : mb;
-
149 
-
150  for (SizeType j = j_beg; j < j_end; ++j) {
-
151  const LocalTileIndex ij(i, j);
-
152 
-
153  const bool isColPartial = (j == j_end - 1 && isEndRangePartial && rankHasLastCol);
-
154  const SizeType ncols = isColPartial ? partialSize : mb;
-
155 
-
156  ex::start_detached(
-
157  dlaf::internal::whenAllLift(blas::Op::NoTrans, blas::Op::NoTrans, alpha, panelA.read(ij),
-
158  panelB.read(ij), k == idx_begin ? beta : T(1),
-
159  (isRowPartial || isColPartial)
-
160  ? splitTile(mat_c.readwrite(ij), {{0, 0}, {nrows, ncols}})
-
161  : mat_c.readwrite(ij)) |
-
162  tile::gemm(dlaf::internal::Policy<B>()));
-
163  }
-
164  }
-
165 
-
166  panelA.reset();
-
167  panelB.reset();
-
168  }
-
169 }
-
170 }
-
171 }
+
139  const bool isKPartial = k == idx_end - 1 && isEndRangePartial;
+
140  const SizeType kSize = isKPartial ? partialSize : mb;
+
141  if (isKPartial) {
+
142  panelA.setWidth(kSize);
+
143  panelB.setHeight(kSize);
+
144  }
+
145 
+
146  const auto rank_k = dist_a.rankGlobalTile({k, k});
+
147 
+
148  // Setup the column workspace for the root ranks, i.e. the ones in the current col
+
149  if (rank_k.col() == rank.col()) {
+
150  const auto k_local = dist_a.template localTileFromGlobalTile<Coord::Col>(k);
+
151  for (SizeType i = i_beg; i < i_end; ++i) {
+
152  const LocalTileIndex ik(i, k_local);
+
153  const bool isRowPartial = (i == i_end - 1 && isEndRangePartial && rankHasLastRow);
+
154  const SizeType nrows = isRowPartial ? partialSize : mb;
+
155  panelA.setTile(ik, (isRowPartial || isKPartial)
+
156  ? splitTile(mat_a.read(ik), {{0, 0}, {nrows, kSize}})
+
157  : mat_a.read(ik));
+
158  }
+
159  }
+
160  // Setup the row workspace for the root ranks, i.e. the ones in the current row
+
161  if (rank_k.row() == rank.row()) {
+
162  const auto k_local = dist_a.template localTileFromGlobalTile<Coord::Row>(k);
+
163  for (SizeType j = j_beg; j < j_end; ++j) {
+
164  const LocalTileIndex kj(k_local, j);
+
165  const bool isColPartial = (j == j_end - 1 && isEndRangePartial && rankHasLastCol);
+
166  const SizeType ncols = isColPartial ? partialSize : mb;
+
167  panelB.setTile(kj, (isKPartial || isColPartial)
+
168  ? splitTile(mat_b.read(kj), {{0, 0}, {kSize, ncols}})
+
169  : mat_b.read(kj));
+
170  }
+
171  }
+
172 
+
173  // Broadcast both column and row panel from root to others (row-wise and col-wise, respectively)
+
174  broadcast(rank_k.col(), panelA, row_task_chain);
+
175  broadcast(rank_k.row(), panelB, col_task_chain);
+
176 
+
177  // This is the core loop where the k step performs the update over the entire local matrix using
+
178  // the col and row workspaces.
+
179  // Everything needed for the update is available locally thanks to previous broadcasts.
+
180  for (SizeType i = i_beg; i < i_end; ++i) {
+
181  const bool isRowPartial = (i == i_end - 1 && isEndRangePartial && rankHasLastRow);
+
182  const SizeType nrows = isRowPartial ? partialSize : mb;
+
183 
+
184  for (SizeType j = j_beg; j < j_end; ++j) {
+
185  const LocalTileIndex ij(i, j);
+
186 
+
187  const bool isColPartial = (j == j_end - 1 && isEndRangePartial && rankHasLastCol);
+
188  const SizeType ncols = isColPartial ? partialSize : mb;
+
189 
+
190  ex::start_detached(
+
191  dlaf::internal::whenAllLift(blas::Op::NoTrans, blas::Op::NoTrans, alpha, panelA.read(ij),
+
192  panelB.read(ij), k == idx_begin ? beta : T(1),
+
193  (isRowPartial || isColPartial)
+
194  ? splitTile(mat_c.readwrite(ij), {{0, 0}, {nrows, ncols}})
+
195  : mat_c.readwrite(ij)) |
+
196  tile::gemm(dlaf::internal::Policy<B>()));
+
197  }
+
198  }
+
199 
+
200  panelA.reset();
+
201  panelB.reset();
+
202  }
+
203 }
+
204 }
+
205 }
tile.h
broadcast_panel.h
dlaf::comm::broadcast
void broadcast(comm::IndexT_MPI rank_root, matrix::Panel< axis, T, D, storage > &panel, common::Pipeline< comm::Communicator > &serial_comm)
Definition: broadcast_panel.h:58
@@ -249,9 +283,13 @@
index2d.h
index.h
dlaf::matrix::splitTile
ReadOnlyTileSender< T, D > splitTile(ReadOnlyTileSender< T, D > tile, const SubTileSpec &spec)
Definition: tile.h:507
+
matrix.h
+
matrix_ref.h
panel.h
pipeline.h
round_robin.h
+
tile_extensions.h
+
util_matrix.h
diff --git a/master/multiplication_2general_8h.html b/master/multiplication_2general_8h.html index 9415323dbc..0e79750111 100644 --- a/master/multiplication_2general_8h.html +++ b/master/multiplication_2general_8h.html @@ -78,6 +78,7 @@ #include <dlaf/matrix/distribution.h>
#include <dlaf/matrix/index.h>
#include <dlaf/matrix/matrix.h>
+#include <dlaf/matrix/matrix_ref.h>
#include <dlaf/multiplication/general/api.h>
#include <dlaf/util_matrix.h>
@@ -85,6 +86,9 @@ + + + @@ -97,6 +101,96 @@

Functions

template<Backend B, Device D, class T >
void dlaf::multiplication::internal::generalMatrix (const blas::Op opA, const blas::Op opB, const T alpha, MatrixRef< const T, D > &mat_a, MatrixRef< const T, D > &mat_b, const T beta, MatrixRef< T, D > &mat_c)
 
template<Backend B, Device D, class T >
void dlaf::multiplication::internal::generalSubMatrix (const SizeType a, const SizeType b, const blas::Op opA, const blas::Op opB, const T alpha, Matrix< const T, D > &mat_a, Matrix< const T, D > &mat_b, const T beta, Matrix< T, D > &mat_c)
 
 

Function Documentation

+ +

◆ generalMatrix()

+ +
+
+
+template<Backend B, Device D, class T >
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void dlaf::multiplication::internal::generalMatrix (const blas::Op opA,
const blas::Op opB,
const T alpha,
MatrixRef< const T, D > & mat_a,
MatrixRef< const T, D > & mat_b,
const T beta,
MatrixRef< T, D > & mat_c 
)
+
+

General matrix multiplication implementation on local memory, computing C = alpha * opA(A) * opB(B) + beta * C

+
Parameters
+ + + + +
opAspecifies the form of opA(A) to be used in the matrix multiplication: currently only NoTrans,
opBspecifies the form of opB(B) to be used in the matrix multiplication: currently only NoTrans,
mat_acontains the input matrix A.
+
+
+
Precondition
mat_a is not distributed
+
Parameters
+ + +
mat_bcontains the input matrix B.
+
+
+
Precondition
mat_b is not distributed
+
Parameters
+ + +
mat_cOn entry it contains the input matrix C. On exit matrix will be overwritten with the result, while others are left untouched.
+
+
+
Precondition
mat_c is not distributed
+
+multipliable_sizes(mat_a.size(), mat_b.size(), mat_c.size(), opA, opB)
+
+multipliable_sizes(mat_a.tile_size(), mat_b.tile_size(), mat_c.tile_size(), opA, opB)
+
+multipliable_sizes(mat_a.tile_size_of({0, 0}), mat_b.tile_size_of({0, 0}), mat_c.tile_size_of({0, 0}), opA, opB)
+ +
+

◆ generalSubMatrix() [1/2]

diff --git a/master/multiplication_2general_8h_source.html b/master/multiplication_2general_8h_source.html index 79ae052303..67d82cc24f 100644 --- a/master/multiplication_2general_8h_source.html +++ b/master/multiplication_2general_8h_source.html @@ -90,111 +90,132 @@
20 #include <dlaf/matrix/distribution.h>
21 #include <dlaf/matrix/index.h>
22 #include <dlaf/matrix/matrix.h>
-
23 #include <dlaf/multiplication/general/api.h>
-
24 #include <dlaf/util_matrix.h>
-
25 
-
26 namespace dlaf::multiplication::internal {
-
27 
-
60 template <Backend B, Device D, class T>
-
61 void generalSubMatrix(const SizeType a, const SizeType b, const blas::Op opA, const blas::Op opB,
-
62  const T alpha, Matrix<const T, D>& mat_a, Matrix<const T, D>& mat_b, const T beta,
-
63  Matrix<T, D>& mat_c) {
-
64  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_a), mat_a);
-
65  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_b), mat_b);
-
66  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_c), mat_c);
-
67 
-
68  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
69  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
70  DLAF_ASSERT(matrix::single_tile_per_block(mat_c), mat_c);
-
71 
-
72  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
-
73  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
-
74  DLAF_ASSERT(matrix::local_matrix(mat_c), mat_c);
-
75 
-
76  // Note:
-
77  // This is an over-constraint, since the algorithm just cares about the sub-matrix size.
-
78  // It simplifies next check about [a,b) range validity, that otherwise would require it to be
-
79  // validated against every single sub-matrix in a, b and c that might have different element sizes.
-
80  //
-
81  // At the moment, we don't have this use-case, so let's keep it simple.
-
82  DLAF_ASSERT(dlaf::matrix::square_size(mat_a), mat_a);
-
83  DLAF_ASSERT(equal_size(mat_a, mat_b), mat_a, mat_b);
-
84  DLAF_ASSERT(equal_size(mat_a, mat_c), mat_a, mat_c);
-
85 
-
86  [[maybe_unused]] const SizeType m = mat_a.nrTiles().rows();
-
87  DLAF_ASSERT(a <= b, a, b);
-
88  DLAF_ASSERT(a >= 0 && a <= m, a, m);
-
89  DLAF_ASSERT(b >= 0 && b <= m, b, m);
-
90 
-
91  using namespace blas;
-
92 
-
93  if (opA == Op::NoTrans && opB == Op::NoTrans)
-
94  internal::GeneralSub<B, D, T>::callNN(a, b, opA, opB, alpha, mat_a, mat_b, beta, mat_c);
-
95  else
-
96  DLAF_UNIMPLEMENTED(opA, opB);
-
97 }
-
98 
-
126 template <Backend B, Device D, class T>
-
127 void generalSubMatrix([[maybe_unused]] comm::CommunicatorGrid grid,
-
128  common::Pipeline<comm::Communicator>& row_task_chain,
-
129  common::Pipeline<comm::Communicator>& col_task_chain, const SizeType a,
-
130  const SizeType b, const T alpha, Matrix<const T, D>& mat_a,
-
131  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c) {
-
132  DLAF_ASSERT(equal_process_grid(mat_a, grid), mat_a, grid);
-
133  DLAF_ASSERT(equal_process_grid(mat_b, grid), mat_a, grid);
-
134  DLAF_ASSERT(equal_process_grid(mat_c, grid), mat_a, grid);
-
135 
-
136  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_a), mat_a);
-
137  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_b), mat_b);
-
138  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_c), mat_c);
-
139 
-
140  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
141  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
142  DLAF_ASSERT(matrix::single_tile_per_block(mat_c), mat_c);
-
143 
-
144  // Note:
-
145  // This is an over-constraint, since the algorithm just cares about the sub-matrix size (and its
-
146  // distribution).
-
147  // It simplifies next check about [a,b) range validity, that otherwise would require it to be
-
148  // validated against every single sub-matrix in a, b and c that might have different element sizes.
-
149  //
-
150  // At the moment, we don't have this use-case, so let's keep it simple.
-
151  DLAF_ASSERT(dlaf::matrix::square_size(mat_a), mat_a);
-
152  DLAF_ASSERT(equal_size(mat_a, mat_b), mat_a, mat_b);
-
153  DLAF_ASSERT(equal_size(mat_a, mat_c), mat_a, mat_c);
-
154 
-
155  [[maybe_unused]] const SizeType m = mat_a.nrTiles().rows();
-
156  DLAF_ASSERT(a <= b, a, b);
-
157  DLAF_ASSERT(a >= 0 && a <= m, a, m);
-
158  DLAF_ASSERT(b >= 0 && b <= m, b, m);
-
159 
-
160  internal::GeneralSub<B, D, T>::callNN(row_task_chain, col_task_chain, a, b, alpha, mat_a, mat_b, beta,
-
161  mat_c);
-
162 }
-
163 
-
164 template <Backend B, Device D, class T>
-
165 void generalSubMatrix(comm::CommunicatorGrid grid, const SizeType a, const SizeType b, const T alpha,
-
166  Matrix<const T, D>& mat_a, Matrix<const T, D>& mat_b, const T beta,
-
167  Matrix<T, D>& mat_c) {
-
168  common::Pipeline<comm::Communicator> row_task_chain(grid.rowCommunicator().clone());
-
169  common::Pipeline<comm::Communicator> col_task_chain(grid.colCommunicator().clone());
-
170  generalSubMatrix<B, D, T>(grid, row_task_chain, col_task_chain, a, b, alpha, mat_a, mat_b, beta,
-
171  mat_c);
-
172 }
-
173 
-
174 }
+
23 #include <dlaf/matrix/matrix_ref.h>
+
24 #include <dlaf/multiplication/general/api.h>
+
25 #include <dlaf/util_matrix.h>
+
26 
+
27 namespace dlaf::multiplication::internal {
+
28 
+
51 template <Backend B, Device D, class T>
+
52 void generalMatrix(const blas::Op opA, const blas::Op opB, const T alpha, MatrixRef<const T, D>& mat_a,
+
53  MatrixRef<const T, D>& mat_b, const T beta, MatrixRef<T, D>& mat_c) {
+
54  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
55  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
+
56  DLAF_ASSERT(matrix::local_matrix(mat_c), mat_c);
+
57 
+
58  DLAF_ASSERT_HEAVY(matrix::multipliable(mat_a, mat_b, mat_c, blas::Op::NoTrans, blas::Op::NoTrans),
+
59  mat_a, mat_b, mat_c);
+
60 
+
61  if (opA == blas::Op::NoTrans && opB == blas::Op::NoTrans)
+
62  internal::General<B, D, T>::callNN(alpha, mat_a, mat_b, beta, mat_c);
+
63  else
+
64  DLAF_UNIMPLEMENTED(opA, opB);
+
65 }
+
66 
+
99 template <Backend B, Device D, class T>
+
100 void generalSubMatrix(const SizeType a, const SizeType b, const blas::Op opA, const blas::Op opB,
+
101  const T alpha, Matrix<const T, D>& mat_a, Matrix<const T, D>& mat_b, const T beta,
+
102  Matrix<T, D>& mat_c) {
+
103  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_a), mat_a);
+
104  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_b), mat_b);
+
105  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_c), mat_c);
+
106 
+
107  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
108  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
109  DLAF_ASSERT(matrix::single_tile_per_block(mat_c), mat_c);
+
110 
+
111  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
112  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
+
113  DLAF_ASSERT(matrix::local_matrix(mat_c), mat_c);
+
114 
+
115  // Note:
+
116  // This is an over-constraint, since the algorithm just cares about the sub-matrix size.
+
117  // It simplifies next check about [a,b) range validity, that otherwise would require it to be
+
118  // validated against every single sub-matrix in a, b and c that might have different element sizes.
+
119  //
+
120  // At the moment, we don't have this use-case, so let's keep it simple.
+
121  DLAF_ASSERT(dlaf::matrix::square_size(mat_a), mat_a);
+
122  DLAF_ASSERT(equal_size(mat_a, mat_b), mat_a, mat_b);
+
123  DLAF_ASSERT(equal_size(mat_a, mat_c), mat_a, mat_c);
+
124 
+
125  [[maybe_unused]] const SizeType m = mat_a.nrTiles().rows();
+
126  DLAF_ASSERT(a <= b, a, b);
+
127  DLAF_ASSERT(a >= 0 && a <= m, a, m);
+
128  DLAF_ASSERT(b >= 0 && b <= m, b, m);
+
129 
+
130  using namespace blas;
+
131 
+
132  if (opA == Op::NoTrans && opB == Op::NoTrans)
+
133  internal::GeneralSub<B, D, T>::callNN(a, b, opA, opB, alpha, mat_a, mat_b, beta, mat_c);
+
134  else
+
135  DLAF_UNIMPLEMENTED(opA, opB);
+
136 }
+
137 
+
165 template <Backend B, Device D, class T>
+
166 void generalSubMatrix([[maybe_unused]] comm::CommunicatorGrid grid,
+
167  common::Pipeline<comm::Communicator>& row_task_chain,
+
168  common::Pipeline<comm::Communicator>& col_task_chain, const SizeType a,
+
169  const SizeType b, const T alpha, Matrix<const T, D>& mat_a,
+
170  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c) {
+
171  DLAF_ASSERT(equal_process_grid(mat_a, grid), mat_a, grid);
+
172  DLAF_ASSERT(equal_process_grid(mat_b, grid), mat_a, grid);
+
173  DLAF_ASSERT(equal_process_grid(mat_c, grid), mat_a, grid);
+
174 
+
175  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_a), mat_a);
+
176  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_b), mat_b);
+
177  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_c), mat_c);
+
178 
+
179  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
180  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
181  DLAF_ASSERT(matrix::single_tile_per_block(mat_c), mat_c);
+
182 
+
183  // Note:
+
184  // This is an over-constraint, since the algorithm just cares about the sub-matrix size (and its
+
185  // distribution).
+
186  // It simplifies next check about [a,b) range validity, that otherwise would require it to be
+
187  // validated against every single sub-matrix in a, b and c that might have different element sizes.
+
188  //
+
189  // At the moment, we don't have this use-case, so let's keep it simple.
+
190  DLAF_ASSERT(dlaf::matrix::square_size(mat_a), mat_a);
+
191  DLAF_ASSERT(equal_size(mat_a, mat_b), mat_a, mat_b);
+
192  DLAF_ASSERT(equal_size(mat_a, mat_c), mat_a, mat_c);
+
193 
+
194  [[maybe_unused]] const SizeType m = mat_a.nrTiles().rows();
+
195  DLAF_ASSERT(a <= b, a, b);
+
196  DLAF_ASSERT(a >= 0 && a <= m, a, m);
+
197  DLAF_ASSERT(b >= 0 && b <= m, b, m);
+
198 
+
199  internal::GeneralSub<B, D, T>::callNN(row_task_chain, col_task_chain, a, b, alpha, mat_a, mat_b, beta,
+
200  mat_c);
+
201 }
+
202 
+
203 template <Backend B, Device D, class T>
+
204 void generalSubMatrix(comm::CommunicatorGrid grid, const SizeType a, const SizeType b, const T alpha,
+
205  Matrix<const T, D>& mat_a, Matrix<const T, D>& mat_b, const T beta,
+
206  Matrix<T, D>& mat_c) {
+
207  common::Pipeline<comm::Communicator> row_task_chain(grid.rowCommunicator().clone());
+
208  common::Pipeline<comm::Communicator> col_task_chain(grid.colCommunicator().clone());
+
209  generalSubMatrix<B, D, T>(grid, row_task_chain, col_task_chain, a, b, alpha, mat_a, mat_b, beta,
+
210  mat_c);
+
211 }
+
212 
+
213 }
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
dlaf::comm::CommunicatorGrid::rowCommunicator
Communicator & rowCommunicator() noexcept
Return a Communicator grouping all ranks in the row (that includes the current process).
Definition: communicator_grid.h:78
dlaf::comm::CommunicatorGrid::colCommunicator
Communicator & colCommunicator() noexcept
Return a Communicator grouping all ranks in the column (that includes the current process).
Definition: communicator_grid.h:83
dlaf::comm::Communicator::clone
Communicator clone() const noexcept
Definition: communicator.h:73
dlaf::common::Pipeline
Definition: pipeline.h:31
+
dlaf::matrix::internal::MatrixRef
Definition: matrix_ref.h:108
communicator_grid.h
-
dlaf::multiplication::internal::generalSubMatrix
void generalSubMatrix(const SizeType a, const SizeType b, const blas::Op opA, const blas::Op opB, const T alpha, Matrix< const T, D > &mat_a, Matrix< const T, D > &mat_b, const T beta, Matrix< T, D > &mat_c)
Definition: general.h:61
+
matrix_ref.h
+
dlaf::multiplication::internal::generalSubMatrix
void generalSubMatrix(const SizeType a, const SizeType b, const blas::Op opA, const blas::Op opB, const T alpha, Matrix< const T, D > &mat_a, Matrix< const T, D > &mat_b, const T beta, Matrix< T, D > &mat_c)
Definition: general.h:100
+
dlaf::multiplication::internal::generalMatrix
void generalMatrix(const blas::Op opA, const blas::Op opB, const T alpha, MatrixRef< const T, D > &mat_a, MatrixRef< const T, D > &mat_b, const T beta, MatrixRef< T, D > &mat_c)
Definition: general.h:52
- + +
util_matrix.h
diff --git a/master/print__csv_8h_source.html b/master/print__csv_8h_source.html index 2569826fc1..80b4714d62 100644 --- a/master/print__csv_8h_source.html +++ b/master/print__csv_8h_source.html @@ -109,7 +109,7 @@
43 template <class T>
44 void print(format::csv, std::string sym, Matrix<const T, Device::CPU>& mat,
45  std::ostream& os = std::cout) {
-
46  if (!local_matrix(mat)) {
+
46  if (!local_matrix(mat)) {
47  os << mat << std::endl;
48  return;
49  }
@@ -150,7 +150,7 @@
dlaf::format::csv
Definition: print_csv.h:24
util_matrix.h
-
dlaf::matrix::local_matrix
bool local_matrix(const Matrix< const T, D > &m) noexcept
Returns true if the matrix is local to a process.
Definition: util_matrix.h:72
+
dlaf::matrix::local_matrix
bool local_matrix(const MatrixLike< const T, D > &m) noexcept
Returns true if the matrix is local to a process.
Definition: util_matrix.h:72
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b/master/structdlaf_1_1multiplication_1_1internal_1_1_general-members.html @@ -0,0 +1,82 @@ + + + + + + + +DLAF: Member List + + + + + + + + + +
+
+ + + + + + +
+
DLAF +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+
dlaf::multiplication::internal::General< B, D, T > Member List
+
+
+ +

This is the complete list of members for dlaf::multiplication::internal::General< B, D, T >, including all inherited members.

+ + +
callNN(const T alpha, MatrixRef< const T, D > &mat_a, MatrixRef< const T, D > &mat_b, const T beta, MatrixRef< T, D > &mat_c) (defined in dlaf::multiplication::internal::General< B, D, T >)dlaf::multiplication::internal::General< B, D, T >static
+ +
+Generated by doxygen 1.9.1 +
+ + diff --git a/master/structdlaf_1_1multiplication_1_1internal_1_1_general.html b/master/structdlaf_1_1multiplication_1_1internal_1_1_general.html new file mode 100644 index 0000000000..723138f888 --- /dev/null +++ b/master/structdlaf_1_1multiplication_1_1internal_1_1_general.html @@ -0,0 +1,92 @@ + + + + + + + +DLAF: dlaf::multiplication::internal::General< B, D, T > Struct Template Reference + + + + + + + + + +
+
+ + + + + + +
+
DLAF +
+
+
+ + + + + + + + +
+
+ + +
+ +
+ + +
+
+
+Static Public Member Functions | +List of all members
+
+
dlaf::multiplication::internal::General< B, D, T > Struct Template Reference
+
+
+ + + + +

+Static Public Member Functions

+static void callNN (const T alpha, MatrixRef< const T, D > &mat_a, MatrixRef< const T, D > &mat_b, const T beta, MatrixRef< T, D > &mat_c)
 
+
The documentation for this struct was generated from the following files:
    +
  • /home/runner/work/DLA-Future/DLA-Future/include/dlaf/multiplication/general/api.h
    • +
  • /home/runner/work/DLA-Future/DLA-Future/include/dlaf/multiplication/general/impl.h
    • +
+
+ +
+Generated by doxygen 1.9.1 +
+ + diff --git a/master/tile__extensions_8h.html b/master/tile__extensions_8h.html index 24c66360d8..15ee6bd75e 100644 --- a/master/tile__extensions_8h.html +++ b/master/tile__extensions_8h.html @@ -98,6 +98,15 @@ template<Backend B> auto dlaf::tile::add (const dlaf::internal::Policy< B > &p)   +template<Backend B, class T , Device D> +void dlaf::tile::scal (T beta, const matrix::Tile< T, D > &tile) +  +template<Backend B, typename Sender , typename = std::enable_if_t<pika::execution::experimental::is_sender_v<Sender>>> +auto dlaf::tile::scal (const dlaf::internal::Policy< B > &p, Sender &&s) +  +template<Backend B> +auto dlaf::tile::scal (const dlaf::internal::Policy< B > &p) + 

Detailed Description

Provides Tile wrappers for extra basic linear algebra operations not covered by BLAS.

@@ -190,6 +199,90 @@

+

◆ scal() [1/3]

+ +
+
+
+template<Backend B>
+ + + + + + + + +
auto dlaf::tile::scal (const dlaf::internal::Policy< B > & p)
+
+

This overload partially applies the algorithm with a policy for later use with operator| with a sender on the left-hand side.

+ +
+
+ +

◆ scal() [2/3]

+ +
+
+
+template<Backend B, typename Sender , typename = std::enable_if_t<pika::execution::experimental::is_sender_v<Sender>>>
+ + + + + + + + + + + + + + + + + + +
auto dlaf::tile::scal (const dlaf::internal::Policy< B > & p,
Sender && s 
)
+
+

This overload takes a policy argument and a sender which must send all required arguments for the algorithm. Returns a sender which signals a connected receiver when the algorithm is done.

+ +
+
+ +

◆ scal() [3/3]

+ +
+
+
+template<Backend B, class T , Device D>
+ + + + + + + + + + + + + + + + + + +
void dlaf::tile::scal (T beta,
const matrix::Tile< T, D > & tile 
)
+
+

Computes A = beta * A

+

This overload blocks until completion of the algorithm.

+

diff --git a/master/tile__extensions_8h_source.html b/master/tile__extensions_8h_source.html index 2b77e6023e..0b59b958f6 100644 --- a/master/tile__extensions_8h_source.html +++ b/master/tile__extensions_8h_source.html @@ -115,38 +115,72 @@
53 template <Backend B>
54 auto add(const dlaf::internal::Policy<B>& p);
55 
-
56 #else
-
57 
-
58 namespace internal {
-
59 
-
60 template <class T>
-
61 void add(T alpha, const matrix::Tile<const T, Device::CPU>& tile_b,
-
62  const matrix::Tile<T, Device::CPU>& tile_a) {
-
63  DLAF_ASSERT(equal_size(tile_a, tile_b), tile_a, tile_b);
-
64  common::internal::SingleThreadedBlasScope single;
-
65  for (auto j = 0; j < tile_a.size().cols(); ++j)
-
66  blas::axpy(tile_a.size().rows(), alpha, tile_b.ptr({0, j}), 1, tile_a.ptr({0, j}), 1);
-
67 }
-
68 
-
69 #ifdef DLAF_WITH_GPU
-
70 template <class T>
-
71 void add(T alpha, const matrix::Tile<const T, Device::GPU>& tile_b,
-
72  const matrix::Tile<T, Device::GPU>& tile_a, whip::stream_t stream) {
-
73  DLAF_ASSERT(equal_size(tile_a, tile_b), tile_a, tile_b);
+
59 template <Backend B, class T, Device D>
+
60 void scal(T beta, const matrix::Tile<T, D>& tile);
+
61 
+
64 template <Backend B, typename Sender,
+
65  typename = std::enable_if_t<pika::execution::experimental::is_sender_v<Sender>>>
+
66 auto scal(const dlaf::internal::Policy<B>& p, Sender&& s);
+
67 
+
70 template <Backend B>
+
71 auto scal(const dlaf::internal::Policy<B>& p);
+
72 
+
73 #else
74 
-
75  gpulapack::add(blas::Uplo::General, tile_a.size().rows(), tile_a.size().cols(), alpha, tile_b.ptr(),
-
76  tile_b.ld(), tile_a.ptr(), tile_a.ld(), stream);
-
77 }
-
78 #endif
-
79 
-
80 DLAF_MAKE_CALLABLE_OBJECT(add);
-
81 }
-
82 
-
83 DLAF_MAKE_SENDER_ALGORITHM_OVERLOADS(dlaf::internal::TransformDispatchType::Plain, add, internal::add_o)
-
84 
-
85 #endif
-
86 }
-
87 }
+
75 namespace internal {
+
76 
+
77 template <class T>
+
78 void add(T alpha, const matrix::Tile<const T, Device::CPU>& tile_b,
+
79  const matrix::Tile<T, Device::CPU>& tile_a) {
+
80  DLAF_ASSERT(equal_size(tile_a, tile_b), tile_a, tile_b);
+
81  common::internal::SingleThreadedBlasScope single;
+
82  for (auto j = 0; j < tile_a.size().cols(); ++j)
+
83  blas::axpy(tile_a.size().rows(), alpha, tile_b.ptr({0, j}), 1, tile_a.ptr({0, j}), 1);
+
84 }
+
85 
+
86 #ifdef DLAF_WITH_GPU
+
87 template <class T>
+
88 void add(T alpha, const matrix::Tile<const T, Device::GPU>& tile_b,
+
89  const matrix::Tile<T, Device::GPU>& tile_a, whip::stream_t stream) {
+
90  DLAF_ASSERT(equal_size(tile_a, tile_b), tile_a, tile_b);
+
91 
+
92  gpulapack::add(blas::Uplo::General, tile_a.size().rows(), tile_a.size().cols(), alpha, tile_b.ptr(),
+
93  tile_b.ld(), tile_a.ptr(), tile_a.ld(), stream);
+
94 }
+
95 #endif
+
96 
+
97 DLAF_MAKE_CALLABLE_OBJECT(add);
+
98 
+
99 template <class T>
+
100 void scal(T beta, const matrix::Tile<T, Device::CPU>& tile) {
+
101  common::internal::SingleThreadedBlasScope single;
+
102  blas::gemm(blas::Layout::ColMajor, blas::Op::NoTrans, blas::Op::NoTrans, tile.size().rows(),
+
103  tile.size().cols(), 0, T(0), nullptr, tile.ld(), nullptr, 1, beta, tile.ptr(), tile.ld());
+
104 }
+
105 
+
106 #ifdef DLAF_WITH_GPU
+
107 template <class T>
+
108 void scal(cublasHandle_t handle, T beta, const matrix::Tile<T, Device::GPU>& tile) {
+
109  using util::blasToCublasCast;
+
110 
+
111  const T alpha = 0;
+
112  gpublas::internal::Gemm<T>::call(handle, CUBLAS_OP_N, CUBLAS_OP_N, to_int(tile.size().rows()),
+
113  to_int(tile.size().cols()), 0, blasToCublasCast(&alpha),
+
114  blasToCublasCast<T*>(nullptr), to_int(tile.ld()),
+
115  blasToCublasCast<T*>(nullptr), to_int(1), blasToCublasCast(&beta),
+
116  blasToCublasCast(tile.ptr()), to_int(tile.ld()));
+
117 }
+
118 #endif
+
119 
+
120 DLAF_MAKE_CALLABLE_OBJECT(scal);
+
121 }
+
122 
+
123 DLAF_MAKE_SENDER_ALGORITHM_OVERLOADS(dlaf::internal::TransformDispatchType::Plain, add, internal::add_o)
+
124 DLAF_MAKE_SENDER_ALGORITHM_OVERLOADS(dlaf::internal::TransformDispatchType::Blas, scal, internal::scal_o)
+
125 
+
126 #endif
+
127 }
+
128 }
callable_object.h
DLAF_MAKE_CALLABLE_OBJECT
#define DLAF_MAKE_CALLABLE_OBJECT(fname)
Definition: callable_object.h:26
@@ -158,6 +192,7 @@
single_threaded_blas.h
dlaf::tile::add
void add(T alpha, const matrix::Tile< const T, D > &tile_b, const matrix::Tile< T, D > &tile_a)
+
dlaf::to_int
auto to_int(const T unsigned_value)
Definition: types.h:247
util_matrix.h
diff --git a/master/util__matrix_8h.html b/master/util__matrix_8h.html index 958fe290fd..ec420094d9 100644 --- a/master/util__matrix_8h.html +++ b/master/util__matrix_8h.html @@ -127,16 +127,21 @@ bool dlaf::matrix::equal_blocksize (const Matrix< const T, D1 > &lhs, Matrix< const T, D2 > &rhs) noexcept  Returns true if matrices have equal blocksizes.
  - -template<class T , Device D> -bool dlaf::matrix::local_matrix (const Matrix< const T, D > &m) noexcept - Returns true if the matrix is local to a process.
-  + +template<template< class, Device > class MatrixLike, class T , Device D> +bool dlaf::matrix::local_matrix (const MatrixLike< const T, D > &m) noexcept + Returns true if the matrix is local to a process.
+  template<class T , Device D> bool dlaf::matrix::equal_process_grid (const Matrix< const T, D > &m, const comm::CommunicatorGrid &g) noexcept  Returns true if the matrix is distributed on the communication grid.
  + +template<template< class, Device > class MatrixLikeA, template< class, Device > class MatrixLikeB, class T , Device D> +bool dlaf::matrix::same_process_grid (const MatrixLikeA< const T, D > &a, const MatrixLikeB< const T, D > &b) noexcept + Returns true if the matrix is distributed on the communication grid.
+  template<class T , Device D1, Device D2> bool dlaf::matrix::equal_distributions (const Matrix< const T, D1 > &lhs, const Matrix< const T, D2 > &rhs) noexcept @@ -147,11 +152,11 @@ bool dlaf::matrix::multipliable_sizes (common::Size2D< IndexT, Tag > a, common::Size2D< IndexT, Tag > b, common::Size2D< IndexT, Tag > c, const blas::Op opA, const blas::Op opB) noexcept  Returns true if the sizes are compatible for matrix multiplication.
  - -template<class T , Device D> -bool dlaf::matrix::multipliable (const Matrix< const T, D > &a, const Matrix< const T, D > &b, const Matrix< const T, D > &c, const blas::Op opA, const blas::Op opB) noexcept - Returns true if matrices a, b and c have matrix multipliable sizes and block sizes.
-  + +template<template< class, Device > class MatrixLikeA, template< class, Device > class MatrixLikeB, template< class, Device > class MatrixLikeC, class T , Device D> +bool dlaf::matrix::multipliable (const MatrixLikeA< const T, D > &a, const MatrixLikeB< const T, D > &b, const MatrixLikeC< const T, D > &c, const blas::Op opA, const blas::Op opB) noexcept + Returns true if matrices a, b and c have matrix multipliable sizes and block sizes.
+  template<Backend backend, class T , Device D> void dlaf::matrix::util::set0 (pika::execution::thread_priority priority, LocalTileIndex begin, LocalTileSize sz, Matrix< T, D > &matrix)   diff --git a/master/util__matrix_8h_source.html b/master/util__matrix_8h_source.html index bf00df63dc..9829a6aa4a 100644 --- a/master/util__matrix_8h_source.html +++ b/master/util__matrix_8h_source.html @@ -132,8 +132,8 @@
67  return lhs.block_size() == rhs.block_size();
68 }
69 
-
71 template <class T, Device D>
-
72 bool local_matrix(const Matrix<const T, D>& m) noexcept {
+
71 template <template <class, Device> class MatrixLike, class T, Device D>
+
72 bool local_matrix(const MatrixLike<const T, D>& m) noexcept {
73  return m.commGridSize() == comm::Size2D(1, 1);
74 }
75 
@@ -142,294 +142,314 @@
79  return m.commGridSize() == g.size() && m.rankIndex() == g.rank();
80 }
81 
-
83 template <class T, Device D1, Device D2>
-
84 bool equal_distributions(const Matrix<const T, D1>& lhs, const Matrix<const T, D2>& rhs) noexcept {
-
85  return lhs.distribution() == rhs.distribution();
-
86 }
-
87 
-
89 template <class IndexT, class Tag>
-
90 bool multipliable_sizes(common::Size2D<IndexT, Tag> a, common::Size2D<IndexT, Tag> b,
-
91  common::Size2D<IndexT, Tag> c, const blas::Op opA, const blas::Op opB) noexcept {
-
92  if (opA != blas::Op::NoTrans)
-
93  a.transpose();
-
94  if (opB != blas::Op::NoTrans)
-
95  b.transpose();
-
96 
-
97  return a.rows() == c.rows() && a.cols() == b.rows() && b.cols() == c.cols();
-
98 }
-
99 
-
101 template <class T, Device D>
-
102 bool multipliable(const Matrix<const T, D>& a, const Matrix<const T, D>& b, const Matrix<const T, D>& c,
-
103  const blas::Op opA, const blas::Op opB) noexcept {
-
104  return multipliable_sizes(a.size(), b.size(), c.size(), opA, opB) &&
-
105  multipliable_sizes(a.block_size(), b.block_size(), c.block_size(), opA, opB);
-
106 }
-
107 
-
108 namespace util {
-
109 namespace internal {
-
110 
-
112 template <class T>
-
113 class getter_random {
-
114  static_assert(std::is_same_v<T, float> || std::is_same_v<T, double>,
-
115  "T is not compatible with random generator used.");
+
83 template <template <class, Device> class MatrixLikeA, template <class, Device> class MatrixLikeB,
+
84  class T, Device D>
+
85 bool same_process_grid(const MatrixLikeA<const T, D>& a, const MatrixLikeB<const T, D>& b) noexcept {
+
86  return a.commGridSize() == b.commGridSize() && a.rankIndex() == b.rankIndex();
+
87 }
+
88 
+
90 template <class T, Device D1, Device D2>
+
91 bool equal_distributions(const Matrix<const T, D1>& lhs, const Matrix<const T, D2>& rhs) noexcept {
+
92  return lhs.distribution() == rhs.distribution();
+
93 }
+
94 
+
96 template <class IndexT, class Tag>
+
97 bool multipliable_sizes(common::Size2D<IndexT, Tag> a, common::Size2D<IndexT, Tag> b,
+
98  common::Size2D<IndexT, Tag> c, const blas::Op opA, const blas::Op opB) noexcept {
+
99  if (opA != blas::Op::NoTrans)
+
100  a.transpose();
+
101  if (opB != blas::Op::NoTrans)
+
102  b.transpose();
+
103 
+
104  return a.rows() == c.rows() && a.cols() == b.rows() && b.cols() == c.cols();
+
105 }
+
106 
+
108 template <template <class, Device> class MatrixLikeA, template <class, Device> class MatrixLikeB,
+
109  template <class, Device> class MatrixLikeC, class T, Device D>
+
110 bool multipliable(const MatrixLikeA<const T, D>& a, const MatrixLikeB<const T, D>& b,
+
111  const MatrixLikeC<const T, D>& c, const blas::Op opA, const blas::Op opB) noexcept {
+
112  const bool isSizeOk = multipliable_sizes(a.size(), b.size(), c.size(), opA, opB);
+
113 
+
114  if (a.size().isEmpty() || c.size().isEmpty())
+
115  return isSizeOk;
116 
-
117 public:
-
118  getter_random() : random_engine_(std::minstd_rand::default_seed) {}
-
119  getter_random(SizeType seed) : random_engine_(static_cast<std::size_t>(seed)) {
-
120  DLAF_ASSERT(seed >= 0, "");
-
121  }
-
122 
-
123  T operator()() {
-
124  return random_sampler_(random_engine_);
-
125  }
-
126 
-
127 private:
-
128  std::mt19937_64 random_engine_;
-
129  std::uniform_real_distribution<T> random_sampler_{-1, 1};
-
130 };
+
117  const bool allSameGrid = same_process_grid(a, b) && same_process_grid(b, c);
+
118  const bool isTileSizeOk = multipliable_sizes(a.tile_size(), b.tile_size(), c.tile_size(), opA, opB);
+
119  const bool isOffsetOk = multipliable_sizes(a.tile_size_of({0, 0}), b.tile_size_of({0, 0}),
+
120  c.tile_size_of({0, 0}), opA, opB);
+
121 
+
122  if (local_matrix(c))
+
123  return allSameGrid && isSizeOk && isTileSizeOk && isOffsetOk;
+
124 
+
125  // TODO distributed (fix offset)
+
126  return allSameGrid && isSizeOk && isTileSizeOk && isOffsetOk;
+
127 }
+
128 
+
129 namespace util {
+
130 namespace internal {
131 
133 template <class T>
-
134 class getter_random<std::complex<T>> : private getter_random<T> {
-
135 public:
-
136  using getter_random<T>::getter_random;
+
134 class getter_random {
+
135  static_assert(std::is_same_v<T, float> || std::is_same_v<T, double>,
+
136  "T is not compatible with random generator used.");
137 
-
138  std::complex<T> operator()() {
-
139  return std::polar<T>(std::abs(getter_random<T>::operator()()),
-
140  static_cast<T>(M_PI) * getter_random<T>::operator()());
-
141  }
-
142 };
+
138 public:
+
139  getter_random() : random_engine_(std::minstd_rand::default_seed) {}
+
140  getter_random(SizeType seed) : random_engine_(static_cast<std::size_t>(seed)) {
+
141  DLAF_ASSERT(seed >= 0, "");
+
142  }
143 
-
144 }
-
145 
-
148 template <Backend backend, class T, Device D>
-
149 void set0(pika::execution::thread_priority priority, LocalTileIndex begin, LocalTileSize sz,
-
150  Matrix<T, D>& matrix) {
-
151  using dlaf::internal::Policy;
-
152  using pika::execution::thread_stacksize;
-
153  using pika::execution::experimental::start_detached;
-
154 
-
155  for (const auto& idx : iterate_range2d(begin, sz))
-
156  start_detached(matrix.readwrite(idx) |
-
157  tile::set0(Policy<backend>(priority, thread_stacksize::nostack)));
-
158 }
-
159 
-
164 template <Backend backend, class T, Device D>
-
165 void set0(pika::execution::thread_priority priority, Matrix<T, D>& matrix) {
-
166  set0<backend>(priority, LocalTileIndex(0, 0), matrix.distribution().local_nr_tiles(), matrix);
-
167 }
-
168 
-
170 template <Backend backend, class T, Coord axis, Device D, StoreTransposed storage>
-
171 void set0(pika::execution::thread_priority priority, Panel<axis, T, D, storage>& panel) {
+
144  T operator()() {
+
145  return random_sampler_(random_engine_);
+
146  }
+
147 
+
148 private:
+
149  std::mt19937_64 random_engine_;
+
150  std::uniform_real_distribution<T> random_sampler_{-1, 1};
+
151 };
+
152 
+
154 template <class T>
+
155 class getter_random<std::complex<T>> : private getter_random<T> {
+
156 public:
+
157  using getter_random<T>::getter_random;
+
158 
+
159  std::complex<T> operator()() {
+
160  return std::polar<T>(std::abs(getter_random<T>::operator()()),
+
161  static_cast<T>(M_PI) * getter_random<T>::operator()());
+
162  }
+
163 };
+
164 
+
165 }
+
166 
+
169 template <Backend backend, class T, Device D>
+
170 void set0(pika::execution::thread_priority priority, LocalTileIndex begin, LocalTileSize sz,
+
171  Matrix<T, D>& matrix) {
172  using dlaf::internal::Policy;
173  using pika::execution::thread_stacksize;
174  using pika::execution::experimental::start_detached;
175 
-
176  for (const auto& tile_idx : panel.iteratorLocal())
-
177  start_detached(panel.readwrite(tile_idx) |
+
176  for (const auto& idx : iterate_range2d(begin, sz))
+
177  start_detached(matrix.readwrite(idx) |
178  tile::set0(Policy<backend>(priority, thread_stacksize::nostack)));
179 }
180 
-
187 template <class T, class ElementGetter>
-
188 void set(Matrix<T, Device::CPU>& matrix, ElementGetter el_f) {
-
189  using pika::execution::thread_stacksize;
-
190 
-
191  const Distribution& dist = matrix.distribution();
-
192  for (auto tile_wrt_local : iterate_range2d(dist.local_nr_tiles())) {
-
193  GlobalTileIndex tile_wrt_global = dist.global_tile_index(tile_wrt_local);
-
194  auto tl_index = dist.global_element_index(tile_wrt_global, {0, 0});
-
195 
-
196  using TileType = typename std::decay_t<decltype(matrix)>::TileType;
-
197  auto set_f = [tl_index, el_f = el_f](const TileType& tile) {
-
198  for (auto el_idx_l : iterate_range2d(tile.size())) {
-
199  GlobalElementIndex el_idx_g(el_idx_l.row() + tl_index.row(), el_idx_l.col() + tl_index.col());
-
200  tile(el_idx_l) = el_f(el_idx_g);
-
201  }
-
202  };
-
203 
-
204  dlaf::internal::transformDetach(dlaf::internal::Policy<Backend::MC>(thread_stacksize::nostack),
-
205  std::move(set_f), matrix.readwrite(tile_wrt_local));
-
206  }
-
207 }
-
208 
-
218 template <class T, class ElementGetter>
-
219 void set(Matrix<T, Device::CPU>& matrix, ElementGetter el_f, const blas::Op op) {
-
220  auto el_op_f = [op, el_f](const GlobalElementIndex& index) -> T {
-
221  using blas::Op;
-
222  switch (op) {
-
223  case Op::NoTrans:
-
224  return el_f(index);
-
225  case Op::Trans:
-
226  return el_f(transposed(index));
-
227  case Op::ConjTrans:
-
228  return dlaf::conj(el_f(transposed(index)));
-
229  default:
-
230  DLAF_UNIMPLEMENTED(op);
-
231  return T{};
-
232  }
-
233  };
-
234 
-
235  set(matrix, el_op_f);
-
236 }
-
237 
-
241 template <class T>
-
242 void set_identity(Matrix<T, Device::CPU>& matrix) {
-
243  set(matrix, [](const GlobalElementIndex& ij) {
-
244  if (ij.row() == ij.col())
-
245  return T{1};
-
246  return T{0};
-
247  });
-
248 }
-
249 
-
260 template <class T>
-
261 void set_random(Matrix<T, Device::CPU>& matrix) {
-
262  using pika::execution::thread_stacksize;
-
263 
-
264  const Distribution& dist = matrix.distribution();
-
265  for (auto tile_wrt_local : iterate_range2d(dist.local_nr_tiles())) {
-
266  GlobalTileIndex tile_wrt_global = dist.global_tile_index(tile_wrt_local);
-
267  auto tl_index = dist.global_element_index(tile_wrt_global, {0, 0});
-
268  auto seed = tl_index.col() + tl_index.row() * matrix.size().cols();
-
269 
-
270  using TileType = typename std::decay_t<decltype(matrix)>::TileType;
-
271  auto rnd_f = [seed](TileType&& tile) {
-
272  internal::getter_random<T> random_value(seed);
-
273  for (auto el_idx : iterate_range2d(tile.size())) {
-
274  tile(el_idx) = random_value();
-
275  }
-
276  };
-
277 
-
278  dlaf::internal::transformDetach(dlaf::internal::Policy<Backend::MC>(thread_stacksize::nostack),
-
279  std::move(rnd_f), matrix.readwrite(tile_wrt_local));
-
280  }
-
281 }
-
282 
-
283 namespace internal {
+
185 template <Backend backend, class T, Device D>
+
186 void set0(pika::execution::thread_priority priority, Matrix<T, D>& matrix) {
+
187  set0<backend>(priority, LocalTileIndex(0, 0), matrix.distribution().local_nr_tiles(), matrix);
+
188 }
+
189 
+
191 template <Backend backend, class T, Coord axis, Device D, StoreTransposed storage>
+
192 void set0(pika::execution::thread_priority priority, Panel<axis, T, D, storage>& panel) {
+
193  using dlaf::internal::Policy;
+
194  using pika::execution::thread_stacksize;
+
195  using pika::execution::experimental::start_detached;
+
196 
+
197  for (const auto& tile_idx : panel.iteratorLocal())
+
198  start_detached(panel.readwrite(tile_idx) |
+
199  tile::set0(Policy<backend>(priority, thread_stacksize::nostack)));
+
200 }
+
201 
+
208 template <class T, class ElementGetter>
+
209 void set(Matrix<T, Device::CPU>& matrix, ElementGetter el_f) {
+
210  using pika::execution::thread_stacksize;
+
211 
+
212  const Distribution& dist = matrix.distribution();
+
213  for (auto tile_wrt_local : iterate_range2d(dist.local_nr_tiles())) {
+
214  GlobalTileIndex tile_wrt_global = dist.global_tile_index(tile_wrt_local);
+
215  auto tl_index = dist.global_element_index(tile_wrt_global, {0, 0});
+
216 
+
217  using TileType = typename std::decay_t<decltype(matrix)>::TileType;
+
218  auto set_f = [tl_index, el_f = el_f](const TileType& tile) {
+
219  for (auto el_idx_l : iterate_range2d(tile.size())) {
+
220  GlobalElementIndex el_idx_g(el_idx_l.row() + tl_index.row(), el_idx_l.col() + tl_index.col());
+
221  tile(el_idx_l) = el_f(el_idx_g);
+
222  }
+
223  };
+
224 
+
225  dlaf::internal::transformDetach(dlaf::internal::Policy<Backend::MC>(thread_stacksize::nostack),
+
226  std::move(set_f), matrix.readwrite(tile_wrt_local));
+
227  }
+
228 }
+
229 
+
239 template <class T, class ElementGetter>
+
240 void set(Matrix<T, Device::CPU>& matrix, ElementGetter el_f, const blas::Op op) {
+
241  auto el_op_f = [op, el_f](const GlobalElementIndex& index) -> T {
+
242  using blas::Op;
+
243  switch (op) {
+
244  case Op::NoTrans:
+
245  return el_f(index);
+
246  case Op::Trans:
+
247  return el_f(transposed(index));
+
248  case Op::ConjTrans:
+
249  return dlaf::conj(el_f(transposed(index)));
+
250  default:
+
251  DLAF_UNIMPLEMENTED(op);
+
252  return T{};
+
253  }
+
254  };
+
255 
+
256  set(matrix, el_op_f);
+
257 }
+
258 
+
262 template <class T>
+
263 void set_identity(Matrix<T, Device::CPU>& matrix) {
+
264  set(matrix, [](const GlobalElementIndex& ij) {
+
265  if (ij.row() == ij.col())
+
266  return T{1};
+
267  return T{0};
+
268  });
+
269 }
+
270 
+
281 template <class T>
+
282 void set_random(Matrix<T, Device::CPU>& matrix) {
+
283  using pika::execution::thread_stacksize;
284 
-
285 template <class T>
-
286 void set_diagonal_tile(const Tile<T, Device::CPU>& tile, internal::getter_random<T>& random_value,
-
287  SizeType offset_value) {
-
288  // DIAGONAL
-
289  // for diagonal tiles get just lower matrix values and set value for both
-
290  // straight and transposed indices
-
291  for (SizeType j = 0; j < tile.size().cols(); ++j) {
-
292  for (SizeType i = 0; i < j; ++i) {
-
293  auto value = random_value();
-
294 
-
295  tile(TileElementIndex{i, j}) = value;
-
296  tile(TileElementIndex{j, i}) = dlaf::conj(value);
-
297  }
-
298  tile(TileElementIndex{j, j}) = std::real(random_value()) + offset_value;
-
299  }
-
300 }
-
301 
-
302 template <class T>
-
303 void set_lower_and_upper_tile(const Tile<T, Device::CPU>& tile, internal::getter_random<T>& random_value,
-
304  TileElementSize full_tile_size, GlobalTileIndex tile_wrt_global,
-
305  dlaf::matrix::Distribution dist,
-
306  std::optional<SizeType> band_size = std::nullopt) {
-
307  auto is_off_band = [](GlobalElementIndex ij, std::optional<SizeType> band_size) {
-
308  return band_size ? ij.col() < ij.row() - *band_size || ij.col() > ij.row() + *band_size : false;
-
309  };
-
310 
-
311  // LOWER or UPPER (except DIAGONAL)
-
312  // random values are requested in the same order for both original and transposed
-
313  for (SizeType j = 0; j < full_tile_size.cols(); ++j) {
-
314  for (SizeType i = 0; i < full_tile_size.rows(); ++i) {
-
315  auto value = random_value();
-
316 
-
317  // but they are set row-wise in the original tile and col-wise in the
-
318  // transposed one
-
319  if (tile_wrt_global.row() > tile_wrt_global.col()) { // LOWER
-
320  TileElementIndex index{i, j};
-
321  if (index.isIn(tile.size())) {
-
322  auto ij = dist.globalElementIndex(tile_wrt_global, index);
-
323  if (is_off_band(ij, band_size))
-
324  tile(index) = T(0);
-
325  else
-
326  tile(index) = value;
-
327  }
-
328  }
-
329  else { // UPPER
-
330  TileElementIndex index{j, i};
-
331  if (index.isIn(tile.size())) {
-
332  auto ij = dist.globalElementIndex(tile_wrt_global, index);
-
333  if (is_off_band(ij, band_size))
-
334  tile(index) = T(0);
-
335  else
-
336  tile(index) = dlaf::conj(value);
-
337  }
-
338  }
-
339  }
-
340  }
-
341 }
-
342 
-
359 template <class T>
-
360 void set_random_hermitian_with_offset(Matrix<T, Device::CPU>& matrix, const SizeType offset_value,
-
361  std::optional<SizeType> band_size = std::nullopt) {
-
362  using pika::execution::thread_stacksize;
+
285  const Distribution& dist = matrix.distribution();
+
286  for (auto tile_wrt_local : iterate_range2d(dist.local_nr_tiles())) {
+
287  GlobalTileIndex tile_wrt_global = dist.global_tile_index(tile_wrt_local);
+
288  auto tl_index = dist.global_element_index(tile_wrt_global, {0, 0});
+
289  auto seed = tl_index.col() + tl_index.row() * matrix.size().cols();
+
290 
+
291  using TileType = typename std::decay_t<decltype(matrix)>::TileType;
+
292  auto rnd_f = [seed](TileType&& tile) {
+
293  internal::getter_random<T> random_value(seed);
+
294  for (auto el_idx : iterate_range2d(tile.size())) {
+
295  tile(el_idx) = random_value();
+
296  }
+
297  };
+
298 
+
299  dlaf::internal::transformDetach(dlaf::internal::Policy<Backend::MC>(thread_stacksize::nostack),
+
300  std::move(rnd_f), matrix.readwrite(tile_wrt_local));
+
301  }
+
302 }
+
303 
+
304 namespace internal {
+
305 
+
306 template <class T>
+
307 void set_diagonal_tile(const Tile<T, Device::CPU>& tile, internal::getter_random<T>& random_value,
+
308  SizeType offset_value) {
+
309  // DIAGONAL
+
310  // for diagonal tiles get just lower matrix values and set value for both
+
311  // straight and transposed indices
+
312  for (SizeType j = 0; j < tile.size().cols(); ++j) {
+
313  for (SizeType i = 0; i < j; ++i) {
+
314  auto value = random_value();
+
315 
+
316  tile(TileElementIndex{i, j}) = value;
+
317  tile(TileElementIndex{j, i}) = dlaf::conj(value);
+
318  }
+
319  tile(TileElementIndex{j, j}) = std::real(random_value()) + offset_value;
+
320  }
+
321 }
+
322 
+
323 template <class T>
+
324 void set_lower_and_upper_tile(const Tile<T, Device::CPU>& tile, internal::getter_random<T>& random_value,
+
325  TileElementSize full_tile_size, GlobalTileIndex tile_wrt_global,
+
326  dlaf::matrix::Distribution dist,
+
327  std::optional<SizeType> band_size = std::nullopt) {
+
328  auto is_off_band = [](GlobalElementIndex ij, std::optional<SizeType> band_size) {
+
329  return band_size ? ij.col() < ij.row() - *band_size || ij.col() > ij.row() + *band_size : false;
+
330  };
+
331 
+
332  // LOWER or UPPER (except DIAGONAL)
+
333  // random values are requested in the same order for both original and transposed
+
334  for (SizeType j = 0; j < full_tile_size.cols(); ++j) {
+
335  for (SizeType i = 0; i < full_tile_size.rows(); ++i) {
+
336  auto value = random_value();
+
337 
+
338  // but they are set row-wise in the original tile and col-wise in the
+
339  // transposed one
+
340  if (tile_wrt_global.row() > tile_wrt_global.col()) { // LOWER
+
341  TileElementIndex index{i, j};
+
342  if (index.isIn(tile.size())) {
+
343  auto ij = dist.globalElementIndex(tile_wrt_global, index);
+
344  if (is_off_band(ij, band_size))
+
345  tile(index) = T(0);
+
346  else
+
347  tile(index) = value;
+
348  }
+
349  }
+
350  else { // UPPER
+
351  TileElementIndex index{j, i};
+
352  if (index.isIn(tile.size())) {
+
353  auto ij = dist.globalElementIndex(tile_wrt_global, index);
+
354  if (is_off_band(ij, band_size))
+
355  tile(index) = T(0);
+
356  else
+
357  tile(index) = dlaf::conj(value);
+
358  }
+
359  }
+
360  }
+
361  }
+
362 }
363 
-
364  // note:
-
365  // By assuming square blocksizes, it is easier to locate elements. In fact:
-
366  // - Elements on the diagonal are stored in the diagonal of the diagonal tiles
-
367  // - Tiles under the diagonal store elements of the lower triangular matrix
-
368  // - Tiles over the diagonal store elements of the upper triangular matrix
-
369 
-
370  const Distribution& dist = matrix.distribution();
-
371 
-
372  DLAF_ASSERT(square_size(matrix), matrix);
-
373  DLAF_ASSERT(square_blocksize(matrix), matrix);
-
374 
-
375  auto full_tile_size = matrix.block_size();
-
376 
-
377  for (auto tile_wrt_local : iterate_range2d(dist.local_nr_tiles())) {
-
378  GlobalTileIndex tile_wrt_global = dist.global_tile_index(tile_wrt_local);
-
379 
-
380  auto tl_index = dist.global_element_index(tile_wrt_global, {0, 0});
-
381 
-
382  // compute the same seed for original and "transposed" tiles, so transposed ones will know the
-
383  // values of the original one without the need of accessing real values (nor communication in case
-
384  // of distributed matrices)
-
385  SizeType seed;
-
386  if (tile_wrt_global.row() >= tile_wrt_global.col()) // LOWER or DIAGONAL
-
387  seed = tl_index.col() + tl_index.row() * matrix.size().cols();
-
388  else
-
389  seed = tl_index.row() + tl_index.col() * matrix.size().rows();
+
380 template <class T>
+
381 void set_random_hermitian_with_offset(Matrix<T, Device::CPU>& matrix, const SizeType offset_value,
+
382  std::optional<SizeType> band_size = std::nullopt) {
+
383  using pika::execution::thread_stacksize;
+
384 
+
385  // note:
+
386  // By assuming square blocksizes, it is easier to locate elements. In fact:
+
387  // - Elements on the diagonal are stored in the diagonal of the diagonal tiles
+
388  // - Tiles under the diagonal store elements of the lower triangular matrix
+
389  // - Tiles over the diagonal store elements of the upper triangular matrix
390 
-
391  using TileType = typename std::decay_t<decltype(matrix)>::TileType;
-
392  auto set_hp_f = [=](const TileType& tile) {
-
393  internal::getter_random<T> random_value(seed);
-
394  if (tile_wrt_global.row() == tile_wrt_global.col())
-
395  internal::set_diagonal_tile(tile, random_value, offset_value);
-
396  else
-
397  internal::set_lower_and_upper_tile(tile, random_value, full_tile_size, tile_wrt_global, dist,
-
398  band_size);
-
399  };
+
391  const Distribution& dist = matrix.distribution();
+
392 
+
393  DLAF_ASSERT(square_size(matrix), matrix);
+
394  DLAF_ASSERT(square_blocksize(matrix), matrix);
+
395 
+
396  auto full_tile_size = matrix.block_size();
+
397 
+
398  for (auto tile_wrt_local : iterate_range2d(dist.local_nr_tiles())) {
+
399  GlobalTileIndex tile_wrt_global = dist.global_tile_index(tile_wrt_local);
400 
-
401  dlaf::internal::transformDetach(dlaf::internal::Policy<Backend::MC>(thread_stacksize::nostack),
-
402  std::move(set_hp_f), matrix.readwrite(tile_wrt_local));
-
403  }
-
404 }
-
405 
-
406 }
-
407 
-
421 template <class T>
-
422 void set_random_hermitian(Matrix<T, Device::CPU>& matrix) {
-
423  internal::set_random_hermitian_with_offset(matrix, 0);
-
424 }
-
425 
-
439 template <class T>
-
440 void set_random_hermitian_banded(Matrix<T, Device::CPU>& matrix, const SizeType band_size) {
-
441  internal::set_random_hermitian_with_offset(matrix, 0, band_size);
-
442 }
-
443 
+
401  auto tl_index = dist.global_element_index(tile_wrt_global, {0, 0});
+
402 
+
403  // compute the same seed for original and "transposed" tiles, so transposed ones will know the
+
404  // values of the original one without the need of accessing real values (nor communication in case
+
405  // of distributed matrices)
+
406  SizeType seed;
+
407  if (tile_wrt_global.row() >= tile_wrt_global.col()) // LOWER or DIAGONAL
+
408  seed = tl_index.col() + tl_index.row() * matrix.size().cols();
+
409  else
+
410  seed = tl_index.row() + tl_index.col() * matrix.size().rows();
+
411 
+
412  using TileType = typename std::decay_t<decltype(matrix)>::TileType;
+
413  auto set_hp_f = [=](const TileType& tile) {
+
414  internal::getter_random<T> random_value(seed);
+
415  if (tile_wrt_global.row() == tile_wrt_global.col())
+
416  internal::set_diagonal_tile(tile, random_value, offset_value);
+
417  else
+
418  internal::set_lower_and_upper_tile(tile, random_value, full_tile_size, tile_wrt_global, dist,
+
419  band_size);
+
420  };
+
421 
+
422  dlaf::internal::transformDetach(dlaf::internal::Policy<Backend::MC>(thread_stacksize::nostack),
+
423  std::move(set_hp_f), matrix.readwrite(tile_wrt_local));
+
424  }
+
425 }
+
426 
+
427 }
+
428 
+
442 template <class T>
+
443 void set_random_hermitian(Matrix<T, Device::CPU>& matrix) {
+
444  internal::set_random_hermitian_with_offset(matrix, 0);
+
445 }
+
446 
460 template <class T>
-
461 void set_random_hermitian_positive_definite(Matrix<T, Device::CPU>& matrix) {
-
462  internal::set_random_hermitian_with_offset(matrix, 2 * matrix.size().rows());
+
461 void set_random_hermitian_banded(Matrix<T, Device::CPU>& matrix, const SizeType band_size) {
+
462  internal::set_random_hermitian_with_offset(matrix, 0, band_size);
463 }
464 
-
465 }
-
466 }
-
467 }
+
481 template <class T>
+
482 void set_random_hermitian_positive_definite(Matrix<T, Device::CPU>& matrix) {
+
483  internal::set_random_hermitian_with_offset(matrix, 2 * matrix.size().rows());
+
484 }
+
485 
+
486 }
+
487 }
+
488 }
enum_output.h
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
@@ -443,7 +463,7 @@
dlaf::matrix::Matrix
Definition: matrix.h:56
dlaf::matrix::Matrix::readwrite
ReadWriteSenderType readwrite(const LocalTileIndex &index) noexcept
Definition: matrix.h:147
dlaf::matrix::Tile
Definition: tile.h:329
-
dlaf::matrix::util::internal::getter_random
Callable that returns random values in the range [-1, 1].
Definition: util_matrix.h:113
+
dlaf::matrix::util::internal::getter_random
Callable that returns random values in the range [-1, 1].
Definition: util_matrix.h:134
@@ -453,23 +473,24 @@
dlaf::matrix::Panel::readwrite
ReadWriteSenderType readwrite(LocalTileIndex index)
Definition: panel.h:570
dlaf::conj
std::complex< T > conj(const std::complex< T > number)
Return complex conjugate of a complex number.
Definition: types.h:165
+
dlaf::matrix::multipliable
bool multipliable(const MatrixLikeA< const T, D > &a, const MatrixLikeB< const T, D > &b, const MatrixLikeC< const T, D > &c, const blas::Op opA, const blas::Op opB) noexcept
Returns true if matrices a, b and c have matrix multipliable sizes and block sizes.
Definition: util_matrix.h:110
dlaf::matrix::equal_blocksize
bool equal_blocksize(const Matrix< const T, D1 > &lhs, Matrix< const T, D2 > &rhs) noexcept
Returns true if matrices have equal blocksizes.
Definition: util_matrix.h:66
-
dlaf::matrix::multipliable
bool multipliable(const Matrix< const T, D > &a, const Matrix< const T, D > &b, const Matrix< const T, D > &c, const blas::Op opA, const blas::Op opB) noexcept
Returns true if matrices a, b and c have matrix multipliable sizes and block sizes.
Definition: util_matrix.h:102
-
dlaf::matrix::util::set
void set(Matrix< T, Device::CPU > &matrix, ElementGetter el_f)
Definition: util_matrix.h:188
-
dlaf::matrix::util::set_random_hermitian_banded
void set_random_hermitian_banded(Matrix< T, Device::CPU > &matrix, const SizeType band_size)
Definition: util_matrix.h:440
-
dlaf::matrix::util::set_identity
void set_identity(Matrix< T, Device::CPU > &matrix)
Definition: util_matrix.h:242
+
dlaf::matrix::util::set
void set(Matrix< T, Device::CPU > &matrix, ElementGetter el_f)
Definition: util_matrix.h:209
+
dlaf::matrix::util::set_random_hermitian_banded
void set_random_hermitian_banded(Matrix< T, Device::CPU > &matrix, const SizeType band_size)
Definition: util_matrix.h:461
+
dlaf::matrix::util::set_identity
void set_identity(Matrix< T, Device::CPU > &matrix)
Definition: util_matrix.h:263
dlaf::matrix::square_blocksize
bool square_blocksize(const MatrixLike &m) noexcept
Returns true if the matrix block size is square.
Definition: util_matrix.h:48
-
dlaf::matrix::util::set_random_hermitian
void set_random_hermitian(Matrix< T, Device::CPU > &matrix)
Definition: util_matrix.h:422
+
dlaf::matrix::util::set_random_hermitian
void set_random_hermitian(Matrix< T, Device::CPU > &matrix)
Definition: util_matrix.h:443
dlaf::matrix::single_tile_per_block
bool single_tile_per_block(const MatrixLike &m) noexcept
Returns true if the matrix has a single tile per block.
Definition: util_matrix.h:54
-
dlaf::matrix::util::internal::set_random_hermitian_with_offset
void set_random_hermitian_with_offset(Matrix< T, Device::CPU > &matrix, const SizeType offset_value, std::optional< SizeType > band_size=std::nullopt)
Definition: util_matrix.h:360
+
dlaf::matrix::util::internal::set_random_hermitian_with_offset
void set_random_hermitian_with_offset(Matrix< T, Device::CPU > &matrix, const SizeType offset_value, std::optional< SizeType > band_size=std::nullopt)
Definition: util_matrix.h:381
dlaf::matrix::equal_process_grid
bool equal_process_grid(const Matrix< const T, D > &m, const comm::CommunicatorGrid &g) noexcept
Returns true if the matrix is distributed on the communication grid.
Definition: util_matrix.h:78
-
dlaf::matrix::local_matrix
bool local_matrix(const Matrix< const T, D > &m) noexcept
Returns true if the matrix is local to a process.
Definition: util_matrix.h:72
-
dlaf::matrix::equal_distributions
bool equal_distributions(const Matrix< const T, D1 > &lhs, const Matrix< const T, D2 > &rhs) noexcept
Returns true if the matrices are distributed the same way.
Definition: util_matrix.h:84
-
dlaf::matrix::util::set_random
void set_random(Matrix< T, Device::CPU > &matrix)
Definition: util_matrix.h:261
-
dlaf::matrix::multipliable_sizes
bool multipliable_sizes(common::Size2D< IndexT, Tag > a, common::Size2D< IndexT, Tag > b, common::Size2D< IndexT, Tag > c, const blas::Op opA, const blas::Op opB) noexcept
Returns true if the sizes are compatible for matrix multiplication.
Definition: util_matrix.h:90
+
dlaf::matrix::same_process_grid
bool same_process_grid(const MatrixLikeA< const T, D > &a, const MatrixLikeB< const T, D > &b) noexcept
Returns true if the matrix is distributed on the communication grid.
Definition: util_matrix.h:85
+
dlaf::matrix::equal_distributions
bool equal_distributions(const Matrix< const T, D1 > &lhs, const Matrix< const T, D2 > &rhs) noexcept
Returns true if the matrices are distributed the same way.
Definition: util_matrix.h:91
+
dlaf::matrix::util::set_random
void set_random(Matrix< T, Device::CPU > &matrix)
Definition: util_matrix.h:282
+
dlaf::matrix::local_matrix
bool local_matrix(const MatrixLike< const T, D > &m) noexcept
Returns true if the matrix is local to a process.
Definition: util_matrix.h:72
+
dlaf::matrix::multipliable_sizes
bool multipliable_sizes(common::Size2D< IndexT, Tag > a, common::Size2D< IndexT, Tag > b, common::Size2D< IndexT, Tag > c, const blas::Op opA, const blas::Op opB) noexcept
Returns true if the sizes are compatible for matrix multiplication.
Definition: util_matrix.h:97
dlaf::matrix::square_size
bool square_size(const MatrixLike &m) noexcept
Returns true if the matrix is square.
Definition: util_matrix.h:42
dlaf::matrix::equal_size
bool equal_size(const MatrixLikeA &lhs, const MatrixLikeB &rhs) noexcept
Returns true if matrices have equal sizes.
Definition: util_matrix.h:60
-
dlaf::matrix::util::set_random_hermitian_positive_definite
void set_random_hermitian_positive_definite(Matrix< T, Device::CPU > &matrix)
Definition: util_matrix.h:461
+
dlaf::matrix::util::set_random_hermitian_positive_definite
void set_random_hermitian_positive_definite(Matrix< T, Device::CPU > &matrix)
Definition: util_matrix.h:482