diff --git a/master/band__to__tridiag_8h.html b/master/band__to__tridiag_8h.html index 2582c828e4..3488d56c9e 100644 --- a/master/band__to__tridiag_8h.html +++ b/master/band__to__tridiag_8h.html @@ -145,15 +145,15 @@

Precondition
mat_a has a square size,
+
Precondition
mat_a is not distributed
-mat_a has a square block size,
+mat_a has size (N x N)
-band_size is a divisor of mat_a.blockSize().cols(), and band_size >= 2
+mat_a has blocksize (NB x NB)
-mat_a is not distributed,
+mat_a has tilesize (NB x NB)
-mat_a has equal tile and block sizes.
+band_size is a divisor of mat_a.blockSize().cols(), and band_size >= 2 @@ -217,15 +217,15 @@

Precondition
mat_a has a square size,
+
Precondition
mat_a is distributed according to grid
-mat_a has a square block size,
+mat_a has size (N x N)
-band_size is a divisor of mat_a.blockSize().cols() and band_size >= 2,
+mat_a has blocksize (NB x NB)
-mat_a is distributed according to grid,
+mat_a has tilesize (NB x NB)
-mat_a has equal tile and block sizes.
+band_size is a divisor of mat_a.blockSize().cols(), and band_size >= 2 diff --git a/master/band__to__tridiag_8h_source.html b/master/band__to__tridiag_8h_source.html index d774b74304..eafa399c31 100644 --- a/master/band__to__tridiag_8h_source.html +++ b/master/band__to__tridiag_8h_source.html @@ -92,61 +92,61 @@
22 
23 namespace dlaf::eigensolver::internal {
24 
-
73 template <Backend B, Device D, class T>
-
74 TridiagResult<T, Device::CPU> band_to_tridiagonal(blas::Uplo uplo, SizeType band_size,
-
75  Matrix<const T, D>& mat_a) {
-
76  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
77  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
78  DLAF_ASSERT(mat_a.blockSize().rows() % band_size == 0, mat_a.blockSize().rows(), band_size);
-
79  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
-
80  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
81  DLAF_ASSERT(band_size >= 2, band_size);
-
82 
-
83  switch (uplo) {
-
84  case blas::Uplo::Lower:
-
85  return BandToTridiag<B, D, T>::call_L(band_size, mat_a);
-
86  break;
-
87  case blas::Uplo::Upper:
-
88  DLAF_UNIMPLEMENTED(uplo);
-
89  break;
-
90  case blas::Uplo::General:
-
91  DLAF_UNIMPLEMENTED(uplo);
-
92  break;
-
93  }
-
94 
-
95  return DLAF_UNREACHABLE(TridiagResult<T, Device::CPU>);
-
96 }
-
97 
-
146 template <Backend backend, Device device, class T>
-
147 TridiagResult<T, Device::CPU> band_to_tridiagonal(comm::CommunicatorGrid grid, blas::Uplo uplo,
-
148  SizeType band_size, Matrix<const T, device>& mat_a) {
-
149  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
150  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
151  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
-
152  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
153  DLAF_ASSERT(band_size >= 2, band_size);
-
154 
-
155  // If the grid contains only one rank force local implementation.
-
156  if (grid.size() == comm::Size2D(1, 1))
-
157  return band_to_tridiagonal<backend, device, T>(uplo, band_size, mat_a);
-
158 
-
159  switch (uplo) {
-
160  case blas::Uplo::Lower:
-
161  return BandToTridiag<backend, device, T>::call_L(grid, band_size, mat_a);
-
162  break;
-
163  case blas::Uplo::Upper:
-
164  DLAF_UNIMPLEMENTED(uplo);
-
165  break;
-
166  case blas::Uplo::General:
-
167  DLAF_UNIMPLEMENTED(uplo);
-
168  break;
-
169  }
-
170 
-
171  return DLAF_UNREACHABLE(TridiagResult<T, Device::CPU>);
-
172 }
-
173 
+
74 template <Backend B, Device D, class T>
+
75 TridiagResult<T, Device::CPU> band_to_tridiagonal(blas::Uplo uplo, SizeType band_size,
+
76  Matrix<const T, D>& mat_a) {
+
77  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
78  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
79  DLAF_ASSERT(mat_a.blockSize().rows() % band_size == 0, mat_a.blockSize().rows(), band_size);
+
80  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
81  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
82  DLAF_ASSERT(band_size >= 2, band_size);
+
83 
+
84  switch (uplo) {
+
85  case blas::Uplo::Lower:
+
86  return BandToTridiag<B, D, T>::call_L(band_size, mat_a);
+
87  break;
+
88  case blas::Uplo::Upper:
+
89  DLAF_UNIMPLEMENTED(uplo);
+
90  break;
+
91  case blas::Uplo::General:
+
92  DLAF_UNIMPLEMENTED(uplo);
+
93  break;
+
94  }
+
95 
+
96  return DLAF_UNREACHABLE(TridiagResult<T, Device::CPU>);
+
97 }
+
98 
+
148 template <Backend backend, Device device, class T>
+
149 TridiagResult<T, Device::CPU> band_to_tridiagonal(comm::CommunicatorGrid grid, blas::Uplo uplo,
+
150  SizeType band_size, Matrix<const T, device>& mat_a) {
+
151  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
152  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
153  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
+
154  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
155  DLAF_ASSERT(band_size >= 2, band_size);
+
156 
+
157  // If the grid contains only one rank force local implementation.
+
158  if (grid.size() == comm::Size2D(1, 1))
+
159  return band_to_tridiagonal<backend, device, T>(uplo, band_size, mat_a);
+
160 
+
161  switch (uplo) {
+
162  case blas::Uplo::Lower:
+
163  return BandToTridiag<backend, device, T>::call_L(grid, band_size, mat_a);
+
164  break;
+
165  case blas::Uplo::Upper:
+
166  DLAF_UNIMPLEMENTED(uplo);
+
167  break;
+
168  case blas::Uplo::General:
+
169  DLAF_UNIMPLEMENTED(uplo);
+
170  break;
+
171  }
+
172 
+
173  return DLAF_UNREACHABLE(TridiagResult<T, Device::CPU>);
174 }
-
dlaf::eigensolver::internal::band_to_tridiagonal
TridiagResult< T, Device::CPU > band_to_tridiagonal(blas::Uplo uplo, SizeType band_size, Matrix< const T, D > &mat_a)
Definition: band_to_tridiag.h:74
+
175 
+
176 }
+
dlaf::eigensolver::internal::band_to_tridiagonal
TridiagResult< T, Device::CPU > band_to_tridiagonal(blas::Uplo uplo, SizeType band_size, Matrix< const T, D > &mat_a)
Definition: band_to_tridiag.h:75
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
dlaf::comm::CommunicatorGrid::size
Size2D size() const noexcept
Return the number of rows in the grid.
Definition: communicator_grid.h:68
diff --git a/master/bt__band__to__tridiag_8h.html b/master/bt__band__to__tridiag_8h.html index d181a03754..696114b1d1 100644 --- a/master/bt__band__to__tridiag_8h.html +++ b/master/bt__band__to__tridiag_8h.html @@ -80,15 +80,168 @@ - - + + - - + +

Functions

-template<Backend B, Device D, class T >
void dlaf::eigensolver::internal::bt_band_to_tridiagonal (const SizeType band_size, matrix::Matrix< T, D > &mat_e, matrix::Matrix< const T, Device::CPU > &mat_hh)
template<Backend B, Device D, class T >
void dlaf::eigensolver::internal::bt_band_to_tridiagonal (const SizeType band_size, matrix::Matrix< T, D > &mat_e, matrix::Matrix< const T, Device::CPU > &mat_hh)
 
-template<Backend B, Device D, class T >
void dlaf::eigensolver::internal::bt_band_to_tridiagonal (comm::CommunicatorGrid grid, const SizeType band_size, matrix::Matrix< T, D > &mat_e, matrix::Matrix< const T, Device::CPU > &mat_hh)
template<Backend B, Device D, class T >
void dlaf::eigensolver::internal::bt_band_to_tridiagonal (comm::CommunicatorGrid grid, const SizeType band_size, matrix::Matrix< T, D > &mat_e, matrix::Matrix< const T, Device::CPU > &mat_hh)
 
+

Function Documentation

+ +

◆ bt_band_to_tridiagonal() [1/2]

+ +
+
+
+template<Backend B, Device D, class T >
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
void dlaf::eigensolver::internal::bt_band_to_tridiagonal (comm::CommunicatorGrid grid,
const SizeType band_size,
matrix::Matrix< T, D > & mat_e,
matrix::Matrix< const T, Device::CPU > & mat_hh 
)
+
+

Eigenvalue back-transformation implementation, which applies the inverse of the transformation used to get a tridiagonal matrix from a band one.

+

It computes E -= V T V* E, applying to a general matrix E the inverse of the transformation described by the reflectors in V (block-wise, so T represents the T factor which embeds the information about taus), which are the ones used to transform a band matrix to a tridiagonal matrix.

+

In particular, V and T are obtained using data about reflectors and taus passed via mat_hh where they are stored using following compact representation

+

compact extended AT BT CT DT 1 0 0 0 A1 B1 C1 D1 A1 1 0 0 A2 B2 C2 D2 A2 B1 1 0 A3 B3 C3 D3 A3 B2 C1 1 0 B3 C2 D1 0 0 C3 D2 0 0 0 D3

+

where A, B, C and D refers to distinct reflectors, with their components numbered and their taus identified by the letter T.

+
Parameters
+ + +
mat_hhmatrix containing reflectors together with taus (compact form see representation above)
+
+
+
Precondition
mat_hh is distributed according to grid
+
+mat_hh has size (N x N)
+
+mat_hh has blocksize (NB x NB)
+
+mat_hh has tilesize (NB x NB)
+
Parameters
+ + +
mat_ematrix to which the inverse transformation is applied to
+
+
+
Precondition
mat_e is distributed according to grid
+
+mat_e has size (N x M)
+
+mat_e has blocksize (NB x MB)
+
+mat_e has tilesize (NB x MB)
+
Parameters
+ + +
band_sizesize of the reflectors (normal one, not constrained by any matrix size limit)
+
+
+
Precondition
band_size is a divisor of mat_hh.blockSize().cols()
+ +
+
+ +

◆ bt_band_to_tridiagonal() [2/2]

+ +
+
+
+template<Backend B, Device D, class T >
+ + + + + + + + + + + + + + + + + + + + + + + + +
void dlaf::eigensolver::internal::bt_band_to_tridiagonal (const SizeType band_size,
matrix::Matrix< T, D > & mat_e,
matrix::Matrix< const T, Device::CPU > & mat_hh 
)
+
+

Eigenvalue back-transformation implementation on local memory, which applies the inverse of the transformation used to get a tridiagonal matrix from a band one.

+

It computes E -= V T V* E, applying to a general matrix E the inverse of the transformation described by the reflectors in V (block-wise, so T represents the T factor which embeds the information about taus), which are the ones used to transform a band matrix to a tridiagonal matrix.

+

In particular, V and T are obtained using data about reflectors and taus passed via mat_hh where they are stored using following compact representation

+

compact extended AT BT CT DT 1 0 0 0 A1 B1 C1 D1 A1 1 0 0 A2 B2 C2 D2 A2 B1 1 0 A3 B3 C3 D3 A3 B2 C1 1 0 B3 C2 D1 0 0 C3 D2 0 0 0 D3

+

where A, B, C and D refers to distinct reflectors, with their components numbered and their taus identified by the letter T.

+
Parameters
+ + +
mat_hhmatrix containing reflectors together with taus (compact form see representation above)
+
+
+
Precondition
mat_hh is not distributed
+
+mat_hh has size (N x N)
+
+mat_hh has blocksize (NB x NB)
+
+mat_hh has tilesize (NB x NB)
+
Parameters
+ + +
mat_ematrix to which the inverse transformation is applied to
+
+
+
Precondition
mat_e is not distributed
+
+mat_e has size (N x M)
+
+mat_e has blocksize (NB x MB)
+
+mat_e has tilesize (NB x MB)
+
Parameters
+ + +
band_sizesize of the reflectors (normal one, not constrained by any matrix size limit)
+
+
+
Precondition
band_size is a divisor of mat_hh.blockSize().cols()
+ +
+
diff --git a/master/bt__band__to__tridiag_8h_source.html b/master/bt__band__to__tridiag_8h_source.html index e2624bacc1..1401c9f7a4 100644 --- a/master/bt__band__to__tridiag_8h_source.html +++ b/master/bt__band__to__tridiag_8h_source.html @@ -89,80 +89,50 @@
19 
20 namespace dlaf::eigensolver::internal {
21 
-
22 // Eigenvalue back-transformation implementation on local memory, which applies the inverse of the
-
23 // transformation used to get a tridiagonal matrix from a band one.
-
24 //
-
25 // It computes E -= V T V* E, applying to a general matrix E the inverse of the transformation described
-
26 // by the reflectors in V (block-wise, so T represents the T factor which embeds the information about
-
27 // taus), which are the ones used to transform a band matrix to a tridiagonal matrix.
-
28 //
-
29 // In particular, V and T are obtained using data about reflectors and taus passed via @p mat_hh
-
30 // where they are stored using following compact representation
-
31 //
-
32 // compact extended
-
33 // AT BT CT DT 1 0 0 0
-
34 // A1 B1 C1 D1 A1 1 0 0
-
35 // A2 B2 C2 D2 A2 B1 1 0
-
36 // A3 B3 C3 D3 A3 B2 C1 1
-
37 // 0 B3 C2 D1
-
38 // 0 0 C3 D2
-
39 // 0 0 0 D3
-
40 //
-
41 // where A, B, C and D refers to distinct reflectors, with their components numbered and their taus
-
42 // identified by the letter T.
-
43 //
-
44 // @param mat_hh matrix containing reflectors together with taus (compact form see representation above)
-
45 // @param mat_e matrix to which the inverse transformation is applied to
-
46 // @param band_size size of the reflectors (normal one, not constrained by any matrix size limit)
-
47 // @pre mat_hh has a square size
-
48 // @pre mat_hh has a square block size
-
49 // @pre mat_e and mat_hh share the same number of rows
-
50 // @pre mat_e block size and mat_hh block size share the same number of rows
-
51 // @pre band_size is a divisor of mat_hh.blockSize().cols()
-
52 // @pre mat_e is not distributed
-
53 // @pre mat_hh is not distributed
-
54 // @pre mat_e has equal tile and block sizes
-
55 // @pre mat_hh has equal tile and block sizes
-
56 template <Backend B, Device D, class T>
-
57 void bt_band_to_tridiagonal(const SizeType band_size, matrix::Matrix<T, D>& mat_e,
-
58  matrix::Matrix<const T, Device::CPU>& mat_hh) {
-
59  DLAF_ASSERT(matrix::local_matrix(mat_e), mat_e);
-
60  DLAF_ASSERT(matrix::local_matrix(mat_hh), mat_hh);
-
61 
-
62  DLAF_ASSERT(matrix::square_size(mat_hh), mat_hh);
-
63  DLAF_ASSERT(matrix::square_blocksize(mat_hh), mat_hh);
-
64 
-
65  DLAF_ASSERT(mat_hh.size().rows() == mat_e.size().rows(), mat_hh, mat_e);
-
66  DLAF_ASSERT(mat_hh.blockSize().rows() == mat_e.blockSize().rows(), mat_hh, mat_e);
-
67 
-
68  DLAF_ASSERT(matrix::single_tile_per_block(mat_e), mat_e);
-
69  DLAF_ASSERT(matrix::single_tile_per_block(mat_hh), mat_hh);
-
70 
-
71  DLAF_ASSERT(band_size >= 2, band_size);
-
72  DLAF_ASSERT(mat_hh.blockSize().rows() % band_size == 0, mat_hh.blockSize(), band_size);
-
73 
-
74  BackTransformationT2B<B, D, T>::call(band_size, mat_e, mat_hh);
-
75 }
-
76 
-
77 template <Backend B, Device D, class T>
-
78 void bt_band_to_tridiagonal(comm::CommunicatorGrid grid, const SizeType band_size,
-
79  matrix::Matrix<T, D>& mat_e, matrix::Matrix<const T, Device::CPU>& mat_hh) {
-
80  DLAF_ASSERT(matrix::equal_process_grid(mat_e, grid), mat_e, grid);
-
81  DLAF_ASSERT(matrix::equal_process_grid(mat_hh, grid), mat_hh, grid);
-
82 
-
83  DLAF_ASSERT(matrix::square_size(mat_hh), mat_hh);
-
84  DLAF_ASSERT(matrix::square_blocksize(mat_hh), mat_hh);
-
85 
-
86  DLAF_ASSERT(mat_hh.size().rows() == mat_e.size().rows(), mat_hh, mat_e);
-
87  DLAF_ASSERT(mat_hh.blockSize().rows() == mat_e.blockSize().rows(), mat_hh, mat_e);
-
88 
-
89  DLAF_ASSERT(band_size >= 2, band_size);
-
90  DLAF_ASSERT(mat_hh.blockSize().rows() % band_size == 0, mat_hh.blockSize(), band_size);
-
91 
-
92  BackTransformationT2B<B, D, T>::call(grid, band_size, mat_e, mat_hh);
-
93 }
-
94 }
+
58 template <Backend B, Device D, class T>
+
59 void bt_band_to_tridiagonal(const SizeType band_size, matrix::Matrix<T, D>& mat_e,
+
60  matrix::Matrix<const T, Device::CPU>& mat_hh) {
+
61  DLAF_ASSERT(matrix::local_matrix(mat_e), mat_e);
+
62  DLAF_ASSERT(matrix::local_matrix(mat_hh), mat_hh);
+
63 
+
64  DLAF_ASSERT(matrix::square_size(mat_hh), mat_hh);
+
65  DLAF_ASSERT(matrix::square_blocksize(mat_hh), mat_hh);
+
66 
+
67  DLAF_ASSERT(mat_hh.size().rows() == mat_e.size().rows(), mat_hh, mat_e);
+
68  DLAF_ASSERT(mat_hh.blockSize().rows() == mat_e.blockSize().rows(), mat_hh, mat_e);
+
69 
+
70  DLAF_ASSERT(matrix::single_tile_per_block(mat_e), mat_e);
+
71  DLAF_ASSERT(matrix::single_tile_per_block(mat_hh), mat_hh);
+
72 
+
73  DLAF_ASSERT(band_size >= 2, band_size);
+
74  DLAF_ASSERT(mat_hh.blockSize().rows() % band_size == 0, mat_hh.blockSize(), band_size);
+
75 
+
76  BackTransformationT2B<B, D, T>::call(band_size, mat_e, mat_hh);
+
77 }
+
78 
+
115 template <Backend B, Device D, class T>
+
116 void bt_band_to_tridiagonal(comm::CommunicatorGrid grid, const SizeType band_size,
+
117  matrix::Matrix<T, D>& mat_e, matrix::Matrix<const T, Device::CPU>& mat_hh) {
+
118  DLAF_ASSERT(matrix::equal_process_grid(mat_e, grid), mat_e, grid);
+
119  DLAF_ASSERT(matrix::equal_process_grid(mat_hh, grid), mat_hh, grid);
+
120 
+
121  DLAF_ASSERT(matrix::square_size(mat_hh), mat_hh);
+
122  DLAF_ASSERT(matrix::square_blocksize(mat_hh), mat_hh);
+
123 
+
124  DLAF_ASSERT(mat_hh.size().rows() == mat_e.size().rows(), mat_hh, mat_e);
+
125  DLAF_ASSERT(mat_hh.blockSize().rows() == mat_e.blockSize().rows(), mat_hh, mat_e);
+
126 
+
127  DLAF_ASSERT(band_size >= 2, band_size);
+
128  DLAF_ASSERT(mat_hh.blockSize().rows() % band_size == 0, mat_hh.blockSize(), band_size);
+
129 
+
130  BackTransformationT2B<B, D, T>::call(grid, band_size, mat_e, mat_hh);
+
131 }
+
132 }
+
dlaf::eigensolver::internal::bt_band_to_tridiagonal
void bt_band_to_tridiagonal(const SizeType band_size, matrix::Matrix< T, D > &mat_e, matrix::Matrix< const T, Device::CPU > &mat_hh)
Definition: bt_band_to_tridiag.h:59
+
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
+
dlaf::matrix::Matrix
Definition: matrix.h:56
+
util_matrix.h
diff --git a/master/bt__reduction__to__band_8h.html b/master/bt__reduction__to__band_8h.html index c269af5fb8..ca7d080266 100644 --- a/master/bt__reduction__to__band_8h.html +++ b/master/bt__reduction__to__band_8h.html @@ -139,19 +139,32 @@

Parameters
- - - +
mat_ccontains the (m x n) matrix C (blocksize (mb x nb)), while on exit it contains Q C.
mat_vis (m x m) matrix with blocksize (mb x mb), which contains the Householder reflectors. The j-th HH reflector is v_j = (1, V(mb + j : n, j)).
mat_tausis the tau vector as returned by reductionToBand. The j-th element is the scaling factor for the j-th HH tranformation.
mat_ccontains the (m x n) matrix C (blocksize (mb x nb)), while on exit it contains Q C
-
Precondition
mat_c is distributed,
+
Precondition
mat_c is distributed according to grid
+
+mat_c has blocksize (NB x NB)
-mat_v is distributed according to grid,
+mat_c has tilesize (NB x NB)
+
Parameters
+ + +
mat_vis (m x m) matrix with blocksize (mb x mb), which contains the Householder reflectors. The j-th HH reflector is v_j = (1, V(mb + j : n, j)).
+
+
+
Precondition
mat_v is distributed according to grid
-mat_c has equal tile and block sizes,
+mat_v has blocksize (NB x NB)
-mat_v has equal tile and block sizes.
+mat_v has tilesize (NB x NB)
+
Parameters
+ + +
mat_tausis the tau vector as returned by reductionToBand. The j-th element is the scaling factor for the j-th HH tranformation.
+
+
@@ -199,18 +212,31 @@

Parameters
- -
mat_ccontains the (m x n) matrix C (blocksize (mb x nb)), while on exit it contains Q C.
mat_vis (m x m) matrix with blocksize (mb x mb), which contains the Householder reflectors. The j-th HH reflector is v_j = (1, V(mb + j : n, j)).
mat_tausis the tau vector as returned by reductionToBand. The j-th element is the scaling factor for the j-th HH tranformation.
-
Precondition
mat_c is not distributed,
+
Precondition
mat_c is not distributed
-mat_v is not distributed,
+mat_c has blocksize (NB x NB)
-mat_c has equal tile and block sizes,
+mat_c has tilesize (NB x NB)
+
Parameters
+ + +
mat_vis (m x m) matrix with blocksize (mb x mb), which contains the Householder reflectors. The j-th HH reflector is v_j = (1, V(mb + j : n, j)).
+
+
+
Precondition
mat_v is not distributed
-mat_v has equal tile and block sizes.
+mat_v has blocksize (NB x NB) +
+mat_v has tilesize (NB x NB)
+
Parameters
+ + +
mat_tausis the tau vector as returned by reductionToBand. The j-th element is the scaling factor for the j-th HH tranformation.
+
+
diff --git a/master/bt__reduction__to__band_8h_source.html b/master/bt__reduction__to__band_8h_source.html index 460b99926a..ff0c750ad4 100644 --- a/master/bt__reduction__to__band_8h_source.html +++ b/master/bt__reduction__to__band_8h_source.html @@ -91,53 +91,53 @@
21 
22 namespace dlaf::eigensolver::internal {
23 
-
39 template <Backend backend, Device device, class T>
-
40 void bt_reduction_to_band(const SizeType b, Matrix<T, device>& mat_c, Matrix<const T, device>& mat_v,
-
41  Matrix<const T, Device::CPU>& mat_taus) {
-
42  DLAF_ASSERT(matrix::local_matrix(mat_c), mat_c);
-
43  DLAF_ASSERT(matrix::local_matrix(mat_v), mat_v);
-
44  DLAF_ASSERT(square_size(mat_v), mat_v);
-
45  DLAF_ASSERT(square_blocksize(mat_v), mat_v);
-
46  DLAF_ASSERT(mat_c.size().rows() == mat_v.size().rows(), mat_c, mat_v);
-
47  DLAF_ASSERT(mat_c.blockSize().rows() == mat_v.blockSize().rows(), mat_c, mat_v);
-
48  DLAF_ASSERT(single_tile_per_block(mat_c), mat_c);
-
49  DLAF_ASSERT(single_tile_per_block(mat_v), mat_v);
-
50 
-
51  [[maybe_unused]] auto nr_reflectors_blocks = [&b, &mat_v]() {
-
52  const SizeType m = mat_v.size().rows();
-
53  const SizeType mb = mat_v.blockSize().rows();
-
54  return std::max<SizeType>(0, util::ceilDiv(m - b - 1, mb));
-
55  };
-
56  DLAF_ASSERT(mat_taus.nrTiles().rows() == nr_reflectors_blocks(), mat_taus.size().rows(), mat_v, b);
-
57 
-
58  BackTransformationReductionToBand<backend, device, T>::call(b, mat_c, mat_v, mat_taus);
-
59 }
-
60 
-
76 template <Backend backend, Device device, class T>
-
77 void bt_reduction_to_band(comm::CommunicatorGrid grid, const SizeType b, Matrix<T, device>& mat_c,
-
78  Matrix<const T, device>& mat_v, Matrix<const T, Device::CPU>& mat_taus) {
-
79  DLAF_ASSERT(matrix::equal_process_grid(mat_c, grid), mat_c, grid);
-
80  DLAF_ASSERT(matrix::equal_process_grid(mat_v, grid), mat_v, grid);
-
81  DLAF_ASSERT(square_size(mat_v), mat_v);
-
82  DLAF_ASSERT(square_blocksize(mat_v), mat_v);
-
83  DLAF_ASSERT(mat_c.size().rows() == mat_v.size().rows(), mat_c, mat_v);
-
84  DLAF_ASSERT(mat_c.blockSize().rows() == mat_v.blockSize().rows(), mat_c, mat_v);
-
85  DLAF_ASSERT(single_tile_per_block(mat_c), mat_c);
-
86  DLAF_ASSERT(single_tile_per_block(mat_v), mat_v);
-
87 
-
88  [[maybe_unused]] auto nr_reflectors_blocks = [&b, &mat_v]() {
-
89  const SizeType m = mat_v.size().rows();
-
90  const SizeType mb = mat_v.blockSize().rows();
-
91  return mat_v.distribution().template nextLocalTileFromGlobalTile<Coord::Col>(
-
92  std::max<SizeType>(0, util::ceilDiv(m - b - 1, mb)));
-
93  };
-
94  DLAF_ASSERT(mat_taus.distribution().localNrTiles().rows() == nr_reflectors_blocks(), mat_taus, mat_v,
-
95  b);
-
96 
-
97  BackTransformationReductionToBand<backend, device, T>::call(grid, b, mat_c, mat_v, mat_taus);
-
98 }
-
99 }
-
dlaf::eigensolver::internal::bt_reduction_to_band
void bt_reduction_to_band(const SizeType b, Matrix< T, device > &mat_c, Matrix< const T, device > &mat_v, Matrix< const T, Device::CPU > &mat_taus)
Definition: bt_reduction_to_band.h:40
+
43 template <Backend backend, Device device, class T>
+
44 void bt_reduction_to_band(const SizeType b, Matrix<T, device>& mat_c, Matrix<const T, device>& mat_v,
+
45  Matrix<const T, Device::CPU>& mat_taus) {
+
46  DLAF_ASSERT(matrix::local_matrix(mat_c), mat_c);
+
47  DLAF_ASSERT(matrix::local_matrix(mat_v), mat_v);
+
48  DLAF_ASSERT(square_size(mat_v), mat_v);
+
49  DLAF_ASSERT(square_blocksize(mat_v), mat_v);
+
50  DLAF_ASSERT(mat_c.size().rows() == mat_v.size().rows(), mat_c, mat_v);
+
51  DLAF_ASSERT(mat_c.blockSize().rows() == mat_v.blockSize().rows(), mat_c, mat_v);
+
52  DLAF_ASSERT(single_tile_per_block(mat_c), mat_c);
+
53  DLAF_ASSERT(single_tile_per_block(mat_v), mat_v);
+
54 
+
55  [[maybe_unused]] auto nr_reflectors_blocks = [&b, &mat_v]() {
+
56  const SizeType m = mat_v.size().rows();
+
57  const SizeType mb = mat_v.blockSize().rows();
+
58  return std::max<SizeType>(0, util::ceilDiv(m - b - 1, mb));
+
59  };
+
60  DLAF_ASSERT(mat_taus.nrTiles().rows() == nr_reflectors_blocks(), mat_taus.size().rows(), mat_v, b);
+
61 
+
62  BackTransformationReductionToBand<backend, device, T>::call(b, mat_c, mat_v, mat_taus);
+
63 }
+
64 
+
84 template <Backend backend, Device device, class T>
+
85 void bt_reduction_to_band(comm::CommunicatorGrid grid, const SizeType b, Matrix<T, device>& mat_c,
+
86  Matrix<const T, device>& mat_v, Matrix<const T, Device::CPU>& mat_taus) {
+
87  DLAF_ASSERT(matrix::equal_process_grid(mat_c, grid), mat_c, grid);
+
88  DLAF_ASSERT(matrix::equal_process_grid(mat_v, grid), mat_v, grid);
+
89  DLAF_ASSERT(square_size(mat_v), mat_v);
+
90  DLAF_ASSERT(square_blocksize(mat_v), mat_v);
+
91  DLAF_ASSERT(mat_c.size().rows() == mat_v.size().rows(), mat_c, mat_v);
+
92  DLAF_ASSERT(mat_c.blockSize().rows() == mat_v.blockSize().rows(), mat_c, mat_v);
+
93  DLAF_ASSERT(single_tile_per_block(mat_c), mat_c);
+
94  DLAF_ASSERT(single_tile_per_block(mat_v), mat_v);
+
95 
+
96  [[maybe_unused]] auto nr_reflectors_blocks = [&b, &mat_v]() {
+
97  const SizeType m = mat_v.size().rows();
+
98  const SizeType mb = mat_v.blockSize().rows();
+
99  return mat_v.distribution().template nextLocalTileFromGlobalTile<Coord::Col>(
+
100  std::max<SizeType>(0, util::ceilDiv(m - b - 1, mb)));
+
101  };
+
102  DLAF_ASSERT(mat_taus.distribution().localNrTiles().rows() == nr_reflectors_blocks(), mat_taus, mat_v,
+
103  b);
+
104 
+
105  BackTransformationReductionToBand<backend, device, T>::call(grid, b, mat_c, mat_v, mat_taus);
+
106 }
+
107 }
+
dlaf::eigensolver::internal::bt_reduction_to_band
void bt_reduction_to_band(const SizeType b, Matrix< T, device > &mat_c, Matrix< const T, device > &mat_v, Matrix< const T, Device::CPU > &mat_taus)
Definition: bt_reduction_to_band.h:44
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
communicator_grid.h
diff --git a/master/dlaf_2eigensolver_2eigensolver_8h.html b/master/dlaf_2eigensolver_2eigensolver_8h.html index d64c654f17..1a82f120b9 100644 --- a/master/dlaf_2eigensolver_2eigensolver_8h.html +++ b/master/dlaf_2eigensolver_2eigensolver_8h.html @@ -127,14 +127,21 @@

Returns
struct ReturnEigensolverType with eigenvalues, as a vector<T>, and eigenvectors as a Matrix
+
Returns
ReturnEigensolverType with eigenvalues and eigenvectors as a Matrix
Parameters
- - + +
uplospecifies if upper or lower triangular part of mat will be referenced
matcontains the Hermitian matrix A
uplospecifies if upper or lower triangular part of mat will be referenced
[in,out]matcontains the Hermitian matrix A
+
Precondition
mat is not distributed
+
+mat has size (N x N)
+
+mat has blocksize (NB x NB)
+
+mat has tilesize (NB x NB)
@@ -183,13 +190,44 @@

Parameters
- - - - + +
uplospecifies if upper or lower triangular part of mat will be referenced
matcontains the Hermitian matrix A
eigenvaluesis a N x 1 matrix which on output contains the eigenvalues
eigenvectorsis a N x N matrix which on output contains the eigenvectors
uplospecifies if upper or lower triangular part of mat will be referenced
[in,out]matcontains the Hermitian matrix A
+
Precondition
mat is not distributed
+
+mat has size (N x N)
+
+mat has blocksize (NB x NB)
+
+mat has tilesize (NB x NB)
+
Parameters
+ + +
[out]eigenvaluescontains the eigenvalues
+
+
+
Precondition
eigenvalues is not distributed
+
+eigenvalues has size (N x 1)
+
+eigenvalues has blocksize (NB x 1)
+
+eigenvalues has tilesize (NB x 1)
+
Parameters
+ + +
[out]eigenvectorscontains the eigenvectors
+
+
+
Precondition
eigenvectors is not distributed
+
+eigenvectors has size (N x N)
+
+eigenvectors has blocksize (NB x NB)
+
+eigenvectors has tilesize (NB x NB)
@@ -230,15 +268,22 @@

Returns
struct ReturnEigensolverType with eigenvalues, as a vector<T>, and eigenvectors as a Matrix
+
Returns
struct ReturnEigensolverType with eigenvalues and eigenvectors as a Matrix
Parameters
- - - + + +
gridis the communicator grid on which the matrix mat has been distributed,
uplospecifies if upper or lower triangular part of mat will be referenced
matcontains the Hermitian matrix A
gridis the communicator grid on which the matrix mat has been distributed
uplospecifies if upper or lower triangular part of mat will be referenced
[in,out]matcontains the Hermitian matrix A
+
Precondition
mat is distributed according to grid
+
+mat has size (N x N)
+
+mat has blocksize (NB x NB)
+
+mat has tilesize (NB x NB)
@@ -293,14 +338,45 @@

Parameters
- - - - - + + + +
gridis the communicator grid on which the matrix mat has been distributed,
uplospecifies if upper or lower triangular part of mat will be referenced
matcontains the Hermitian matrix A
eigenvaluesis a N x 1 matrix which on output contains the eigenvalues
eigenvectorsis a N x N matrix which on output contains the eigenvectors
gridis the communicator grid on which the matrix mat has been distributed
uplospecifies if upper or lower triangular part of mat will be referenced
[in,out]matcontains the Hermitian matrix A
+
+ +
Precondition
mat is distributed according to grid
+
+mat has size (N x N)
+
+mat has blocksize (NB x NB)
+
+mat has tilesize (NB x NB)
+
Parameters
+ + +
[out]eigenvaluescontains the eigenvalues
+
+
+
Precondition
eigenvalues is stored on all ranks
+
+eigenvalues has size (N x 1)
+
+eigenvalues has blocksize (NB x 1)
+
+eigenvalues has tilesize (NB x 1)
+
Parameters
+ +
[out]eigenvectorscontains the eigenvectors
+
Precondition
eigenvectors is distributed according to grid
+
+eigenvectors has size (N x N)
+
+eigenvectors has blocksize (NB x NB)
+
+eigenvectors has tilesize (NB x NB)
diff --git a/master/dlaf_2eigensolver_2eigensolver_8h_source.html b/master/dlaf_2eigensolver_2eigensolver_8h_source.html index 79d7ba272f..500b4b6add 100644 --- a/master/dlaf_2eigensolver_2eigensolver_8h_source.html +++ b/master/dlaf_2eigensolver_2eigensolver_8h_source.html @@ -91,78 +91,78 @@
21 
22 namespace dlaf {
23 
-
38 template <Backend B, Device D, class T>
-
39 void hermitian_eigensolver(blas::Uplo uplo, Matrix<T, D>& mat, Matrix<BaseType<T>, D>& eigenvalues,
-
40  Matrix<T, D>& eigenvectors) {
-
41  DLAF_ASSERT(matrix::local_matrix(mat), mat);
-
42  DLAF_ASSERT(square_size(mat), mat);
-
43  DLAF_ASSERT(square_blocksize(mat), mat);
-
44  DLAF_ASSERT(matrix::local_matrix(eigenvalues), eigenvalues);
-
45  DLAF_ASSERT(eigenvalues.size().rows() == eigenvectors.size().rows(), eigenvalues, eigenvectors);
-
46  DLAF_ASSERT(eigenvalues.blockSize().rows() == eigenvectors.blockSize().rows(), eigenvalues,
-
47  eigenvectors);
-
48  DLAF_ASSERT(matrix::local_matrix(eigenvectors), eigenvectors);
-
49  DLAF_ASSERT(square_size(eigenvectors), eigenvectors);
-
50  DLAF_ASSERT(square_blocksize(eigenvectors), eigenvectors);
-
51  DLAF_ASSERT(eigenvectors.size() == mat.size(), eigenvectors, mat);
-
52  DLAF_ASSERT(eigenvectors.blockSize() == mat.blockSize(), eigenvectors, mat);
-
53  DLAF_ASSERT(single_tile_per_block(mat), mat);
-
54  DLAF_ASSERT(single_tile_per_block(eigenvalues), eigenvalues);
-
55  DLAF_ASSERT(single_tile_per_block(eigenvectors), eigenvectors);
-
56 
-
57  eigensolver::internal::Eigensolver<B, D, T>::call(uplo, mat, eigenvalues, eigenvectors);
-
58 }
-
59 
-
72 template <Backend B, Device D, class T>
-
73 EigensolverResult<T, D> hermitian_eigensolver(blas::Uplo uplo, Matrix<T, D>& mat) {
-
74  const SizeType size = mat.size().rows();
-
75  matrix::Matrix<BaseType<T>, D> eigenvalues(LocalElementSize(size, 1),
-
76  TileElementSize(mat.blockSize().rows(), 1));
-
77  matrix::Matrix<T, D> eigenvectors(LocalElementSize(size, size), mat.blockSize());
-
78 
-
79  hermitian_eigensolver<B, D, T>(uplo, mat, eigenvalues, eigenvectors);
-
80  return {std::move(eigenvalues), std::move(eigenvectors)};
-
81 }
-
82 
-
98 template <Backend B, Device D, class T>
-
99 void hermitian_eigensolver(comm::CommunicatorGrid grid, blas::Uplo uplo, Matrix<T, D>& mat,
-
100  Matrix<BaseType<T>, D>& eigenvalues, Matrix<T, D>& eigenvectors) {
-
101  DLAF_ASSERT(matrix::equal_process_grid(mat, grid), mat);
-
102  DLAF_ASSERT(square_size(mat), mat);
-
103  DLAF_ASSERT(square_blocksize(mat), mat);
-
104  DLAF_ASSERT(matrix::local_matrix(eigenvalues), eigenvalues);
-
105  DLAF_ASSERT(eigenvalues.size().rows() == eigenvectors.size().rows(), eigenvalues, eigenvectors);
-
106  DLAF_ASSERT(eigenvalues.blockSize().rows() == eigenvectors.blockSize().rows(), eigenvalues,
-
107  eigenvectors);
-
108  DLAF_ASSERT(matrix::equal_process_grid(eigenvectors, grid), eigenvectors);
-
109  DLAF_ASSERT(square_size(eigenvectors), eigenvectors);
-
110  DLAF_ASSERT(square_blocksize(eigenvectors), eigenvectors);
-
111  DLAF_ASSERT(eigenvectors.size() == mat.size(), eigenvectors, mat);
-
112  DLAF_ASSERT(eigenvectors.blockSize() == mat.blockSize(), eigenvectors, mat);
-
113  DLAF_ASSERT(single_tile_per_block(mat), mat);
-
114  DLAF_ASSERT(single_tile_per_block(eigenvalues), eigenvalues);
-
115  DLAF_ASSERT(single_tile_per_block(eigenvectors), eigenvectors);
-
116 
-
117  eigensolver::internal::Eigensolver<B, D, T>::call(grid, uplo, mat, eigenvalues, eigenvectors);
-
118 }
-
119 
-
133 template <Backend B, Device D, class T>
-
134 EigensolverResult<T, D> hermitian_eigensolver(comm::CommunicatorGrid grid, blas::Uplo uplo,
-
135  Matrix<T, D>& mat) {
-
136  const SizeType size = mat.size().rows();
-
137  matrix::Matrix<BaseType<T>, D> eigenvalues(LocalElementSize(size, 1),
-
138  TileElementSize(mat.blockSize().rows(), 1));
-
139  matrix::Matrix<T, D> eigenvectors(GlobalElementSize(size, size), mat.blockSize(), grid);
-
140 
-
141  hermitian_eigensolver<B, D, T>(grid, uplo, mat, eigenvalues, eigenvectors);
-
142  return {std::move(eigenvalues), std::move(eigenvectors)};
-
143 }
-
144 }
+
53 template <Backend B, Device D, class T>
+
54 void hermitian_eigensolver(blas::Uplo uplo, Matrix<T, D>& mat, Matrix<BaseType<T>, D>& eigenvalues,
+
55  Matrix<T, D>& eigenvectors) {
+
56  DLAF_ASSERT(matrix::local_matrix(mat), mat);
+
57  DLAF_ASSERT(square_size(mat), mat);
+
58  DLAF_ASSERT(square_blocksize(mat), mat);
+
59  DLAF_ASSERT(matrix::local_matrix(eigenvalues), eigenvalues);
+
60  DLAF_ASSERT(eigenvalues.size().rows() == eigenvectors.size().rows(), eigenvalues, eigenvectors);
+
61  DLAF_ASSERT(eigenvalues.blockSize().rows() == eigenvectors.blockSize().rows(), eigenvalues,
+
62  eigenvectors);
+
63  DLAF_ASSERT(matrix::local_matrix(eigenvectors), eigenvectors);
+
64  DLAF_ASSERT(square_size(eigenvectors), eigenvectors);
+
65  DLAF_ASSERT(square_blocksize(eigenvectors), eigenvectors);
+
66  DLAF_ASSERT(eigenvectors.size() == mat.size(), eigenvectors, mat);
+
67  DLAF_ASSERT(eigenvectors.blockSize() == mat.blockSize(), eigenvectors, mat);
+
68  DLAF_ASSERT(single_tile_per_block(mat), mat);
+
69  DLAF_ASSERT(single_tile_per_block(eigenvalues), eigenvalues);
+
70  DLAF_ASSERT(single_tile_per_block(eigenvectors), eigenvectors);
+
71 
+
72  eigensolver::internal::Eigensolver<B, D, T>::call(uplo, mat, eigenvalues, eigenvectors);
+
73 }
+
74 
+
93 template <Backend B, Device D, class T>
+
94 EigensolverResult<T, D> hermitian_eigensolver(blas::Uplo uplo, Matrix<T, D>& mat) {
+
95  const SizeType size = mat.size().rows();
+
96  matrix::Matrix<BaseType<T>, D> eigenvalues(LocalElementSize(size, 1),
+
97  TileElementSize(mat.blockSize().rows(), 1));
+
98  matrix::Matrix<T, D> eigenvectors(LocalElementSize(size, size), mat.blockSize());
+
99 
+
100  hermitian_eigensolver<B, D, T>(uplo, mat, eigenvalues, eigenvectors);
+
101  return {std::move(eigenvalues), std::move(eigenvectors)};
+
102 }
+
103 
+
135 template <Backend B, Device D, class T>
+
136 void hermitian_eigensolver(comm::CommunicatorGrid grid, blas::Uplo uplo, Matrix<T, D>& mat,
+
137  Matrix<BaseType<T>, D>& eigenvalues, Matrix<T, D>& eigenvectors) {
+
138  DLAF_ASSERT(matrix::equal_process_grid(mat, grid), mat);
+
139  DLAF_ASSERT(square_size(mat), mat);
+
140  DLAF_ASSERT(square_blocksize(mat), mat);
+
141  DLAF_ASSERT(matrix::local_matrix(eigenvalues), eigenvalues);
+
142  DLAF_ASSERT(eigenvalues.size().rows() == eigenvectors.size().rows(), eigenvalues, eigenvectors);
+
143  DLAF_ASSERT(eigenvalues.blockSize().rows() == eigenvectors.blockSize().rows(), eigenvalues,
+
144  eigenvectors);
+
145  DLAF_ASSERT(matrix::equal_process_grid(eigenvectors, grid), eigenvectors);
+
146  DLAF_ASSERT(square_size(eigenvectors), eigenvectors);
+
147  DLAF_ASSERT(square_blocksize(eigenvectors), eigenvectors);
+
148  DLAF_ASSERT(eigenvectors.size() == mat.size(), eigenvectors, mat);
+
149  DLAF_ASSERT(eigenvectors.blockSize() == mat.blockSize(), eigenvectors, mat);
+
150  DLAF_ASSERT(single_tile_per_block(mat), mat);
+
151  DLAF_ASSERT(single_tile_per_block(eigenvalues), eigenvalues);
+
152  DLAF_ASSERT(single_tile_per_block(eigenvectors), eigenvectors);
+
153 
+
154  eigensolver::internal::Eigensolver<B, D, T>::call(grid, uplo, mat, eigenvalues, eigenvectors);
+
155 }
+
156 
+
177 template <Backend B, Device D, class T>
+
178 EigensolverResult<T, D> hermitian_eigensolver(comm::CommunicatorGrid grid, blas::Uplo uplo,
+
179  Matrix<T, D>& mat) {
+
180  const SizeType size = mat.size().rows();
+
181  matrix::Matrix<BaseType<T>, D> eigenvalues(LocalElementSize(size, 1),
+
182  TileElementSize(mat.blockSize().rows(), 1));
+
183  matrix::Matrix<T, D> eigenvectors(GlobalElementSize(size, size), mat.blockSize(), grid);
+
184 
+
185  hermitian_eigensolver<B, D, T>(grid, uplo, mat, eigenvalues, eigenvectors);
+
186  return {std::move(eigenvalues), std::move(eigenvectors)};
+
187 }
+
188 }
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
dlaf::matrix::Matrix
Definition: matrix.h:56
communicator_grid.h
-
dlaf::hermitian_eigensolver
void hermitian_eigensolver(blas::Uplo uplo, Matrix< T, D > &mat, Matrix< BaseType< T >, D > &eigenvalues, Matrix< T, D > &eigenvectors)
Definition: eigensolver.h:39
+
dlaf::hermitian_eigensolver
void hermitian_eigensolver(blas::Uplo uplo, Matrix< T, D > &mat, Matrix< BaseType< T >, D > &eigenvalues, Matrix< T, D > &eigenvectors)
Definition: eigensolver.h:54
dlaf::EigensolverResult
Definition: api.h:22
diff --git a/master/factorization_2cholesky_8h.html b/master/factorization_2cholesky_8h.html index 84c6eec857..735ec0faf0 100644 --- a/master/factorization_2cholesky_8h.html +++ b/master/factorization_2cholesky_8h.html @@ -120,18 +120,18 @@

Parameters
- +
uplospecifies if the elements of the Hermitian matrix to be referenced are the elements in the lower or upper triangular part,
uplospecifies if the elements of the Hermitian matrix to be referenced are the elements in the lower or upper triangular part,
mat_aon entry it contains the triangular matrix A, on exit the matrix elements are overwritten with the elements of the Cholesky factor. Only the tiles of the matrix which contain the upper or the lower triangular part (depending on the value of uplo),
-
Precondition
mat_a has a square size,
+
Precondition
mat_a is not distributed
-mat_a has a square block size,
+mat_a has size (N x N)
-mat_a has equal tile and block sizes
+mat_a has blocksize (NB x NB)
-mat_a is not distributed.
+mat_a has tilesize (NB x NB)
@@ -177,13 +177,13 @@

Precondition
mat_a has a square size,
+
Precondition
mat_a is distributed according to grid
-mat_a has a square block size,
+mat_a has size (N x N)
-mat_a has equal tile and block sizes
+mat_a has blocksize (NB x NB)
-mat_a is distributed according to grid.
+mat_a has tilesize (NB x NB) diff --git a/master/factorization_2cholesky_8h_source.html b/master/factorization_2cholesky_8h_source.html index 5759ff309b..c97d4c35c3 100644 --- a/master/factorization_2cholesky_8h_source.html +++ b/master/factorization_2cholesky_8h_source.html @@ -91,37 +91,37 @@
21 
22 namespace dlaf {
23 
-
38 template <Backend backend, Device device, class T>
-
39 void cholesky_factorization(blas::Uplo uplo, Matrix<T, device>& mat_a) {
-
40  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
41  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
42  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
43  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
-
44 
-
45  if (uplo == blas::Uplo::Lower)
-
46  factorization::internal::Cholesky<backend, device, T>::call_L(mat_a);
-
47  else
-
48  factorization::internal::Cholesky<backend, device, T>::call_U(mat_a);
-
49 }
-
50 
-
66 template <Backend backend, Device device, class T>
-
67 void cholesky_factorization(comm::CommunicatorGrid grid, blas::Uplo uplo, Matrix<T, device>& mat_a) {
-
68  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
69  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
70  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
71  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
-
72 
-
73  // Method only for Lower triangular matrix
-
74  if (uplo == blas::Uplo::Lower)
-
75  factorization::internal::Cholesky<backend, device, T>::call_L(grid, mat_a);
-
76  else
-
77  factorization::internal::Cholesky<backend, device, T>::call_U(grid, mat_a);
-
78 }
-
79 
-
80 }
+
39 template <Backend backend, Device device, class T>
+
40 void cholesky_factorization(blas::Uplo uplo, Matrix<T, device>& mat_a) {
+
41  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
42  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
43  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
44  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
45 
+
46  if (uplo == blas::Uplo::Lower)
+
47  factorization::internal::Cholesky<backend, device, T>::call_L(mat_a);
+
48  else
+
49  factorization::internal::Cholesky<backend, device, T>::call_U(mat_a);
+
50 }
+
51 
+
67 template <Backend backend, Device device, class T>
+
68 void cholesky_factorization(comm::CommunicatorGrid grid, blas::Uplo uplo, Matrix<T, device>& mat_a) {
+
69  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
70  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
71  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
72  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
+
73 
+
74  // Method only for Lower triangular matrix
+
75  if (uplo == blas::Uplo::Lower)
+
76  factorization::internal::Cholesky<backend, device, T>::call_L(grid, mat_a);
+
77  else
+
78  factorization::internal::Cholesky<backend, device, T>::call_U(grid, mat_a);
+
79 }
+
80 
+
81 }
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
communicator_grid.h
-
dlaf::cholesky_factorization
void cholesky_factorization(blas::Uplo uplo, Matrix< T, device > &mat_a)
Definition: cholesky.h:39
+
dlaf::cholesky_factorization
void cholesky_factorization(blas::Uplo uplo, Matrix< T, device > &mat_a)
Definition: cholesky.h:40
diff --git a/master/gen__eigensolver_8h.html b/master/gen__eigensolver_8h.html index 36048fac3a..7cfcf64ed6 100644 --- a/master/gen__eigensolver_8h.html +++ b/master/gen__eigensolver_8h.html @@ -138,12 +138,31 @@

Returns
struct ReturnEigensolverType with eigenvalues, as a vector<T>, and eigenvectors as a Matrix
Parameters
- + +
uplospecifies if upper or lower triangular part of mat_a and mat_b will be referenced
uplospecifies if upper or lower triangular part of mat_a and mat_b will be referenced
mat_acontains the Hermitian matrix A
+
+
+
Precondition
mat_a is not distributed
+
+mat_a has size (N x N)
+
+mat_a has blocksize (NB x NB)
+
+mat_a has tilesize (NB x NB)
+
Parameters
+
mat_bcontains the Hermitian positive definite matrix B
+
Precondition
mat_b is not distributed
+
+mat_b has size (N x N)
+
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
@@ -203,14 +222,57 @@

Parameters
- + +
uplospecifies if upper or lower triangular part of mat_a and mat_b will be referenced
uplospecifies if upper or lower triangular part of mat_a and mat_b will be referenced
mat_acontains the Hermitian matrix A
+
+ +
Precondition
mat_a is not distributed
+
+mat_a has size (N x N)
+
+mat_a has blocksize (NB x NB)
+
+mat_a has tilesize (NB x NB)
+
Parameters
+ - -
mat_bcontains the Hermitian positive definite matrix B
eigenvaluesis a N x 1 matrix which on output contains the eigenvalues
eigenvectorsis a N x N matrix which on output contains the eigenvectors
+
Precondition
mat_b is not distributed
+
+mat_b has size (N x N)
+
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
+
Parameters
+ + +
[out]eigenvaluescontains the eigenvalues
+
+
+
Precondition
eigenvalues is not distributed
+
+eigenvalues has size (N x 1)
+
+eigenvalues has blocksize (NB x NB)
+
+eigenvalues has tilesize (NB x NB)
+
Parameters
+ + +
[out]eigenvectorscontains the eigenvectors
+
+
+
Precondition
eigenvectors is not distributed
+
+eigenvectors has size (N x N)
+
+eigenvectors has blocksize (NB x NB)
+
+eigenvectors has tilesize (NB x NB)
@@ -264,12 +326,31 @@

Parameters
- + +
gridis the communicator grid on which the matrices mat_a and mat_b have been distributed,
uplospecifies if upper or lower triangular part of mat_a and mat_b will be referenced
uplospecifies if upper or lower triangular part of mat_a and mat_b will be referenced
mat_acontains the Hermitian matrix A
+
+ +
Precondition
mat_a is distributed according to grid
+
+mat_a has size (N x N)
+
+mat_a has blocksize (NB x NB)
+
+mat_a has tilesize (NB x NB)
+
Parameters
+
mat_bcontains the Hermitian positive definite matrix B
+
Precondition
mat_b is distributed according to grid
+
+mat_b has size (N x N)
+
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
@@ -336,14 +417,57 @@

Parameters
- + +
gridis the communicator grid on which the matrices mat_a and mat_b have been distributed,
uplospecifies if upper or lower triangular part of mat_a and mat_b will be referenced
uplospecifies if upper or lower triangular part of mat_a and mat_b will be referenced
mat_acontains the Hermitian matrix A
+
+ +
Precondition
mat_a is distributed according to grid
+
+mat_a has size (N x N)
+
+mat_a has blocksize (NB x NB)
+
+mat_a has tilesize (NB x NB)
+
Parameters
+ +
mat_bcontains the Hermitian positive definite matrix B
+
+
+
Precondition
mat_b is distributed according to grid
+
+mat_b has size (N x N)
+
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
+
Parameters
+ -
eigenvaluesis a N x 1 matrix which on output contains the eigenvalues
eigenvectorsis a N x N matrix which on output contains the eigenvectors
+
Precondition
eigenvalues is not distributed
+
+eigenvalues has size (N x 1)
+
+eigenvalues has blocksize (NB x 1)
+
+eigenvalues has tilesize (NB x 1)
+
Parameters
+ + +
[out]eigenvectorscontains the eigenvectors
+
+
+
Precondition
eigenvectors is distributed according to grid
+
+eigenvectors has size (N x N)
+
+eigenvectors has blocksize (NB x NB)
+
+eigenvectors has tilesize (NB x NB)
diff --git a/master/gen__eigensolver_8h_source.html b/master/gen__eigensolver_8h_source.html index ee0aaa5fa2..11e256da01 100644 --- a/master/gen__eigensolver_8h_source.html +++ b/master/gen__eigensolver_8h_source.html @@ -90,114 +90,114 @@
20 
21 namespace dlaf {
22 
-
41 template <Backend B, Device D, class T>
-
42 void hermitian_generalized_eigensolver(blas::Uplo uplo, Matrix<T, D>& mat_a, Matrix<T, D>& mat_b,
-
43  Matrix<BaseType<T>, D>& eigenvalues, Matrix<T, D>& eigenvectors) {
-
44  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
-
45  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
-
46  DLAF_ASSERT(matrix::local_matrix(eigenvalues), eigenvalues);
-
47  DLAF_ASSERT(matrix::local_matrix(eigenvectors), eigenvectors);
-
48  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
49  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
50  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
-
51  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
-
52  DLAF_ASSERT(matrix::square_size(eigenvectors), eigenvectors);
-
53  DLAF_ASSERT(matrix::square_blocksize(eigenvectors), eigenvectors);
-
54  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
-
55  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
-
56  DLAF_ASSERT(eigenvalues.size().rows() == eigenvectors.size().rows(), eigenvalues, eigenvectors);
-
57  DLAF_ASSERT(eigenvalues.blockSize().rows() == eigenvectors.blockSize().rows(), eigenvalues,
-
58  eigenvectors);
-
59  DLAF_ASSERT(eigenvectors.size() == mat_a.size(), eigenvectors, mat_a);
-
60  DLAF_ASSERT(eigenvectors.blockSize() == mat_a.blockSize(), eigenvectors, mat_a);
-
61  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
62  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
63  DLAF_ASSERT(matrix::single_tile_per_block(eigenvalues), eigenvalues);
-
64  DLAF_ASSERT(matrix::single_tile_per_block(eigenvectors), eigenvectors);
-
65 
-
66  eigensolver::internal::GenEigensolver<B, D, T>::call(uplo, mat_a, mat_b, eigenvalues, eigenvectors);
-
67 }
-
68 
-
84 template <Backend B, Device D, class T>
-
85 EigensolverResult<T, D> hermitian_generalized_eigensolver(blas::Uplo uplo, Matrix<T, D>& mat_a,
-
86  Matrix<T, D>& mat_b) {
-
87  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
-
88  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
-
89  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
90  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
91  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
-
92  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
-
93  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
-
94  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
-
95 
-
96  const SizeType size = mat_a.size().rows();
-
97 
-
98  matrix::Matrix<BaseType<T>, D> eigenvalues(LocalElementSize(size, 1),
-
99  TileElementSize(mat_a.blockSize().rows(), 1));
-
100  matrix::Matrix<T, D> eigenvectors(LocalElementSize(size, size), mat_a.blockSize());
-
101 
-
102  hermitian_generalized_eigensolver<B, D, T>(uplo, mat_a, mat_b, eigenvalues, eigenvectors);
-
103 
-
104  return {std::move(eigenvalues), std::move(eigenvectors)};
-
105 }
-
106 
-
126 template <Backend B, Device D, class T>
-
127 void hermitian_generalized_eigensolver(comm::CommunicatorGrid grid, blas::Uplo uplo, Matrix<T, D>& mat_a,
-
128  Matrix<T, D>& mat_b, Matrix<BaseType<T>, D>& eigenvalues,
-
129  Matrix<T, D>& eigenvectors) {
-
130  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
-
131  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
-
132  DLAF_ASSERT(matrix::local_matrix(eigenvalues), eigenvalues);
-
133  DLAF_ASSERT(matrix::equal_process_grid(eigenvectors, grid), eigenvectors, grid);
-
134  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
135  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
136  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
-
137  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
-
138  DLAF_ASSERT(matrix::square_size(eigenvectors), eigenvectors);
-
139  DLAF_ASSERT(matrix::square_blocksize(eigenvectors), eigenvectors);
-
140  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
-
141  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
-
142  DLAF_ASSERT(eigenvalues.size().rows() == eigenvectors.size().rows(), eigenvalues, eigenvectors);
-
143  DLAF_ASSERT(eigenvalues.blockSize().rows() == eigenvectors.blockSize().rows(), eigenvalues,
-
144  eigenvectors);
-
145  DLAF_ASSERT(eigenvectors.size() == mat_a.size(), eigenvectors, mat_a);
-
146  DLAF_ASSERT(eigenvectors.blockSize() == mat_a.blockSize(), eigenvectors, mat_a);
-
147  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
148  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
149  DLAF_ASSERT(matrix::single_tile_per_block(eigenvalues), eigenvalues);
-
150  DLAF_ASSERT(matrix::single_tile_per_block(eigenvectors), eigenvectors);
-
151 
-
152  eigensolver::internal::GenEigensolver<B, D, T>::call(grid, uplo, mat_a, mat_b, eigenvalues,
-
153  eigenvectors);
-
154 }
-
155 
-
172 template <Backend B, Device D, class T>
-
173 EigensolverResult<T, D> hermitian_generalized_eigensolver(comm::CommunicatorGrid grid, blas::Uplo uplo,
-
174  Matrix<T, D>& mat_a, Matrix<T, D>& mat_b) {
-
175  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
-
176  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
-
177  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
178  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
179  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
-
180  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
-
181  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
-
182  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
-
183 
-
184  const SizeType size = mat_a.size().rows();
-
185 
-
186  matrix::Matrix<BaseType<T>, D> eigenvalues(LocalElementSize(size, 1),
-
187  TileElementSize(mat_a.blockSize().rows(), 1));
-
188  matrix::Matrix<T, D> eigenvectors(GlobalElementSize(size, size), mat_a.blockSize(), grid);
-
189 
-
190  hermitian_generalized_eigensolver<B, D, T>(grid, uplo, mat_a, mat_b, eigenvalues, eigenvectors);
-
191 
-
192  return {std::move(eigenvalues), std::move(eigenvectors)};
-
193 }
-
194 }
+
61 template <Backend B, Device D, class T>
+
62 void hermitian_generalized_eigensolver(blas::Uplo uplo, Matrix<T, D>& mat_a, Matrix<T, D>& mat_b,
+
63  Matrix<BaseType<T>, D>& eigenvalues, Matrix<T, D>& eigenvectors) {
+
64  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
65  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
+
66  DLAF_ASSERT(matrix::local_matrix(eigenvalues), eigenvalues);
+
67  DLAF_ASSERT(matrix::local_matrix(eigenvectors), eigenvectors);
+
68  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
69  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
70  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
+
71  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
+
72  DLAF_ASSERT(matrix::square_size(eigenvectors), eigenvectors);
+
73  DLAF_ASSERT(matrix::square_blocksize(eigenvectors), eigenvectors);
+
74  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
+
75  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
+
76  DLAF_ASSERT(eigenvalues.size().rows() == eigenvectors.size().rows(), eigenvalues, eigenvectors);
+
77  DLAF_ASSERT(eigenvalues.blockSize().rows() == eigenvectors.blockSize().rows(), eigenvalues,
+
78  eigenvectors);
+
79  DLAF_ASSERT(eigenvectors.size() == mat_a.size(), eigenvectors, mat_a);
+
80  DLAF_ASSERT(eigenvectors.blockSize() == mat_a.blockSize(), eigenvectors, mat_a);
+
81  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
82  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
83  DLAF_ASSERT(matrix::single_tile_per_block(eigenvalues), eigenvalues);
+
84  DLAF_ASSERT(matrix::single_tile_per_block(eigenvectors), eigenvectors);
+
85 
+
86  eigensolver::internal::GenEigensolver<B, D, T>::call(uplo, mat_a, mat_b, eigenvalues, eigenvectors);
+
87 }
+
88 
+
114 template <Backend B, Device D, class T>
+
115 EigensolverResult<T, D> hermitian_generalized_eigensolver(blas::Uplo uplo, Matrix<T, D>& mat_a,
+
116  Matrix<T, D>& mat_b) {
+
117  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
118  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
+
119  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
120  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
121  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
+
122  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
+
123  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
+
124  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
+
125 
+
126  const SizeType size = mat_a.size().rows();
+
127 
+
128  matrix::Matrix<BaseType<T>, D> eigenvalues(LocalElementSize(size, 1),
+
129  TileElementSize(mat_a.blockSize().rows(), 1));
+
130  matrix::Matrix<T, D> eigenvectors(LocalElementSize(size, size), mat_a.blockSize());
+
131 
+
132  hermitian_generalized_eigensolver<B, D, T>(uplo, mat_a, mat_b, eigenvalues, eigenvectors);
+
133 
+
134  return {std::move(eigenvalues), std::move(eigenvectors)};
+
135 }
+
136 
+
176 template <Backend B, Device D, class T>
+
177 void hermitian_generalized_eigensolver(comm::CommunicatorGrid grid, blas::Uplo uplo, Matrix<T, D>& mat_a,
+
178  Matrix<T, D>& mat_b, Matrix<BaseType<T>, D>& eigenvalues,
+
179  Matrix<T, D>& eigenvectors) {
+
180  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
+
181  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
+
182  DLAF_ASSERT(matrix::local_matrix(eigenvalues), eigenvalues);
+
183  DLAF_ASSERT(matrix::equal_process_grid(eigenvectors, grid), eigenvectors, grid);
+
184  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
185  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
186  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
+
187  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
+
188  DLAF_ASSERT(matrix::square_size(eigenvectors), eigenvectors);
+
189  DLAF_ASSERT(matrix::square_blocksize(eigenvectors), eigenvectors);
+
190  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
+
191  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
+
192  DLAF_ASSERT(eigenvalues.size().rows() == eigenvectors.size().rows(), eigenvalues, eigenvectors);
+
193  DLAF_ASSERT(eigenvalues.blockSize().rows() == eigenvectors.blockSize().rows(), eigenvalues,
+
194  eigenvectors);
+
195  DLAF_ASSERT(eigenvectors.size() == mat_a.size(), eigenvectors, mat_a);
+
196  DLAF_ASSERT(eigenvectors.blockSize() == mat_a.blockSize(), eigenvectors, mat_a);
+
197  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
198  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
199  DLAF_ASSERT(matrix::single_tile_per_block(eigenvalues), eigenvalues);
+
200  DLAF_ASSERT(matrix::single_tile_per_block(eigenvectors), eigenvectors);
+
201 
+
202  eigensolver::internal::GenEigensolver<B, D, T>::call(grid, uplo, mat_a, mat_b, eigenvalues,
+
203  eigenvectors);
+
204 }
+
205 
+
232 template <Backend B, Device D, class T>
+
233 EigensolverResult<T, D> hermitian_generalized_eigensolver(comm::CommunicatorGrid grid, blas::Uplo uplo,
+
234  Matrix<T, D>& mat_a, Matrix<T, D>& mat_b) {
+
235  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
+
236  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
+
237  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
238  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
239  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
+
240  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
+
241  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
+
242  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
+
243 
+
244  const SizeType size = mat_a.size().rows();
+
245 
+
246  matrix::Matrix<BaseType<T>, D> eigenvalues(LocalElementSize(size, 1),
+
247  TileElementSize(mat_a.blockSize().rows(), 1));
+
248  matrix::Matrix<T, D> eigenvectors(GlobalElementSize(size, size), mat_a.blockSize(), grid);
+
249 
+
250  hermitian_generalized_eigensolver<B, D, T>(grid, uplo, mat_a, mat_b, eigenvalues, eigenvectors);
+
251 
+
252  return {std::move(eigenvalues), std::move(eigenvectors)};
+
253 }
+
254 }
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
dlaf::matrix::Matrix
Definition: matrix.h:56
-
dlaf::hermitian_generalized_eigensolver
void hermitian_generalized_eigensolver(blas::Uplo uplo, Matrix< T, D > &mat_a, Matrix< T, D > &mat_b, Matrix< BaseType< T >, D > &eigenvalues, Matrix< T, D > &eigenvectors)
Definition: gen_eigensolver.h:42
+
dlaf::hermitian_generalized_eigensolver
void hermitian_generalized_eigensolver(blas::Uplo uplo, Matrix< T, D > &mat_a, Matrix< T, D > &mat_b, Matrix< BaseType< T >, D > &eigenvalues, Matrix< T, D > &eigenvectors)
Definition: gen_eigensolver.h:62
dlaf::EigensolverResult
Definition: api.h:22
diff --git a/master/gen__to__std_8h.html b/master/gen__to__std_8h.html index 9131e03ac8..d8f56033c4 100644 --- a/master/gen__to__std_8h.html +++ b/master/gen__to__std_8h.html @@ -127,19 +127,31 @@

Parameters
- + -
uplospecifies if the elements of the Hermitian matrix A and the triangular matrix B to be referenced are the elements in the lower or upper triangular part,
uplospecifies if the elements of the Hermitian matrix A and the triangular matrix B to be referenced are the elements in the lower or upper triangular part,
mat_aon entry it contains the Hermitian matrix A (if A is real, the matrix is symmetric), on exit the matrix elements are overwritten with the elements of the matrix B. Only the tiles of the matrix which contain the lower triangular or the upper triangular part are accessed.
mat_bcontains the triangular matrix. It can be lower (L) or upper (U). Only the tiles of the matrix which contain the lower triangular or the upper triangular part are accessed. Note: B should be modifiable as the diagonal tiles might be temporarly modified during the calculation.
-
Precondition
mat_a and mat_b have the same square size,
+
Precondition
mat_a is not distributed
-mat_a and mat_b have the same square block size,
+mat_a has size (N x N)
-mat_a and mat_b have the same tile and block sizes,
+mat_a has blocksize (NB x NB)
-mat_a and mat_b are not distributed.
+mat_a has tilesize (NB x NB)
+
Parameters
+ + +
mat_bcontains the triangular matrix. It can be lower (L) or upper (U). Only the tiles of the matrix which contain the lower triangular or the upper triangular part are accessed. Note: B should be modifiable as the diagonal tiles might be temporarily modified during the calculation.
+
+
+
Precondition
mat_b is not distributed
+
+mat_b has size (N x N)
+
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
@@ -187,19 +199,31 @@

Parameters
- + -
gridis the communicator grid on which the matrix A has been distributed,
uplospecifies if the elements of the Hermitian matrix A and the triangular matrix B to be referenced are the elements in the lower or upper triangular part,
uplospecifies if the elements of the Hermitian matrix A and the triangular matrix B to be referenced are the elements in the lower or upper triangular part,
mat_aon entry it contains the Hermitian matrix A (if A is real, the matrix is symmetric), on exit the matrix elements are overwritten with the elements of the matrix B. Only the tiles of the matrix which contain the lower triangular or the upper triangular part are accessed.
mat_bcontains the triangular matrix. It can be lower (L) or upper (U). Only the tiles of the matrix which contain the lower triangular or the upper triangular part are accessed. Note: B should be modifiable as the diagonal tiles might be temporarly modified during the calculation.
-
Precondition
mat_a and mat_b have the same square size,
+
Precondition
mat_a is distributed according to grid
+
+mat_a has size (N x N)
+
+mat_a has blocksize (NB x NB)
+
+mat_a has tilesize (NB x NB)
+
Parameters
+ + +
mat_bcontains the triangular matrix. It can be lower (L) or upper (U). Only the tiles of the matrix which contain the lower triangular or the upper triangular part are accessed. Note: B should be modifiable as the diagonal tiles might be temporarily modified during the calculation.
+
+
+
Precondition
mat_b is distributed according to grid
-mat_a and mat_b have the same square block size,
+mat_b has size (N x N)
-mat_a and mat_b have the same tile and block sizes,
+mat_b has blocksize (NB x NB)
-mat_a and mat_b are distributed according to the grid.
+mat_b has tilesize (NB x NB)
diff --git a/master/gen__to__std_8h_source.html b/master/gen__to__std_8h_source.html index 320f1c1dd6..8e12ed4beb 100644 --- a/master/gen__to__std_8h_source.html +++ b/master/gen__to__std_8h_source.html @@ -91,63 +91,63 @@
21 
22 namespace dlaf::eigensolver::internal {
23 
-
42 template <Backend backend, Device device, class T>
-
43 void generalized_to_standard(blas::Uplo uplo, Matrix<T, device>& mat_a, Matrix<T, device>& mat_b) {
-
44  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
45  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
46  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
-
47  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
-
48  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
-
49  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
-
50  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
51  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
52  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
-
53  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
-
54 
-
55  switch (uplo) {
-
56  case blas::Uplo::Lower:
-
57  GenToStd<backend, device, T>::call_L(mat_a, mat_b);
-
58  break;
-
59  case blas::Uplo::Upper:
-
60  GenToStd<backend, device, T>::call_U(mat_a, mat_b);
-
61  break;
-
62  case blas::Uplo::General:
-
63  DLAF_UNIMPLEMENTED(uplo);
+
48 template <Backend backend, Device device, class T>
+
49 void generalized_to_standard(blas::Uplo uplo, Matrix<T, device>& mat_a, Matrix<T, device>& mat_b) {
+
50  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
51  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
52  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
+
53  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
+
54  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
+
55  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
+
56  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
57  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
58  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
59  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
+
60 
+
61  switch (uplo) {
+
62  case blas::Uplo::Lower:
+
63  GenToStd<backend, device, T>::call_L(mat_a, mat_b);
64  break;
-
65  }
-
66 }
-
67 
-
87 template <Backend backend, Device device, class T>
-
88 void generalized_to_standard(comm::CommunicatorGrid grid, blas::Uplo uplo, Matrix<T, device>& mat_a,
-
89  Matrix<T, device>& mat_b) {
-
90  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
91  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
92  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
-
93  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
-
94  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
-
95  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
-
96  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
97  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
98  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
-
99  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
-
100 
-
101  switch (uplo) {
-
102  case blas::Uplo::Lower:
-
103  GenToStd<backend, device, T>::call_L(grid, mat_a, mat_b);
-
104  break;
-
105  case blas::Uplo::Upper:
-
106  GenToStd<backend, device, T>::call_U(grid, mat_a, mat_b);
-
107  break;
-
108  case blas::Uplo::General:
-
109  DLAF_UNIMPLEMENTED(uplo);
-
110  break;
-
111  }
-
112 }
-
113 
-
114 }
+
65  case blas::Uplo::Upper:
+
66  GenToStd<backend, device, T>::call_U(mat_a, mat_b);
+
67  break;
+
68  case blas::Uplo::General:
+
69  DLAF_UNIMPLEMENTED(uplo);
+
70  break;
+
71  }
+
72 }
+
73 
+
99 template <Backend backend, Device device, class T>
+
100 void generalized_to_standard(comm::CommunicatorGrid grid, blas::Uplo uplo, Matrix<T, device>& mat_a,
+
101  Matrix<T, device>& mat_b) {
+
102  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
103  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
104  DLAF_ASSERT(matrix::square_size(mat_b), mat_b);
+
105  DLAF_ASSERT(matrix::square_blocksize(mat_b), mat_b);
+
106  DLAF_ASSERT(mat_a.size() == mat_b.size(), mat_a, mat_b);
+
107  DLAF_ASSERT(mat_a.blockSize() == mat_b.blockSize(), mat_a, mat_b);
+
108  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
109  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
110  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
+
111  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
+
112 
+
113  switch (uplo) {
+
114  case blas::Uplo::Lower:
+
115  GenToStd<backend, device, T>::call_L(grid, mat_a, mat_b);
+
116  break;
+
117  case blas::Uplo::Upper:
+
118  GenToStd<backend, device, T>::call_U(grid, mat_a, mat_b);
+
119  break;
+
120  case blas::Uplo::General:
+
121  DLAF_UNIMPLEMENTED(uplo);
+
122  break;
+
123  }
+
124 }
+
125 
+
126 }
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
communicator_grid.h
-
dlaf::eigensolver::internal::generalized_to_standard
void generalized_to_standard(blas::Uplo uplo, Matrix< T, device > &mat_a, Matrix< T, device > &mat_b)
Definition: gen_to_std.h:43
+
dlaf::eigensolver::internal::generalized_to_standard
void generalized_to_standard(blas::Uplo uplo, Matrix< T, device > &mat_a, Matrix< T, device > &mat_b)
Definition: gen_to_std.h:49
diff --git a/master/hermitian_8h.html b/master/hermitian_8h.html index 1ca2b14580..02eefb3272 100644 --- a/master/hermitian_8h.html +++ b/master/hermitian_8h.html @@ -151,22 +151,44 @@

Parameters
- + +
sidespecifies whether A appears on the Left or on the Right of matrix B,
uplospecifies if the elements of the Hermitian matrix A to be referenced are the elements in the lower or upper triangular part,
uplospecifies if the elements of the Hermitian matrix A to be referenced are the elements in the lower or upper triangular part,
mat_acontains the hermitian matrix A. Only the tiles of the matrix which contain the upper or the lower triangular part which represent the Hermitian matrix (depending on the value of uplo) are accessed in read-only mode (the elements are not modified),
+
+ +
Precondition
mat_b is not distributed
+
+mat_b has size (N x M)
+
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
+
Parameters
+ -
mat_bcontains the matrix B accessed in read-only mode (the elements are not modified),
mat_con entry it contains the matrix C, on exit the matrix elements are overwritten with the elements of the result.
-
Precondition
mat_a has a square size,
+
Precondition
mat_b is not distributed
-mat_a has a square block size,
+mat_b has size (M x K) +
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
+
Parameters
+ + +
mat_con entry it contains the matrix C, on exit the matrix elements are overwritten with the elements of the result.
+
+
+
Precondition
mat_b is not distributed
-mat_a mat_b and mat_c have equal tile and block size,
+mat_b has size (N x K)
-mat_a mat_b and mat_c are not distributed,
+mat_b has blocksize (NB x NB)
-mat_a mat_b are multipliable and the result can be summed to mat_c.
+mat_b has tilesize (NB x NB)
@@ -237,22 +259,44 @@

Parameters
- + +
sidespecifies whether A appears on the Left or on the Right of matrix B,
uplospecifies if the elements of the Hermitian matrix A to be referenced are the elements in the lower or upper triangular part,
uplospecifies if the elements of the Hermitian matrix A to be referenced are the elements in the lower or upper triangular part,
mat_acontains the hermitian matrix A. Only the tiles of the matrix which contain the upper or the lower triangular part which represent the Hermitian matrix (depending on the value of uplo) are accessed in read-only mode (the elements are not modified),
+
+ +
Precondition
mat_b is distributed according to grid
+
+mat_b has size (N x M)
+
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
+
Parameters
+ -
mat_bcontains the matrix B accessed in read-only mode (the elements are not modified),
mat_con entry it contains the matrix C, on exit the matrix elements are overwritten with the elements of the result.
-
Precondition
mat_a has a square size,
+
Precondition
mat_b is distributed according to grid
-mat_a has a square block size,
+mat_b has size (M x K) +
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
+
Parameters
+ + +
mat_con entry it contains the matrix C, on exit the matrix elements are overwritten with the elements of the result.
+
+
+
Precondition
mat_b is distributed according to grid
-mat_a, mat_b and mat_c have equal tile and block size,
+mat_b has size (N x K)
-mat_a, mat_b and mat_c are distributed according to the grid,
+mat_b has blocksize (NB x NB)
-mat_a mat_b are multipliable and the result can be summed to mat_c.
+mat_b has tilesize (NB x NB)
diff --git a/master/hermitian_8h_source.html b/master/hermitian_8h_source.html index 24aaf19a9f..dc29572f8b 100644 --- a/master/hermitian_8h_source.html +++ b/master/hermitian_8h_source.html @@ -91,80 +91,80 @@
21 
22 namespace dlaf {
23 
-
41 template <Backend B, Device D, class T>
-
42 void hermitian_multiplication(blas::Side side, blas::Uplo uplo, const T alpha, Matrix<const T, D>& mat_a,
-
43  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c) {
-
44  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
45  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
46  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
47  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
48  DLAF_ASSERT(matrix::single_tile_per_block(mat_c), mat_c);
-
49  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
-
50  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
-
51  DLAF_ASSERT(matrix::local_matrix(mat_c), mat_c);
-
52 
-
53  if (side == blas::Side::Left) {
-
54  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_c, blas::Op::NoTrans, blas::Op::NoTrans), mat_a,
-
55  mat_b, mat_c);
-
56  switch (uplo) {
-
57  case blas::Uplo::Lower:
-
58  return multiplication::internal::Hermitian<B, D, T>::call_LL(alpha, mat_a, mat_b, beta, mat_c);
-
59  break;
-
60  case blas::Uplo::Upper:
-
61  DLAF_UNIMPLEMENTED(uplo);
-
62  break;
-
63  case blas::Uplo::General:
-
64  DLAF_UNIMPLEMENTED(uplo);
-
65  break;
-
66  }
-
67  }
-
68  else {
-
69  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_c, blas::Op::NoTrans, blas::Op::NoTrans), mat_a,
-
70  mat_b, mat_c);
-
71  DLAF_UNIMPLEMENTED(side);
-
72  }
-
73 }
-
74 
-
93 template <Backend B, Device D, class T>
-
94 void hermitian_multiplication(comm::CommunicatorGrid grid, blas::Side side, blas::Uplo uplo,
-
95  const T alpha, Matrix<const T, D>& mat_a, Matrix<const T, D>& mat_b,
-
96  const T beta, Matrix<T, D>& mat_c) {
-
97  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
98  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
99  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
100  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
101  DLAF_ASSERT(matrix::single_tile_per_block(mat_c), mat_c);
-
102  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
-
103  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
-
104  DLAF_ASSERT(matrix::equal_process_grid(mat_c, grid), mat_c, grid);
-
105 
-
106  if (side == blas::Side::Left) {
-
107  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_c, blas::Op::NoTrans, blas::Op::NoTrans), mat_a,
-
108  mat_b, mat_c);
-
109  switch (uplo) {
-
110  case blas::Uplo::Lower:
-
111  return multiplication::internal::Hermitian<B, D, T>::call_LL(grid, alpha, mat_a, mat_b, beta,
-
112  mat_c);
-
113  break;
-
114  case blas::Uplo::Upper:
-
115  DLAF_UNIMPLEMENTED(uplo);
-
116  break;
-
117  case blas::Uplo::General:
-
118  DLAF_UNIMPLEMENTED(uplo);
-
119  break;
-
120  }
-
121  }
-
122  else {
-
123  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_c, blas::Op::NoTrans, blas::Op::NoTrans), mat_a,
-
124  mat_b, mat_c);
-
125  DLAF_UNIMPLEMENTED(side);
-
126  }
-
127 }
-
128 
-
129 }
+
51 template <Backend B, Device D, class T>
+
52 void hermitian_multiplication(blas::Side side, blas::Uplo uplo, const T alpha, Matrix<const T, D>& mat_a,
+
53  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c) {
+
54  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
55  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
56  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
57  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
58  DLAF_ASSERT(matrix::single_tile_per_block(mat_c), mat_c);
+
59  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
60  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
+
61  DLAF_ASSERT(matrix::local_matrix(mat_c), mat_c);
+
62 
+
63  if (side == blas::Side::Left) {
+
64  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_c, blas::Op::NoTrans, blas::Op::NoTrans), mat_a,
+
65  mat_b, mat_c);
+
66  switch (uplo) {
+
67  case blas::Uplo::Lower:
+
68  return multiplication::internal::Hermitian<B, D, T>::call_LL(alpha, mat_a, mat_b, beta, mat_c);
+
69  break;
+
70  case blas::Uplo::Upper:
+
71  DLAF_UNIMPLEMENTED(uplo);
+
72  break;
+
73  case blas::Uplo::General:
+
74  DLAF_UNIMPLEMENTED(uplo);
+
75  break;
+
76  }
+
77  }
+
78  else {
+
79  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_c, blas::Op::NoTrans, blas::Op::NoTrans), mat_a,
+
80  mat_b, mat_c);
+
81  DLAF_UNIMPLEMENTED(side);
+
82  }
+
83 }
+
84 
+
113 template <Backend B, Device D, class T>
+
114 void hermitian_multiplication(comm::CommunicatorGrid grid, blas::Side side, blas::Uplo uplo,
+
115  const T alpha, Matrix<const T, D>& mat_a, Matrix<const T, D>& mat_b,
+
116  const T beta, Matrix<T, D>& mat_c) {
+
117  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
118  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
119  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
120  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
121  DLAF_ASSERT(matrix::single_tile_per_block(mat_c), mat_c);
+
122  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
+
123  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
+
124  DLAF_ASSERT(matrix::equal_process_grid(mat_c, grid), mat_c, grid);
+
125 
+
126  if (side == blas::Side::Left) {
+
127  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_c, blas::Op::NoTrans, blas::Op::NoTrans), mat_a,
+
128  mat_b, mat_c);
+
129  switch (uplo) {
+
130  case blas::Uplo::Lower:
+
131  return multiplication::internal::Hermitian<B, D, T>::call_LL(grid, alpha, mat_a, mat_b, beta,
+
132  mat_c);
+
133  break;
+
134  case blas::Uplo::Upper:
+
135  DLAF_UNIMPLEMENTED(uplo);
+
136  break;
+
137  case blas::Uplo::General:
+
138  DLAF_UNIMPLEMENTED(uplo);
+
139  break;
+
140  }
+
141  }
+
142  else {
+
143  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_c, blas::Op::NoTrans, blas::Op::NoTrans), mat_a,
+
144  mat_b, mat_c);
+
145  DLAF_UNIMPLEMENTED(side);
+
146  }
+
147 }
+
148 
+
149 }
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
communicator_grid.h
-
dlaf::hermitian_multiplication
void hermitian_multiplication(blas::Side side, blas::Uplo uplo, const T alpha, Matrix< const T, D > &mat_a, Matrix< const T, D > &mat_b, const T beta, Matrix< T, D > &mat_c)
Definition: hermitian.h:42
+
dlaf::hermitian_multiplication
void hermitian_multiplication(blas::Side side, blas::Uplo uplo, const T alpha, Matrix< const T, D > &mat_a, Matrix< const T, D > &mat_b, const T beta, Matrix< T, D > &mat_c)
Definition: hermitian.h:52
diff --git a/master/merge_8h_source.html b/master/merge_8h_source.html index ee56c6628c..2d1e9dbf28 100644 --- a/master/merge_8h_source.html +++ b/master/merge_8h_source.html @@ -623,8 +623,8 @@
552  d2 = tmp;
553 
554  rots.push_back(GivensRotation<T>{i1s, i2s, c, s});
-
555  // Set the the `i1` column as "Dense" if the `i2` column has opposite non-zero structure (i.e if
-
556  // one comes from Q1 and the other from Q2 or vice-versa)
+
555  // Set the `i1` column as "Dense" if the `i2` column has opposite non-zero structure (i.e if one
+
556  // comes from Q1 and the other from Q2 or vice-versa)
557  if ((c1 == ColType::UpperHalf && c2 == ColType::LowerHalf) ||
558  (c1 == ColType::LowerHalf && c2 == ColType::UpperHalf)) {
559  c1 = ColType::Dense;
diff --git a/master/multiplication_2general_8h.html b/master/multiplication_2general_8h.html index 444018c341..9415323dbc 100644 --- a/master/multiplication_2general_8h.html +++ b/master/multiplication_2general_8h.html @@ -176,20 +176,44 @@

Parameters
+
mat_acontains the input matrix A. Only tiles whose both row and col tile coords are in the closed range [a,b] are accessed in read-only mode (elements are not modified)
+
+ +
Precondition
mat_a is distributed according to grid
+
+mat_a has size (N x N)
+
+mat_a has blocksize (NB x NB)
+
+mat_a has tilesize (NB x NB)
+
Parameters
+ +
mat_bcontains the input matrix B. Only tiles whose both row and col tile coords are in the closed range [a,b] are accessed in read-only mode (elements are not modified)
+
+
+
Precondition
mat_b is distributed according to grid
+
+mat_b has size (N x N)
+
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
+
Parameters
+
mat_cOn entry it contains the input matrix C. On exit matrix tiles in the range will be overwritten with the result, while others are left untouched. Only tiles whose both row and col tile coords are in the closed range [a,b] are accessed.
-
Precondition
mat_a, mat_b and mat_c are distributed in the same way,
+
Precondition
mat_c is distributed according to grid
-mat_a, mat_b and mat_c have the same square block size,
+mat_c has size (N x N)
-mat_a, mat_b and mat_c have the same size,
+mat_c has blocksize (NB x NB)
-mat_a, mat_b and mat_c have equal tile and block sizes,
+mat_c has tilesize (NB x NB)
-a <= b <= mat_a.nrTiles().rows()
+a <= b <= mat_a.nrTiles().rows()
@@ -266,22 +290,46 @@

Parameters
- + +
opAspecifies the form of opA(A) to be used in the matrix multiplication: NoTrans, Trans, ConjTrans,
opBspecifies the form of opB(B) to be used in the matrix multiplication: NoTrans, Trans, ConjTrans,
opBspecifies the form of opB(B) to be used in the matrix multiplication: NoTrans, Trans, ConjTrans,
mat_acontains the input matrix A. Only tiles whose both row and col tile coords are in the closed range [a,b] are accessed in read-only mode (elements are not modified)
+
+ +
Precondition
mat_a is not distributed
+
+mat_a has size (N x N)
+
+mat_a has blocksize (NB x NB)
+
+mat_a has tilesize (NB x NB)
+
Parameters
+ +
mat_bcontains the input matrix B. Only tiles whose both row and col tile coords are in the closed range [a,b] are accessed in read-only mode (elements are not modified)
+
+
+
Precondition
mat_b is not distributed
+
+mat_b has size (N x N)
+
+mat_b has blocksize (NB x NB)
+
+mat_b has tilesize (NB x NB)
+
Parameters
+
mat_cOn entry it contains the input matrix C. On exit matrix tiles in the range will be overwritten with the result, while others are left untouched. Only tiles whose both row and col tile coords are in the closed range [a,b] are accessed.
-
Precondition
mat_a, mat_b and mat_c have the same square block size,
+
Precondition
mat_c is not distributed
-mat_a, mat_b and mat_c have the same size,
+mat_c has size (N x N)
-mat_a, mat_b and mat_c have equal tile and block sizes,
+mat_c has blocksize (NB x NB)
-mat_a, mat_b and mat_c are not distributed,
+mat_c has tilesize (NB x NB)
-a <= b <= mat_a.nrTiles().rows()
+a <= b <= mat_a.nrTiles().rows()
diff --git a/master/multiplication_2general_8h_source.html b/master/multiplication_2general_8h_source.html index 07da257a09..79ae052303 100644 --- a/master/multiplication_2general_8h_source.html +++ b/master/multiplication_2general_8h_source.html @@ -95,94 +95,94 @@
25 
26 namespace dlaf::multiplication::internal {
27 
-
48 template <Backend B, Device D, class T>
-
49 void generalSubMatrix(const SizeType a, const SizeType b, const blas::Op opA, const blas::Op opB,
-
50  const T alpha, Matrix<const T, D>& mat_a, Matrix<const T, D>& mat_b, const T beta,
-
51  Matrix<T, D>& mat_c) {
-
52  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_a), mat_a);
-
53  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_b), mat_b);
-
54  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_c), mat_c);
-
55 
-
56  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
57  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
58  DLAF_ASSERT(matrix::single_tile_per_block(mat_c), mat_c);
-
59 
-
60  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
-
61  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
-
62  DLAF_ASSERT(matrix::local_matrix(mat_c), mat_c);
-
63 
-
64  // Note:
-
65  // This is an over-constraint, since the algorithm just cares about the sub-matrix size.
-
66  // It simplifies next check about [a,b) range validity, that otherwise would require it to be
-
67  // validated against every single sub-matrix in a, b and c that might have different element sizes.
-
68  //
-
69  // At the moment, we don't have this use-case, so let's keep it simple.
-
70  DLAF_ASSERT(dlaf::matrix::square_size(mat_a), mat_a);
-
71  DLAF_ASSERT(equal_size(mat_a, mat_b), mat_a, mat_b);
-
72  DLAF_ASSERT(equal_size(mat_a, mat_c), mat_a, mat_c);
-
73 
-
74  [[maybe_unused]] const SizeType m = mat_a.nrTiles().rows();
-
75  DLAF_ASSERT(a <= b, a, b);
-
76  DLAF_ASSERT(a >= 0 && a <= m, a, m);
-
77  DLAF_ASSERT(b >= 0 && b <= m, b, m);
-
78 
-
79  using namespace blas;
-
80 
-
81  if (opA == Op::NoTrans && opB == Op::NoTrans)
-
82  internal::GeneralSub<B, D, T>::callNN(a, b, opA, opB, alpha, mat_a, mat_b, beta, mat_c);
-
83  else
-
84  DLAF_UNIMPLEMENTED(opA, opB);
-
85 }
-
86 
-
103 template <Backend B, Device D, class T>
-
104 void generalSubMatrix([[maybe_unused]] comm::CommunicatorGrid grid,
-
105  common::Pipeline<comm::Communicator>& row_task_chain,
-
106  common::Pipeline<comm::Communicator>& col_task_chain, const SizeType a,
-
107  const SizeType b, const T alpha, Matrix<const T, D>& mat_a,
-
108  Matrix<const T, D>& mat_b, const T beta, Matrix<T, D>& mat_c) {
-
109  DLAF_ASSERT(equal_process_grid(mat_a, grid), mat_a, grid);
-
110  DLAF_ASSERT(equal_process_grid(mat_b, grid), mat_a, grid);
-
111  DLAF_ASSERT(equal_process_grid(mat_c, grid), mat_a, grid);
-
112 
-
113  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_a), mat_a);
-
114  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_b), mat_b);
-
115  DLAF_ASSERT(dlaf::matrix::square_blocksize(mat_c), mat_c);
-
116 
-
117  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
118  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
119  DLAF_ASSERT(matrix::single_tile_per_block(mat_c), mat_c);
-
120 
-
121  // Note:
-
122  // This is an over-constraint, since the algorithm just cares about the sub-matrix size (and its
-
123  // distribution).
-
124  // It simplifies next check about [a,b) range validity, that otherwise would require it to be
-
125  // validated against every single sub-matrix in a, b and c that might have different element sizes.
-
126  //
-
127  // At the moment, we don't have this use-case, so let's keep it simple.
-
128  DLAF_ASSERT(dlaf::matrix::square_size(mat_a), mat_a);
-
129  DLAF_ASSERT(equal_size(mat_a, mat_b), mat_a, mat_b);
-
130  DLAF_ASSERT(equal_size(mat_a, mat_c), mat_a, mat_c);
-
131 
-
132  [[maybe_unused]] const SizeType m = mat_a.nrTiles().rows();
-
133  DLAF_ASSERT(a <= b, a, b);
-
134  DLAF_ASSERT(a >= 0 && a <= m, a, m);
-
135  DLAF_ASSERT(b >= 0 && b <= m, b, m);
-
136 
-
137  internal::GeneralSub<B, D, T>::callNN(row_task_chain, col_task_chain, a, b, alpha, mat_a, mat_b, beta,
-
138  mat_c);
-
139 }
-
140 
-
141 template <Backend B, Device D, class T>
-
142 void generalSubMatrix(comm::CommunicatorGrid grid, const SizeType a, const SizeType b, const T alpha,
-
143  Matrix<const T, D>& mat_a, Matrix<const T, D>& mat_b, const T beta,
-
144  Matrix<T, D>& mat_c) {
-
145  common::Pipeline<comm::Communicator> row_task_chain(grid.rowCommunicator().clone());
-
146  common::Pipeline<comm::Communicator> col_task_chain(grid.colCommunicator().clone());
-
147  generalSubMatrix<B, D, T>(grid, row_task_chain, col_task_chain, a, b, alpha, mat_a, mat_b, beta,
-
148  mat_c);
-
149 }
-
150 
-
151 }
+
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 }
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
@@ -192,7 +192,7 @@ -
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:49
+
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
util_matrix.h
diff --git a/master/multiplication_2triangular_8h.html b/master/multiplication_2triangular_8h.html index ec9614cb67..dd218c8af3 100644 --- a/master/multiplication_2triangular_8h.html +++ b/master/multiplication_2triangular_8h.html @@ -153,21 +153,31 @@

sidespecifies whether op(A) appears on the Left or on the Right of matrix B, uplospecifies whether the matrix A is a Lower or an Upper triangular matrix, opspecifies the form of op(A) to be used in the matrix multiplication: NoTrans, Trans, ConjTrans, - diagspecifies if the matrix A is assumed to be unit triangular (Unit) or not (NonUnit), + diagspecifies if the matrix A is assumed to be unit triangular (Unit) or not (NonUnit), mat_acontains the triangular matrix A. Only the tiles of the matrix which contain the upper or the lower triangular part (depending on the value of uplo) are accessed in read-only mode (the elements are not modified), - mat_bon entry it contains the matrix B, on exit the matrix elements are overwritten with the elements of the result. -
Precondition
mat_a has a square size,
+
Precondition
mat_a is not distributed
+
+mat_a has size (N x N)
+
+mat_a has blocksize (NB x NB)
-mat_a has a square block size,
+mat_a has tilesize (NB x NB)
+
Parameters
+ + +
mat_bon entry it contains the matrix B, on exit the matrix elements are overwritten with the elements of the result.
+
+
+
Precondition
mat_b is not distributed
-mat_a and mat_b have equal tile and block sizes,
+mat_b has size (N x N)
-mat_a and mat_b are not distributed,
+mat_b has blocksize (NB x NB)
-mat_a and mat_b are multipliable.
+mat_b has tilesize (NB x NB)
@@ -240,21 +250,31 @@

sidespecifies whether op(A) appears on the Left or on the Right of matrix B, uplospecifies whether the matrix A is a Lower or an Upper triangular matrix, opspecifies the form of op(A) to be used in the matrix multiplication: NoTrans, Trans, ConjTrans, - diagspecifies if the matrix A is assumed to be unit triangular (Unit) or not (NonUnit), + diagspecifies if the matrix A is assumed to be unit triangular (Unit) or not (NonUnit), mat_acontains the triangular matrix A. Only the tiles of the matrix which contain the upper or the lower triangular part (depending on the value of uplo) are accessed in read-only mode (the elements are not modified), - mat_bon entry it contains the matrix B, on exit the matrix elements are overwritten with the elements of the result. -
Precondition
mat_a has a square size,
+
Precondition
mat_a is distributed according to grid
+
+mat_a has size (N x N)
+
+mat_a has blocksize (NB x NB)
-mat_a has a square block size,
+mat_a has tilesize (NB x NB)
+
Parameters
+ + +
mat_bon entry it contains the matrix B, on exit the matrix elements are overwritten with the elements of the result.
+
+
+
Precondition
mat_b is distributed according to grid
-mat_a and mat_b have equal tile and block sizes,
+mat_b has size (N x N)
-mat_a and mat_b are distributed according to the grid,
+mat_b has blocksize (NB x NB)
-mat_a and mat_b are multipliable.
+mat_b has tilesize (NB x NB)
diff --git a/master/multiplication_2triangular_8h_source.html b/master/multiplication_2triangular_8h_source.html index e6e23ff868..d76144a7bb 100644 --- a/master/multiplication_2triangular_8h_source.html +++ b/master/multiplication_2triangular_8h_source.html @@ -91,127 +91,127 @@
21 
22 namespace dlaf {
23 
-
42 template <Backend backend, Device device, class T>
-
43 void triangular_multiplication(blas::Side side, blas::Uplo uplo, blas::Op op, blas::Diag diag, T alpha,
-
44  Matrix<const T, device>& mat_a, Matrix<T, device>& mat_b) {
-
45  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
46  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
47  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
48  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
49  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
-
50  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
-
51 
-
52  if (side == blas::Side::Left) {
-
53  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_b, op, blas::Op::NoTrans), mat_a, mat_b, op);
-
54 
-
55  if (uplo == blas::Uplo::Lower) {
-
56  if (op == blas::Op::NoTrans) {
-
57  multiplication::internal::Triangular<backend, device, T>::call_LLN(diag, alpha, mat_a, mat_b);
-
58  }
-
59  else {
-
60  multiplication::internal::Triangular<backend, device, T>::call_LLT(op, diag, alpha, mat_a,
-
61  mat_b);
-
62  }
-
63  }
-
64  else {
-
65  if (op == blas::Op::NoTrans) {
-
66  multiplication::internal::Triangular<backend, device, T>::call_LUN(diag, alpha, mat_a, mat_b);
+
47 template <Backend backend, Device device, class T>
+
48 void triangular_multiplication(blas::Side side, blas::Uplo uplo, blas::Op op, blas::Diag diag, T alpha,
+
49  Matrix<const T, device>& mat_a, Matrix<T, device>& mat_b) {
+
50  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
51  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
52  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
53  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
54  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
55  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
+
56 
+
57  if (side == blas::Side::Left) {
+
58  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_b, op, blas::Op::NoTrans), mat_a, mat_b, op);
+
59 
+
60  if (uplo == blas::Uplo::Lower) {
+
61  if (op == blas::Op::NoTrans) {
+
62  multiplication::internal::Triangular<backend, device, T>::call_LLN(diag, alpha, mat_a, mat_b);
+
63  }
+
64  else {
+
65  multiplication::internal::Triangular<backend, device, T>::call_LLT(op, diag, alpha, mat_a,
+
66  mat_b);
67  }
-
68  else {
-
69  multiplication::internal::Triangular<backend, device, T>::call_LUT(op, diag, alpha, mat_a,
-
70  mat_b);
-
71  }
-
72  }
-
73  }
-
74  else {
-
75  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_b, blas::Op::NoTrans, op), mat_a, mat_b, op);
-
76 
-
77  if (uplo == blas::Uplo::Lower) {
-
78  if (op == blas::Op::NoTrans) {
-
79  multiplication::internal::Triangular<backend, device, T>::call_RLN(diag, alpha, mat_a, mat_b);
-
80  }
-
81  else {
-
82  multiplication::internal::Triangular<backend, device, T>::call_RLT(op, diag, alpha, mat_a,
-
83  mat_b);
-
84  }
-
85  }
-
86  else {
-
87  if (op == blas::Op::NoTrans) {
-
88  multiplication::internal::Triangular<backend, device, T>::call_RUN(diag, alpha, mat_a, mat_b);
+
68  }
+
69  else {
+
70  if (op == blas::Op::NoTrans) {
+
71  multiplication::internal::Triangular<backend, device, T>::call_LUN(diag, alpha, mat_a, mat_b);
+
72  }
+
73  else {
+
74  multiplication::internal::Triangular<backend, device, T>::call_LUT(op, diag, alpha, mat_a,
+
75  mat_b);
+
76  }
+
77  }
+
78  }
+
79  else {
+
80  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_b, blas::Op::NoTrans, op), mat_a, mat_b, op);
+
81 
+
82  if (uplo == blas::Uplo::Lower) {
+
83  if (op == blas::Op::NoTrans) {
+
84  multiplication::internal::Triangular<backend, device, T>::call_RLN(diag, alpha, mat_a, mat_b);
+
85  }
+
86  else {
+
87  multiplication::internal::Triangular<backend, device, T>::call_RLT(op, diag, alpha, mat_a,
+
88  mat_b);
89  }
-
90  else {
-
91  multiplication::internal::Triangular<backend, device, T>::call_RUT(op, diag, alpha, mat_a,
-
92  mat_b);
-
93  }
-
94  }
-
95  }
-
96 }
-
97 
-
116 template <Backend backend, Device device, class T>
-
117 void triangular_multiplication(comm::CommunicatorGrid grid, blas::Side side, blas::Uplo uplo,
-
118  blas::Op op, blas::Diag diag, T alpha, Matrix<const T, device>& mat_a,
-
119  Matrix<T, device>& mat_b) {
-
120  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
121  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
122  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
123  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
124  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
-
125  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
-
126 
-
127  if (side == blas::Side::Left) {
-
128  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_b, op, blas::Op::NoTrans), mat_a, mat_b, op);
-
129 
-
130  if (uplo == blas::Uplo::Lower) {
-
131  if (op == blas::Op::NoTrans) {
-
132  multiplication::internal::Triangular<backend, device, T>::call_LLN(grid, diag, alpha, mat_a,
-
133  mat_b);
-
134  }
-
135  else {
-
136  // Left Lower Trans/ConjTrans
-
137  DLAF_UNIMPLEMENTED(side, uplo, op, diag);
-
138  }
-
139  }
-
140  else {
+
90  }
+
91  else {
+
92  if (op == blas::Op::NoTrans) {
+
93  multiplication::internal::Triangular<backend, device, T>::call_RUN(diag, alpha, mat_a, mat_b);
+
94  }
+
95  else {
+
96  multiplication::internal::Triangular<backend, device, T>::call_RUT(op, diag, alpha, mat_a,
+
97  mat_b);
+
98  }
+
99  }
+
100  }
+
101 }
+
102 
+
126 template <Backend backend, Device device, class T>
+
127 void triangular_multiplication(comm::CommunicatorGrid grid, blas::Side side, blas::Uplo uplo,
+
128  blas::Op op, blas::Diag diag, T alpha, Matrix<const T, device>& mat_a,
+
129  Matrix<T, device>& mat_b) {
+
130  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
131  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
132  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
133  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
134  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
+
135  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
+
136 
+
137  if (side == blas::Side::Left) {
+
138  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_b, op, blas::Op::NoTrans), mat_a, mat_b, op);
+
139 
+
140  if (uplo == blas::Uplo::Lower) {
141  if (op == blas::Op::NoTrans) {
-
142  multiplication::internal::Triangular<backend, device, T>::call_LUN(grid, diag, alpha, mat_a,
+
142  multiplication::internal::Triangular<backend, device, T>::call_LLN(grid, diag, alpha, mat_a,
143  mat_b);
144  }
145  else {
-
146  // Left Upper Trans/ConjTrans
+
146  // Left Lower Trans/ConjTrans
147  DLAF_UNIMPLEMENTED(side, uplo, op, diag);
148  }
149  }
-
150  }
-
151  else {
-
152  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_b, blas::Op::NoTrans, op), mat_a, mat_b, op);
-
153 
-
154  if (uplo == blas::Uplo::Lower) {
-
155  if (op == blas::Op::NoTrans) {
-
156  multiplication::internal::Triangular<backend, device, T>::call_RLN(grid, diag, alpha, mat_a,
-
157  mat_b);
+
150  else {
+
151  if (op == blas::Op::NoTrans) {
+
152  multiplication::internal::Triangular<backend, device, T>::call_LUN(grid, diag, alpha, mat_a,
+
153  mat_b);
+
154  }
+
155  else {
+
156  // Left Upper Trans/ConjTrans
+
157  DLAF_UNIMPLEMENTED(side, uplo, op, diag);
158  }
-
159  else {
-
160  // Right Lower Trans/ConjTrans
-
161  DLAF_UNIMPLEMENTED(side, uplo, op, diag);
-
162  }
-
163  }
-
164  else {
+
159  }
+
160  }
+
161  else {
+
162  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_b, blas::Op::NoTrans, op), mat_a, mat_b, op);
+
163 
+
164  if (uplo == blas::Uplo::Lower) {
165  if (op == blas::Op::NoTrans) {
-
166  multiplication::internal::Triangular<backend, device, T>::call_RUN(grid, diag, alpha, mat_a,
+
166  multiplication::internal::Triangular<backend, device, T>::call_RLN(grid, diag, alpha, mat_a,
167  mat_b);
168  }
169  else {
-
170  // Right Upper Trans/ConjTrans
+
170  // Right Lower Trans/ConjTrans
171  DLAF_UNIMPLEMENTED(side, uplo, op, diag);
172  }
173  }
-
174  }
-
175 }
-
176 }
+
174  else {
+
175  if (op == blas::Op::NoTrans) {
+
176  multiplication::internal::Triangular<backend, device, T>::call_RUN(grid, diag, alpha, mat_a,
+
177  mat_b);
+
178  }
+
179  else {
+
180  // Right Upper Trans/ConjTrans
+
181  DLAF_UNIMPLEMENTED(side, uplo, op, diag);
+
182  }
+
183  }
+
184  }
+
185 }
+
186 }
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
communicator_grid.h
-
dlaf::triangular_multiplication
void triangular_multiplication(blas::Side side, blas::Uplo uplo, blas::Op op, blas::Diag diag, T alpha, Matrix< const T, device > &mat_a, Matrix< T, device > &mat_b)
Definition: triangular.h:43
+
dlaf::triangular_multiplication
void triangular_multiplication(blas::Side side, blas::Uplo uplo, blas::Op op, blas::Diag diag, T alpha, Matrix< const T, device > &mat_a, Matrix< T, device > &mat_b)
Definition: triangular.h:48
util_matrix.h
diff --git a/master/norm_8h.html b/master/norm_8h.html index 43f286bc06..543615284d 100644 --- a/master/norm_8h.html +++ b/master/norm_8h.html @@ -131,11 +131,11 @@

Compute the max norm of the distributed Matrix A (ge/sy/he)

Note
uplo == blas::uplo::Upper not yet implemented
-
Precondition
A.blockSize().rows() == A.blockSize().cols(),
+
Precondition
A is distributed according to grid
-A is distributed according to grid,
+A has blocksize (NB x NB)
-A has equal tile and block sizes,
+A has tilesize (NB x NB)
Returns
the max norm of the Matrix A or 0 if A.size().isEmpty()
diff --git a/master/permutations_2general_8h.html b/master/permutations_2general_8h.html index 6ab01fc2a9..9c55060bd1 100644 --- a/master/permutations_2general_8h.html +++ b/master/permutations_2general_8h.html @@ -161,18 +161,47 @@

Template Parameters
- +
coordis Coord::Col, a row communicator pipeline is expected, otherwise if
isCoord::Row a column communicator pipeline is expected.
isCoord::Row a column communicator pipeline is expected.
Parameters
+
permsis the index map of permutations represented as a local tiled column vector. Indices are in the range [0, n) where n is the global size of the submatrix (i.e. submatrix indices are used instead of the full matrix indices). Only tiles whose row tile coords are in the range [i_begin,i_end) are accessed in read-only mode.
+
+
+
Precondition
perms is not distributed
+
+perms has blocksize (NB x MB)
+
+perms has tilesize (NB x MB)
+
Parameters
+ -
mat_inis the distributed input matrix. Only tiles whose both global row and col tile coords are in the range [i_begin,i_end) are accessed in readwrite-mode.
mat_outis the distributed output matrix. Only tiles whose both global row and col tile coords are in the range [i_begin,i_end) are accessed in readwrite-mode.
+
Precondition
mat_in is distributed according to grid
+
+mat_in has size (N x N)
+
+mat_in has blocksize (NB x NB)
+
+mat_in has tilesize (NB x NB)
+
Parameters
+ + +
mat_outis the distributed output matrix. Only tiles whose both global row and col tile coords are in the range [i_begin,i_end) are accessed in readwrite-mode.
+
+
+
Precondition
mat_out is distributed according to grid
+
+mat_out has size (N x N)
+
+mat_out has blocksize (NB x NB)
+
+mat_out has tilesize (NB x NB)

Note: The Pipeline<> API allows to use permute() within other algorithms without having to clone communicators internally.

@@ -226,11 +255,38 @@

Parameters
+
permsis the index map of permutations represented as a tiled column vector. Indices are in the range [0, n) where n is the size of the submatrix (i.e. the indices are local to the submatrix, they are not global). Only tiles whose row tile coords are in the range [i_begin,i_end) are accessed in read-only mode.
+
+ +
Precondition
perms is not distributed
+
+perms has blocksize (NB x MB)
+
+perms has tilesize (NB x MB)
+
Parameters
+ +
mat_inis the input matrix. Only tiles whose both row and col tile coords are in the range [i_begin,i_end) are accessed in read-only mode.
+
+
+
Precondition
mat_in is not distributed
+
+mat_in has size (N x N)
+
+mat_in has blocksize (NB x NB)
+
+mat_in has tilesize (NB x NB)
+
Parameters
+
mat_outis the output matrix. Only tiles whose both row and col tile coords are in the range [i_begin,i_end) are accessed in write-only mode.
+
Precondition
mat_out has size (N x N)
+
+mat_out has blocksize (NB x NB)
+
+mat_out has tilesize (NB x NB)
diff --git a/master/permutations_2general_8h_source.html b/master/permutations_2general_8h_source.html index affdb84dff..866afe7b15 100644 --- a/master/permutations_2general_8h_source.html +++ b/master/permutations_2general_8h_source.html @@ -92,82 +92,82 @@
22 
23 namespace dlaf::permutations {
24 
-
38 template <Backend B, Device D, class T, Coord coord>
-
39 void permute(SizeType i_begin, SizeType i_end, Matrix<const SizeType, D>& perms,
-
40  Matrix<const T, D>& mat_in, Matrix<T, D>& mat_out) {
-
41  DLAF_ASSERT(matrix::local_matrix(perms), perms);
-
42  DLAF_ASSERT(matrix::local_matrix(mat_in), mat_in);
-
43  DLAF_ASSERT(matrix::local_matrix(mat_out), mat_out);
-
44 
-
45  // Note:
-
46  // These are not implementation constraints, but more logic constraints. Indeed, these ensure that
-
47  // the range [i_begin, i_end] is square in terms of elements (it would not make sense to have it square
-
48  // in terms of number of tiles). Moreover, by requiring mat_in and mat_out matrices to have the same
-
49  // shape, it is ensured that range [i_begin, i_end] is actually the same on both sides.
-
50  DLAF_ASSERT(square_size(mat_in), mat_in);
-
51  DLAF_ASSERT(matrix::square_blocksize(mat_in), mat_in);
-
52  DLAF_ASSERT(matrix::equal_size(mat_in, mat_out), mat_in);
-
53  DLAF_ASSERT(matrix::equal_blocksize(mat_in, mat_out), mat_in);
-
54 
-
55  DLAF_ASSERT(matrix::single_tile_per_block(perms), perms);
-
56  DLAF_ASSERT(matrix::single_tile_per_block(mat_in), mat_in);
-
57  DLAF_ASSERT(matrix::single_tile_per_block(mat_out), mat_out);
-
58 
-
59  DLAF_ASSERT(perms.size().rows() == mat_in.size().rows(), perms, mat_in);
-
60  DLAF_ASSERT(perms.size().cols() == 1, perms);
-
61  DLAF_ASSERT(perms.blockSize().rows() == mat_in.blockSize().rows(), mat_in, perms);
-
62 
-
63  DLAF_ASSERT(i_begin >= 0 && i_begin <= i_end, i_begin, i_end);
-
64  DLAF_ASSERT(i_end <= perms.nrTiles().rows(), i_end, perms);
+
49 template <Backend B, Device D, class T, Coord coord>
+
50 void permute(SizeType i_begin, SizeType i_end, Matrix<const SizeType, D>& perms,
+
51  Matrix<const T, D>& mat_in, Matrix<T, D>& mat_out) {
+
52  DLAF_ASSERT(matrix::local_matrix(perms), perms);
+
53  DLAF_ASSERT(matrix::local_matrix(mat_in), mat_in);
+
54  DLAF_ASSERT(matrix::local_matrix(mat_out), mat_out);
+
55 
+
56  // Note:
+
57  // These are not implementation constraints, but more logic constraints. Indeed, these ensure that
+
58  // the range [i_begin, i_end] is square in terms of elements (it would not make sense to have it square
+
59  // in terms of number of tiles). Moreover, by requiring mat_in and mat_out matrices to have the same
+
60  // shape, it is ensured that range [i_begin, i_end] is actually the same on both sides.
+
61  DLAF_ASSERT(square_size(mat_in), mat_in);
+
62  DLAF_ASSERT(matrix::square_blocksize(mat_in), mat_in);
+
63  DLAF_ASSERT(matrix::equal_size(mat_in, mat_out), mat_in);
+
64  DLAF_ASSERT(matrix::equal_blocksize(mat_in, mat_out), mat_in);
65 
-
66  internal::Permutations<B, D, T, coord>::call(i_begin, i_end, perms, mat_in, mat_out);
-
67 }
-
68 
-
89 template <Backend B, Device D, class T, Coord coord>
-
90 void permute(comm::CommunicatorGrid grid, common::Pipeline<comm::Communicator>& sub_task_chain,
-
91  SizeType i_begin, SizeType i_end, Matrix<const SizeType, D>& perms,
-
92  Matrix<const T, D>& mat_in, Matrix<T, D>& mat_out) {
-
93  DLAF_ASSERT(matrix::local_matrix(perms), perms);
-
94  DLAF_ASSERT(matrix::equal_process_grid(mat_in, grid), mat_in, grid);
-
95  DLAF_ASSERT(matrix::equal_process_grid(mat_out, grid), mat_out, grid);
-
96 
-
97  // Note:
-
98  // These are not implementation constraints, but more logic constraints. Indeed, these ensure that
-
99  // the range [i_begin, i_end] is square in terms of elements (it would not make sense to have it square
-
100  // in terms of number of tiles). Moreover, by requiring mat_in and mat_out matrices to have the same
-
101  // shape, it is ensured that range [i_begin, i_end] is actually the same on both sides.
-
102  DLAF_ASSERT(square_size(mat_in), mat_in);
-
103  DLAF_ASSERT(matrix::square_blocksize(mat_in), mat_in);
-
104  DLAF_ASSERT(matrix::equal_size(mat_in, mat_out), mat_in);
-
105  DLAF_ASSERT(matrix::equal_blocksize(mat_in, mat_out), mat_in);
-
106 
-
107  DLAF_ASSERT(matrix::single_tile_per_block(perms), perms);
-
108  DLAF_ASSERT(matrix::single_tile_per_block(mat_in), mat_in);
-
109  DLAF_ASSERT(matrix::single_tile_per_block(mat_out), mat_out);
-
110 
-
111  DLAF_ASSERT(perms.size().rows() == mat_in.size().rows(), perms, mat_in);
-
112  DLAF_ASSERT(perms.size().cols() == 1, perms);
-
113  DLAF_ASSERT(perms.blockSize().rows() == mat_in.blockSize().rows(), mat_in, perms);
-
114 
-
115  DLAF_ASSERT(i_begin >= 0 && i_begin <= i_end, i_begin, i_end);
-
116  DLAF_ASSERT(i_end <= perms.nrTiles().rows(), i_end, perms);
-
117 
-
118  internal::Permutations<B, D, T, coord>::call(sub_task_chain, i_begin, i_end, perms, mat_in, mat_out);
-
119 }
-
120 
-
126 template <Backend B, Device D, class T, Coord coord>
-
127 void permute(comm::CommunicatorGrid grid, SizeType i_begin, SizeType i_end,
-
128  Matrix<const SizeType, D>& perms, Matrix<const T, D>& mat_in, Matrix<T, D>& mat_out) {
-
129  common::Pipeline<comm::Communicator> sub_task_chain(grid.subCommunicator(orthogonal(coord)).clone());
-
130  permute<B, D, T, coord>(grid, sub_task_chain, i_begin, i_end, perms, mat_in, mat_out);
-
131 }
-
132 }
+
66  DLAF_ASSERT(matrix::single_tile_per_block(perms), perms);
+
67  DLAF_ASSERT(matrix::single_tile_per_block(mat_in), mat_in);
+
68  DLAF_ASSERT(matrix::single_tile_per_block(mat_out), mat_out);
+
69 
+
70  DLAF_ASSERT(perms.size().rows() == mat_in.size().rows(), perms, mat_in);
+
71  DLAF_ASSERT(perms.size().cols() == 1, perms);
+
72  DLAF_ASSERT(perms.blockSize().rows() == mat_in.blockSize().rows(), mat_in, perms);
+
73 
+
74  DLAF_ASSERT(i_begin >= 0 && i_begin <= i_end, i_begin, i_end);
+
75  DLAF_ASSERT(i_end <= perms.nrTiles().rows(), i_end, perms);
+
76 
+
77  internal::Permutations<B, D, T, coord>::call(i_begin, i_end, perms, mat_in, mat_out);
+
78 }
+
79 
+
114 template <Backend B, Device D, class T, Coord coord>
+
115 void permute(comm::CommunicatorGrid grid, common::Pipeline<comm::Communicator>& sub_task_chain,
+
116  SizeType i_begin, SizeType i_end, Matrix<const SizeType, D>& perms,
+
117  Matrix<const T, D>& mat_in, Matrix<T, D>& mat_out) {
+
118  DLAF_ASSERT(matrix::local_matrix(perms), perms);
+
119  DLAF_ASSERT(matrix::equal_process_grid(mat_in, grid), mat_in, grid);
+
120  DLAF_ASSERT(matrix::equal_process_grid(mat_out, grid), mat_out, grid);
+
121 
+
122  // Note:
+
123  // These are not implementation constraints, but more logic constraints. Indeed, these ensure that
+
124  // the range [i_begin, i_end] is square in terms of elements (it would not make sense to have it square
+
125  // in terms of number of tiles). Moreover, by requiring mat_in and mat_out matrices to have the same
+
126  // shape, it is ensured that range [i_begin, i_end] is actually the same on both sides.
+
127  DLAF_ASSERT(square_size(mat_in), mat_in);
+
128  DLAF_ASSERT(matrix::square_blocksize(mat_in), mat_in);
+
129  DLAF_ASSERT(matrix::equal_size(mat_in, mat_out), mat_in);
+
130  DLAF_ASSERT(matrix::equal_blocksize(mat_in, mat_out), mat_in);
+
131 
+
132  DLAF_ASSERT(matrix::single_tile_per_block(perms), perms);
+
133  DLAF_ASSERT(matrix::single_tile_per_block(mat_in), mat_in);
+
134  DLAF_ASSERT(matrix::single_tile_per_block(mat_out), mat_out);
+
135 
+
136  DLAF_ASSERT(perms.size().rows() == mat_in.size().rows(), perms, mat_in);
+
137  DLAF_ASSERT(perms.size().cols() == 1, perms);
+
138  DLAF_ASSERT(perms.blockSize().rows() == mat_in.blockSize().rows(), mat_in, perms);
+
139 
+
140  DLAF_ASSERT(i_begin >= 0 && i_begin <= i_end, i_begin, i_end);
+
141  DLAF_ASSERT(i_end <= perms.nrTiles().rows(), i_end, perms);
+
142 
+
143  internal::Permutations<B, D, T, coord>::call(sub_task_chain, i_begin, i_end, perms, mat_in, mat_out);
+
144 }
+
145 
+
150 template <Backend B, Device D, class T, Coord coord>
+
151 void permute(comm::CommunicatorGrid grid, SizeType i_begin, SizeType i_end,
+
152  Matrix<const SizeType, D>& perms, Matrix<const T, D>& mat_in, Matrix<T, D>& mat_out) {
+
153  common::Pipeline<comm::Communicator> sub_task_chain(grid.subCommunicator(orthogonal(coord)).clone());
+
154  permute<B, D, T, coord>(grid, sub_task_chain, i_begin, i_end, perms, mat_in, mat_out);
+
155 }
+
156 }
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
dlaf::common::Pipeline
Definition: pipeline.h:31
communicator_grid.h
dlaf::orthogonal
constexpr Coord orthogonal(const Coord rc)
Given a Coord, returns its orthogonal.
Definition: index2d.h:30
-
dlaf::permutations::permute
void permute(SizeType i_begin, SizeType i_end, Matrix< const SizeType, D > &perms, Matrix< const T, D > &mat_in, Matrix< T, D > &mat_out)
Definition: general.h:39
+
dlaf::permutations::permute
void permute(SizeType i_begin, SizeType i_end, Matrix< const SizeType, D > &perms, Matrix< const T, D > &mat_in, Matrix< T, D > &mat_out)
Definition: general.h:50
util_matrix.h
diff --git a/master/reduction__to__band_8h.html b/master/reduction__to__band_8h.html index 6791c51692..76a1a4b979 100644 --- a/master/reduction__to__band_8h.html +++ b/master/reduction__to__band_8h.html @@ -142,22 +142,26 @@

Parameters
- + -
gridis the CommunicatorGrid on which mat_a is distributed
gridis the CommunicatorGrid on which mat_a is distributed
mat_aon entry it contains an Hermitian matrix, on exit it is overwritten with the band-diagonal result together with the elementary reflectors as described above. Just the tiles of the lower triangular part will be used.
band_sizesize of the band of the resulting matrix (main diagonal + band_size sub-diagonals)
-
Returns
the tau vector as needed by backtransformationReductionToBand
-
Precondition
mat_a has a square size
-
-mat_a has a square block size
+
Precondition
mat_a is distributed according to grid
-mat_a has equal tile and block sizes
+mat_a has size (N x N)
-mat_a is distributed according to grid
+mat_a has blocksize (NB x NB)
-mat_a.blockSize().rows() % band_size == 0
+mat_a has tilesize (NB x NB)
+
Parameters
+ + +
band_sizesize of the band of the resulting matrix (main diagonal + band_size sub-diagonals)
+
+
+
Precondition
mat_a.blockSize().rows() % band_size == 0
+
Returns
the tau vector as needed by backtransformationReductionToBand
@@ -192,20 +196,24 @@

Parameters
-
mat_aon entry it contains an Hermitian matrix, on exit it is overwritten with the band-diagonal result together with the elementary reflectors. Just the tiles of the lower triangular part will be used.
band_sizesize of the band of the resulting matrix (main diagonal + band_size sub-diagonals)
-
Returns
the tau vector as needed by backtransformationReductionToBand
-
Precondition
mat_a has a square size
-
-mat_a has a square block size
+
Precondition
mat_a is not distributed
-mat_a has equal tile and block sizes
+mat_a has size (N x N)
-mat_a is a local matrix
+mat_a has blocksize (NB x NB)
-mat_a.blockSize().rows() % band_size == 0
+mat_a has tilesize (NB x NB)
+
Parameters
+ + +
band_sizesize of the band of the resulting matrix (main diagonal + band_size sub-diagonals)
+
+
+
Precondition
mat_a.blockSize().rows() % band_size == 0
+
Returns
the tau vector as needed by backtransformationReductionToBand
diff --git a/master/reduction__to__band_8h_source.html b/master/reduction__to__band_8h_source.html index 0ab4df3ae9..901ae4a5e1 100644 --- a/master/reduction__to__band_8h_source.html +++ b/master/reduction__to__band_8h_source.html @@ -91,40 +91,40 @@
21 namespace dlaf::eigensolver::internal {
22 
26 //
-
38 template <Backend B, Device D, class T>
-
39 Matrix<T, Device::CPU> reduction_to_band(Matrix<T, D>& mat_a, const SizeType band_size) {
-
40  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
41  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
42  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
43 
-
44  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
40 template <Backend B, Device D, class T>
+
41 Matrix<T, Device::CPU> reduction_to_band(Matrix<T, D>& mat_a, const SizeType band_size) {
+
42  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
43  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
44  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
45 
-
46  DLAF_ASSERT(band_size >= 2, band_size);
-
47  DLAF_ASSERT(mat_a.blockSize().rows() % band_size == 0, mat_a.blockSize().rows(), band_size);
-
48 
-
49  return ReductionToBand<B, D, T>::call(mat_a, band_size);
-
50 }
-
51 
-
68 
-
84 
-
105 template <Backend B, Device D, class T>
-
106 Matrix<T, Device::CPU> reduction_to_band(comm::CommunicatorGrid grid, Matrix<T, D>& mat_a,
-
107  const SizeType band_size) {
-
108  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
109  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
110  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
111  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
-
112 
-
113  DLAF_ASSERT(band_size >= 2, band_size);
-
114  DLAF_ASSERT(mat_a.blockSize().rows() % band_size == 0, mat_a.blockSize().rows(), band_size);
-
115 
-
116  return ReductionToBand<B, D, T>::call(grid, mat_a, band_size);
-
117 }
-
118 }
+
46  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
47 
+
48  DLAF_ASSERT(band_size >= 2, band_size);
+
49  DLAF_ASSERT(mat_a.blockSize().rows() % band_size == 0, mat_a.blockSize().rows(), band_size);
+
50 
+
51  return ReductionToBand<B, D, T>::call(mat_a, band_size);
+
52 }
+
53 
+
70 
+
86 
+
109 template <Backend B, Device D, class T>
+
110 Matrix<T, Device::CPU> reduction_to_band(comm::CommunicatorGrid grid, Matrix<T, D>& mat_a,
+
111  const SizeType band_size) {
+
112  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
113  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
114  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
115  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
+
116 
+
117  DLAF_ASSERT(band_size >= 2, band_size);
+
118  DLAF_ASSERT(mat_a.blockSize().rows() % band_size == 0, mat_a.blockSize().rows(), band_size);
+
119 
+
120  return ReductionToBand<B, D, T>::call(grid, mat_a, band_size);
+
121 }
+
122 }
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
communicator_grid.h
-
dlaf::eigensolver::internal::reduction_to_band
Matrix< T, Device::CPU > reduction_to_band(Matrix< T, D > &mat_a, const SizeType band_size)
Definition: reduction_to_band.h:39
+
dlaf::eigensolver::internal::reduction_to_band
Matrix< T, Device::CPU > reduction_to_band(Matrix< T, D > &mat_a, const SizeType band_size)
Definition: reduction_to_band.h:41
util_matrix.h
diff --git a/master/rot_8h_source.html b/master/rot_8h_source.html index 65162c64b0..11f5aafee5 100644 --- a/master/rot_8h_source.html +++ b/master/rot_8h_source.html @@ -236,12 +236,12 @@
165  common::internal::SingleThreadedBlasScope single;
166 
167  for (const GivensRotation<T>& rot : rots) {
-
168  // Get the index of the tile that has column `rot.i` and the the index of the column within the tile.
+
168  // Get the index of the tile that has column `rot.i` and the index of the column within the tile.
169  const SizeType i_tile = distr.globalTileLinearIndex(GlobalElementIndex(0, rot.i));
170  const SizeType i_el = distr.tileElementFromGlobalElement<Coord::Col>(rot.i);
171  T* x = tiles[to_sizet(i_tile)].ptr(TileElementIndex(0, i_el));
172 
-
173  // Get the index of the tile that has column `rot.j` and the the index of the column within the tile.
+
173  // Get the index of the tile that has column `rot.j` and the index of the column within the tile.
174  const SizeType j_tile = distr.globalTileLinearIndex(GlobalElementIndex(0, rot.j));
175  const SizeType j_el = distr.tileElementFromGlobalElement<Coord::Col>(rot.j);
176  T* y = tiles[to_sizet(j_tile)].ptr(TileElementIndex(0, j_el));
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['start_5foffset_5f_1173',['start_offset_',['../structdlaf_1_1matrix_1_1_panel_3_01axis_00_01const_01_t_00_01_d_00_01_store_transposed_1_1_no_01_4.html#a8603ffce77527952a466a9630d98cd5c',1,'dlaf::matrix::Panel< axis, const T, D, StoreTransposed::No >']]] + ['start_5f_1174',['start_',['../structdlaf_1_1matrix_1_1_panel_3_01axis_00_01const_01_t_00_01_d_00_01_store_transposed_1_1_no_01_4.html#ac49583acc02b6ba394d9b4e3b7450e53',1,'dlaf::matrix::Panel< axis, const T, D, StoreTransposed::No >']]], + ['start_5foffset_5f_1175',['start_offset_',['../structdlaf_1_1matrix_1_1_panel_3_01axis_00_01const_01_t_00_01_d_00_01_store_transposed_1_1_no_01_4.html#a8603ffce77527952a466a9630d98cd5c',1,'dlaf::matrix::Panel< axis, const T, D, StoreTransposed::No >']]] ]; diff --git a/master/solver_2triangular_8h.html b/master/solver_2triangular_8h.html index 89907f9320..a21ffad0e2 100644 --- a/master/solver_2triangular_8h.html +++ b/master/solver_2triangular_8h.html @@ -153,21 +153,31 @@

sidespecifies whether op(A) appears on the Left or on the Right of matrix X, uplospecifies whether the matrix A is a Lower or Upper triangular matrix, opspecifies the form of op(A) to be used in the matrix multiplication: NoTrans, Trans, ConjTrans, - diagspecifies if the matrix A is assumed to be unit triangular (Unit) or not (NonUnit), + diagspecifies if the matrix A is assumed to be unit triangular (Unit) or not (NonUnit), mat_acontains the triangular matrix A. Only the tiles of the matrix which contain the upper or the lower triangular part (depending on the value of uplo) are accessed in read-only mode (the elements are not modified), - mat_bon entry it contains the matrix B, on exit the matrix elements are overwritten with the elements of the matrix X, -
Precondition
mat_a has a square size,
+
Precondition
mat_a is not distributed
+
+mat_a has size (N x M)
+
+mat_a has blocksize (NB x NB)
-mat_a has a square block size,
+mat_a has tilesize (NB x NB)
+
Parameters
+ + +
mat_bon entry it contains the matrix B, on exit the matrix elements are overwritten with the elements of the matrix X,
+
+
+
Precondition
mat_b is not distributed
-mat_a and mat_b have equal tile and block size,
+mat_b has size (M x K)
-mat_a and mat_b are not distributed,
+mat_b has blocksize (NB x NB)
-mat_a and mat_b are multipliable.
+mat_b has tilesize (NB x NB)
@@ -240,21 +250,31 @@

sidespecifies whether op(A) appears on the Left or on the Right of matrix X, uplospecifies whether the matrix A is a Lower or Upper triangular matrix, opspecifies the form of op(A) to be used in the matrix multiplication: NoTrans, Trans, ConjTrans, - diagspecifies if the matrix A is assumed to be unit triangular (Unit) or not (NonUnit), + diagspecifies if the matrix A is assumed to be unit triangular (Unit) or not (NonUnit), mat_acontains the triangular matrix A. Only the tiles of the matrix which contain the upper or the lower triangular part (depending on the value of uplo) are accessed in read-only mode (the elements are not modified), - mat_bon entry it contains the matrix B, on exit the matrix elements are overwritten with the elements of the matrix X, -
Precondition
matrix A has a square size,
+
Precondition
mat_b is distributed according to grid
+
+mat_b has size (N x M)
+
+mat_b has blocksize (NB x NB)
-matrix A has a square block size,
+mat_b has tilesize (NB x NB)
+
Parameters
+ + +
mat_bon entry it contains the matrix B, on exit the matrix elements are overwritten with the elements of the matrix X,
+
+
+
Precondition
mat_b is distributed according to grid
-matrix A and matrix B have equal tile and block sizes,
+mat_b has size (M x K)
-matrix A and matrix B are distributed according to the grid,
+mat_b has blocksize (NB x NB)
-matrix A and matrix B are multipliable.
+mat_b has tilesize (NB x NB)
diff --git a/master/solver_2triangular_8h_source.html b/master/solver_2triangular_8h_source.html index f5c4f09b15..65c20e824d 100644 --- a/master/solver_2triangular_8h_source.html +++ b/master/solver_2triangular_8h_source.html @@ -91,116 +91,116 @@
21 
22 namespace dlaf {
23 
-
42 template <Backend backend, Device device, class T>
-
43 void triangular_solver(blas::Side side, blas::Uplo uplo, blas::Op op, blas::Diag diag, T alpha,
-
44  Matrix<const T, device>& mat_a, Matrix<T, device>& mat_b) {
-
45  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
46  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
47  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
48  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
49  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
-
50  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
-
51 
-
52  if (side == blas::Side::Left) {
-
53  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_b, op, blas::Op::NoTrans), mat_a, mat_b, op);
-
54 
-
55  if (uplo == blas::Uplo::Lower) {
-
56  if (op == blas::Op::NoTrans) {
-
57  solver::internal::Triangular<backend, device, T>::call_LLN(diag, alpha, mat_a, mat_b);
-
58  }
-
59  else {
-
60  solver::internal::Triangular<backend, device, T>::call_LLT(op, diag, alpha, mat_a, mat_b);
-
61  }
-
62  }
-
63  else {
-
64  if (op == blas::Op::NoTrans) {
-
65  solver::internal::Triangular<backend, device, T>::call_LUN(diag, alpha, mat_a, mat_b);
+
47 template <Backend backend, Device device, class T>
+
48 void triangular_solver(blas::Side side, blas::Uplo uplo, blas::Op op, blas::Diag diag, T alpha,
+
49  Matrix<const T, device>& mat_a, Matrix<T, device>& mat_b) {
+
50  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
51  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
52  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
53  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
54  DLAF_ASSERT(matrix::local_matrix(mat_a), mat_a);
+
55  DLAF_ASSERT(matrix::local_matrix(mat_b), mat_b);
+
56 
+
57  if (side == blas::Side::Left) {
+
58  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_b, op, blas::Op::NoTrans), mat_a, mat_b, op);
+
59 
+
60  if (uplo == blas::Uplo::Lower) {
+
61  if (op == blas::Op::NoTrans) {
+
62  solver::internal::Triangular<backend, device, T>::call_LLN(diag, alpha, mat_a, mat_b);
+
63  }
+
64  else {
+
65  solver::internal::Triangular<backend, device, T>::call_LLT(op, diag, alpha, mat_a, mat_b);
66  }
-
67  else {
-
68  solver::internal::Triangular<backend, device, T>::call_LUT(op, diag, alpha, mat_a, mat_b);
-
69  }
-
70  }
-
71  }
-
72  else {
-
73  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_b, blas::Op::NoTrans, op), mat_a, mat_b, op);
-
74 
-
75  if (uplo == blas::Uplo::Lower) {
-
76  if (op == blas::Op::NoTrans) {
-
77  solver::internal::Triangular<backend, device, T>::call_RLN(diag, alpha, mat_a, mat_b);
-
78  }
-
79  else {
-
80  solver::internal::Triangular<backend, device, T>::call_RLT(op, diag, alpha, mat_a, mat_b);
-
81  }
-
82  }
-
83  else {
-
84  if (op == blas::Op::NoTrans) {
-
85  solver::internal::Triangular<backend, device, T>::call_RUN(diag, alpha, mat_a, mat_b);
+
67  }
+
68  else {
+
69  if (op == blas::Op::NoTrans) {
+
70  solver::internal::Triangular<backend, device, T>::call_LUN(diag, alpha, mat_a, mat_b);
+
71  }
+
72  else {
+
73  solver::internal::Triangular<backend, device, T>::call_LUT(op, diag, alpha, mat_a, mat_b);
+
74  }
+
75  }
+
76  }
+
77  else {
+
78  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_b, blas::Op::NoTrans, op), mat_a, mat_b, op);
+
79 
+
80  if (uplo == blas::Uplo::Lower) {
+
81  if (op == blas::Op::NoTrans) {
+
82  solver::internal::Triangular<backend, device, T>::call_RLN(diag, alpha, mat_a, mat_b);
+
83  }
+
84  else {
+
85  solver::internal::Triangular<backend, device, T>::call_RLT(op, diag, alpha, mat_a, mat_b);
86  }
-
87  else {
-
88  solver::internal::Triangular<backend, device, T>::call_RUT(op, diag, alpha, mat_a, mat_b);
-
89  }
-
90  }
-
91  }
-
92 }
-
93 
-
114 template <Backend backend, Device device, class T>
-
115 void triangular_solver(comm::CommunicatorGrid grid, blas::Side side, blas::Uplo uplo, blas::Op op,
-
116  blas::Diag diag, T alpha, Matrix<const T, device>& mat_a,
-
117  Matrix<T, device>& mat_b) {
-
118  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
-
119  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
-
120  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
-
121  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
-
122  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
-
123  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
-
124 
-
125  if (side == blas::Side::Left) {
-
126  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_b, op, blas::Op::NoTrans), mat_a, mat_b, op);
-
127 
-
128  if (uplo == blas::Uplo::Lower) {
-
129  if (op == blas::Op::NoTrans) {
-
130  solver::internal::Triangular<backend, device, T>::call_LLN(grid, diag, alpha, mat_a, mat_b);
-
131  }
-
132  else {
-
133  solver::internal::Triangular<backend, device, T>::call_LLT(grid, op, diag, alpha, mat_a, mat_b);
-
134  }
-
135  }
-
136  else {
-
137  if (op == blas::Op::NoTrans) {
-
138  solver::internal::Triangular<backend, device, T>::call_LUN(grid, diag, alpha, mat_a, mat_b);
-
139  }
-
140  else {
-
141  solver::internal::Triangular<backend, device, T>::call_LUT(grid, op, diag, alpha, mat_a, mat_b);
-
142  }
-
143  }
-
144  }
-
145  else {
-
146  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_b, blas::Op::NoTrans, op), mat_a, mat_b, op);
-
147 
-
148  if (uplo == blas::Uplo::Lower) {
-
149  if (op == blas::Op::NoTrans) {
-
150  solver::internal::Triangular<backend, device, T>::call_RLN(grid, diag, alpha, mat_a, mat_b);
-
151  }
-
152  else {
-
153  solver::internal::Triangular<backend, device, T>::call_RLT(grid, op, diag, alpha, mat_a, mat_b);
-
154  }
-
155  }
-
156  else {
-
157  if (op == blas::Op::NoTrans) {
-
158  solver::internal::Triangular<backend, device, T>::call_RUN(grid, diag, alpha, mat_a, mat_b);
-
159  }
-
160  else {
-
161  solver::internal::Triangular<backend, device, T>::call_RUT(grid, op, diag, alpha, mat_a, mat_b);
-
162  }
-
163  }
-
164  }
-
165 }
-
166 
-
167 }
+
87  }
+
88  else {
+
89  if (op == blas::Op::NoTrans) {
+
90  solver::internal::Triangular<backend, device, T>::call_RUN(diag, alpha, mat_a, mat_b);
+
91  }
+
92  else {
+
93  solver::internal::Triangular<backend, device, T>::call_RUT(op, diag, alpha, mat_a, mat_b);
+
94  }
+
95  }
+
96  }
+
97 }
+
98 
+
124 template <Backend backend, Device device, class T>
+
125 void triangular_solver(comm::CommunicatorGrid grid, blas::Side side, blas::Uplo uplo, blas::Op op,
+
126  blas::Diag diag, T alpha, Matrix<const T, device>& mat_a,
+
127  Matrix<T, device>& mat_b) {
+
128  DLAF_ASSERT(matrix::square_size(mat_a), mat_a);
+
129  DLAF_ASSERT(matrix::square_blocksize(mat_a), mat_a);
+
130  DLAF_ASSERT(matrix::single_tile_per_block(mat_a), mat_a);
+
131  DLAF_ASSERT(matrix::single_tile_per_block(mat_b), mat_b);
+
132  DLAF_ASSERT(matrix::equal_process_grid(mat_a, grid), mat_a, grid);
+
133  DLAF_ASSERT(matrix::equal_process_grid(mat_b, grid), mat_b, grid);
+
134 
+
135  if (side == blas::Side::Left) {
+
136  DLAF_ASSERT(matrix::multipliable(mat_a, mat_b, mat_b, op, blas::Op::NoTrans), mat_a, mat_b, op);
+
137 
+
138  if (uplo == blas::Uplo::Lower) {
+
139  if (op == blas::Op::NoTrans) {
+
140  solver::internal::Triangular<backend, device, T>::call_LLN(grid, diag, alpha, mat_a, mat_b);
+
141  }
+
142  else {
+
143  solver::internal::Triangular<backend, device, T>::call_LLT(grid, op, diag, alpha, mat_a, mat_b);
+
144  }
+
145  }
+
146  else {
+
147  if (op == blas::Op::NoTrans) {
+
148  solver::internal::Triangular<backend, device, T>::call_LUN(grid, diag, alpha, mat_a, mat_b);
+
149  }
+
150  else {
+
151  solver::internal::Triangular<backend, device, T>::call_LUT(grid, op, diag, alpha, mat_a, mat_b);
+
152  }
+
153  }
+
154  }
+
155  else {
+
156  DLAF_ASSERT(matrix::multipliable(mat_b, mat_a, mat_b, blas::Op::NoTrans, op), mat_a, mat_b, op);
+
157 
+
158  if (uplo == blas::Uplo::Lower) {
+
159  if (op == blas::Op::NoTrans) {
+
160  solver::internal::Triangular<backend, device, T>::call_RLN(grid, diag, alpha, mat_a, mat_b);
+
161  }
+
162  else {
+
163  solver::internal::Triangular<backend, device, T>::call_RLT(grid, op, diag, alpha, mat_a, mat_b);
+
164  }
+
165  }
+
166  else {
+
167  if (op == blas::Op::NoTrans) {
+
168  solver::internal::Triangular<backend, device, T>::call_RUN(grid, diag, alpha, mat_a, mat_b);
+
169  }
+
170  else {
+
171  solver::internal::Triangular<backend, device, T>::call_RUT(grid, op, diag, alpha, mat_a, mat_b);
+
172  }
+
173  }
+
174  }
+
175 }
+
176 
+
177 }
dlaf::comm::CommunicatorGrid
Definition: communicator_grid.h:42
communicator_grid.h
-
dlaf::triangular_solver
void triangular_solver(blas::Side side, blas::Uplo uplo, blas::Op op, blas::Diag diag, T alpha, Matrix< const T, device > &mat_a, Matrix< T, device > &mat_b)
Definition: triangular.h:43
+
dlaf::triangular_solver
void triangular_solver(blas::Side side, blas::Uplo uplo, blas::Op op, blas::Diag diag, T alpha, Matrix< const T, device > &mat_a, Matrix< T, device > &mat_b)
Definition: triangular.h:48
util_matrix.h
diff --git a/master/structdlaf_1_1comm_1_1internal_1_1_m_p_i_call_helper.html b/master/structdlaf_1_1comm_1_1internal_1_1_m_p_i_call_helper.html index da9e6e4556..696d5765ff 100644 --- a/master/structdlaf_1_1comm_1_1internal_1_1_m_p_i_call_helper.html +++ b/master/structdlaf_1_1comm_1_1internal_1_1_m_p_i_call_helper.html @@ -94,7 +94,7 @@ struct dlaf::comm::internal::MPICallHelper< F >

Helper type for wrapping MPI calls.

-

Wrapper type around calls to MPI functions. Provides a call operator that creates an MPI request and passes it as the last argument to the provided callable. The wrapper then waits for the the request to complete with yield_while.

+

Wrapper type around calls to MPI functions. Provides a call operator that creates an MPI request and passes it as the last argument to the provided callable. The wrapper then waits for the request to complete with yield_while.

This could in theory be a lambda inside transformMPI. However, clang at least until version 12 fails with an internal compiler error with a trailing decltype for SFINAE. GCC has no problems with a lambda.

The documentation for this struct was generated from the following file: