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rAKA akantu
sparse_matrix_aij.cc
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/**
* @file sparse_matrix_aij.cc
*
* @author Nicolas Richart <nicolas.richart@epfl.ch>
*
* @date creation: Fri Aug 21 2015
* @date last modification: Mon Dec 04 2017
*
* @brief Implementation of the AIJ sparse matrix
*
*
* Copyright (©) 2015-2018 EPFL (Ecole Polytechnique Fédérale de Lausanne)
* Laboratory (LSMS - Laboratoire de Simulation en Mécanique des Solides)
*
* Akantu is free software: you can redistribute it and/or modify it under the
* terms of the GNU Lesser General Public License as published by the Free
* Software Foundation, either version 3 of the License, or (at your option) any
* later version.
*
* Akantu is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
* A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
* details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Akantu. If not, see <http://www.gnu.org/licenses/>.
*
*/
/* -------------------------------------------------------------------------- */
#include "sparse_matrix_aij.hh"
#include "aka_iterators.hh"
#include "dof_manager_default.hh"
#include "dof_synchronizer.hh"
#include "solver_vector_default.hh"
#include "terms_to_assemble.hh"
/* -------------------------------------------------------------------------- */
#include <fstream>
/* -------------------------------------------------------------------------- */
namespace
akantu
{
/* -------------------------------------------------------------------------- */
SparseMatrixAIJ
::
SparseMatrixAIJ
(
DOFManagerDefault
&
dof_manager
,
const
MatrixType
&
matrix_type
,
const
ID
&
id
)
:
SparseMatrix
(
dof_manager
,
matrix_type
,
id
),
dof_manager
(
dof_manager
),
irn
(
0
,
1
,
id
+
":irn"
),
jcn
(
0
,
1
,
id
+
":jcn"
),
a
(
0
,
1
,
id
+
":a"
)
{}
/* -------------------------------------------------------------------------- */
SparseMatrixAIJ
::
SparseMatrixAIJ
(
const
SparseMatrixAIJ
&
matrix
,
const
ID
&
id
)
:
SparseMatrix
(
matrix
,
id
),
dof_manager
(
matrix
.
dof_manager
),
irn
(
matrix
.
irn
,
id
+
":irn"
),
jcn
(
matrix
.
jcn
,
id
+
":jcn"
),
a
(
matrix
.
a
,
id
+
":a"
)
{}
/* -------------------------------------------------------------------------- */
SparseMatrixAIJ
::~
SparseMatrixAIJ
()
=
default
;
/* -------------------------------------------------------------------------- */
void
SparseMatrixAIJ
::
applyBoundary
(
Real
block_val
)
{
AKANTU_DEBUG_IN
();
const
auto
&
blocked_dofs
=
this
->
dof_manager
.
getGlobalBlockedDOFs
();
auto
begin
=
blocked_dofs
.
begin
();
auto
end
=
blocked_dofs
.
end
();
auto
is_blocked
=
[
&
](
auto
&&
i
)
->
bool
{
auto
il
=
this
->
dof_manager
.
globalToLocalEquationNumber
(
i
);
return
std
::
binary_search
(
begin
,
end
,
il
);
};
for
(
auto
&&
ij_a
:
zip
(
irn
,
jcn
,
a
))
{
UInt
ni
=
std
::
get
<
0
>
(
ij_a
)
-
1
;
UInt
nj
=
std
::
get
<
1
>
(
ij_a
)
-
1
;
if
(
is_blocked
(
ni
)
or
is_blocked
(
nj
))
{
std
::
get
<
2
>
(
ij_a
)
=
std
::
get
<
0
>
(
ij_a
)
!=
std
::
get
<
1
>
(
ij_a
)
?
0.
:
this
->
dof_manager
.
isLocalOrMasterDOF
(
this
->
dof_manager
.
globalToLocalEquationNumber
(
ni
))
?
block_val
:
0.
;
}
}
this
->
value_release
++
;
AKANTU_DEBUG_OUT
();
}
/* -------------------------------------------------------------------------- */
void
SparseMatrixAIJ
::
saveProfile
(
const
std
::
string
&
filename
)
const
{
AKANTU_DEBUG_IN
();
std
::
ofstream
outfile
;
outfile
.
open
(
filename
.
c_str
());
UInt
m
=
this
->
size_
;
auto
&
comm
=
dof_manager
.
getCommunicator
();
// write header
if
(
comm
.
whoAmI
()
==
0
)
{
outfile
<<
"%%MatrixMarket matrix coordinate pattern"
;
if
(
this
->
matrix_type
==
_symmetric
)
{
outfile
<<
" symmetric"
;
}
else
{
outfile
<<
" general"
;
}
outfile
<<
std
::
endl
;
outfile
<<
m
<<
" "
<<
m
<<
" "
<<
this
->
nb_non_zero
<<
std
::
endl
;
}
for
(
auto
p
:
arange
(
comm
.
getNbProc
()))
{
// write content
if
(
comm
.
whoAmI
()
==
p
)
{
for
(
UInt
i
=
0
;
i
<
this
->
nb_non_zero
;
++
i
)
{
outfile
<<
this
->
irn
.
storage
()[
i
]
<<
" "
<<
this
->
jcn
.
storage
()[
i
]
<<
" 1"
<<
std
::
endl
;
}
}
comm
.
barrier
();
}
outfile
.
close
();
AKANTU_DEBUG_OUT
();
}
/* -------------------------------------------------------------------------- */
void
SparseMatrixAIJ
::
saveMatrix
(
const
std
::
string
&
filename
)
const
{
AKANTU_DEBUG_IN
();
auto
&
comm
=
dof_manager
.
getCommunicator
();
// open and set the properties of the stream
std
::
ofstream
outfile
;
if
(
0
==
comm
.
whoAmI
())
{
outfile
.
open
(
filename
.
c_str
());
}
else
{
outfile
.
open
(
filename
.
c_str
(),
std
::
ios_base
::
app
);
}
outfile
.
precision
(
std
::
numeric_limits
<
Real
>::
digits10
);
// write header
decltype
(
nb_non_zero
)
nnz
=
this
->
nb_non_zero
;
comm
.
allReduce
(
nnz
);
if
(
comm
.
whoAmI
()
==
0
)
{
outfile
<<
"%%MatrixMarket matrix coordinate real"
;
if
(
this
->
matrix_type
==
_symmetric
)
{
outfile
<<
" symmetric"
;
}
else
{
outfile
<<
" general"
;
}
outfile
<<
std
::
endl
;
outfile
<<
this
->
size_
<<
" "
<<
this
->
size_
<<
" "
<<
nnz
<<
std
::
endl
;
}
for
(
auto
p
:
arange
(
comm
.
getNbProc
()))
{
// write content
if
(
comm
.
whoAmI
()
==
p
)
{
for
(
UInt
i
=
0
;
i
<
this
->
nb_non_zero
;
++
i
)
{
outfile
<<
this
->
irn
(
i
)
<<
" "
<<
this
->
jcn
(
i
)
<<
" "
<<
this
->
a
(
i
)
<<
std
::
endl
;
}
}
comm
.
barrier
();
}
// time to end
outfile
.
close
();
AKANTU_DEBUG_OUT
();
}
/* -------------------------------------------------------------------------- */
void
SparseMatrixAIJ
::
matVecMul
(
const
Array
<
Real
>
&
x
,
Array
<
Real
>
&
y
,
Real
alpha
,
Real
beta
)
const
{
AKANTU_DEBUG_IN
();
y
*=
beta
;
auto
i_it
=
this
->
irn
.
begin
();
auto
j_it
=
this
->
jcn
.
begin
();
auto
a_it
=
this
->
a
.
begin
();
auto
a_end
=
this
->
a
.
end
();
auto
x_it
=
x
.
begin_reinterpret
(
x
.
size
()
*
x
.
getNbComponent
());
auto
y_it
=
y
.
begin_reinterpret
(
x
.
size
()
*
x
.
getNbComponent
());
for
(;
a_it
!=
a_end
;
++
i_it
,
++
j_it
,
++
a_it
)
{
Int
i
=
this
->
dof_manager
.
globalToLocalEquationNumber
(
*
i_it
-
1
);
Int
j
=
this
->
dof_manager
.
globalToLocalEquationNumber
(
*
j_it
-
1
);
const
Real
&
A
=
*
a_it
;
y_it
[
i
]
+=
alpha
*
A
*
x_it
[
j
];
if
((
this
->
matrix_type
==
_symmetric
)
&&
(
i
!=
j
))
{
y_it
[
j
]
+=
alpha
*
A
*
x_it
[
i
];
}
}
if
(
this
->
dof_manager
.
hasSynchronizer
())
{
this
->
dof_manager
.
getSynchronizer
().
reduceSynchronizeArray
<
AddOperation
>
(
y
);
}
AKANTU_DEBUG_OUT
();
}
/* -------------------------------------------------------------------------- */
void
SparseMatrixAIJ
::
matVecMul
(
const
SolverVector
&
_x
,
SolverVector
&
_y
,
Real
alpha
,
Real
beta
)
const
{
AKANTU_DEBUG_IN
();
auto
&&
x
=
aka
::
as_type
<
SolverVectorArray
>
(
_x
).
getVector
();
auto
&&
y
=
aka
::
as_type
<
SolverVectorArray
>
(
_y
).
getVector
();
this
->
matVecMul
(
x
,
y
,
alpha
,
beta
);
}
/* -------------------------------------------------------------------------- */
void
SparseMatrixAIJ
::
copyContent
(
const
SparseMatrix
&
matrix
)
{
AKANTU_DEBUG_IN
();
const
auto
&
mat
=
aka
::
as_type
<
SparseMatrixAIJ
>
(
matrix
);
AKANTU_DEBUG_ASSERT
(
nb_non_zero
==
mat
.
getNbNonZero
(),
"The to matrix don't have the same profiles"
);
memcpy
(
a
.
storage
(),
mat
.
getA
().
storage
(),
nb_non_zero
*
sizeof
(
Real
));
this
->
value_release
++
;
AKANTU_DEBUG_OUT
();
}
/* -------------------------------------------------------------------------- */
void
SparseMatrixAIJ
::
copyProfile
(
const
SparseMatrix
&
other
)
{
const
auto
&
A
=
aka
::
as_type
<
SparseMatrixAIJ
>
(
other
);
SparseMatrix
::
clearProfile
();
this
->
irn
.
copy
(
A
.
irn
);
this
->
jcn
.
copy
(
A
.
jcn
);
this
->
irn_jcn_k
.
clear
();
UInt
i
;
UInt
j
;
UInt
k
;
for
(
auto
&&
data
:
enumerate
(
irn
,
jcn
))
{
std
::
tie
(
k
,
i
,
j
)
=
data
;
this
->
irn_jcn_k
[
this
->
key
(
i
-
1
,
j
-
1
)]
=
k
;
}
this
->
nb_non_zero
=
this
->
irn
.
size
();
this
->
a
.
resize
(
this
->
nb_non_zero
);
this
->
a
.
set
(
0.
);
this
->
size_
=
A
.
size_
;
this
->
profile_release
=
A
.
profile_release
;
this
->
value_release
++
;
}
/* -------------------------------------------------------------------------- */
template
<
class
MatrixType
>
void
SparseMatrixAIJ
::
addMeToTemplated
(
MatrixType
&
B
,
Real
alpha
)
const
{
UInt
i
;
UInt
j
;
Real
A_ij
;
for
(
auto
&&
tuple
:
zip
(
irn
,
jcn
,
a
))
{
std
::
tie
(
i
,
j
,
A_ij
)
=
tuple
;
B
.
add
(
i
-
1
,
j
-
1
,
alpha
*
A_ij
);
}
}
/* -------------------------------------------------------------------------- */
void
SparseMatrixAIJ
::
addMeTo
(
SparseMatrix
&
B
,
Real
alpha
)
const
{
if
(
aka
::
is_of_type
<
SparseMatrixAIJ
>
(
B
))
{
this
->
addMeToTemplated
<
SparseMatrixAIJ
>
(
aka
::
as_type
<
SparseMatrixAIJ
>
(
B
),
alpha
);
}
else
{
// this->addMeToTemplated<SparseMatrix>(*this, alpha);
}
}
/* -------------------------------------------------------------------------- */
void
SparseMatrixAIJ
::
mul
(
Real
alpha
)
{
this
->
a
*=
alpha
;
this
->
value_release
++
;
}
/* -------------------------------------------------------------------------- */
void
SparseMatrixAIJ
::
set
(
Real
val
)
{
a
.
set
(
val
);
this
->
value_release
++
;
}
}
// namespace akantu
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