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ParSparseMatrix.cpp
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Sun, Sep 8, 01:11
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9 KB
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text/x-c
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Tue, Sep 10, 01:11 (1 d, 21 h)
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blob
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20621709
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rLAMMPS lammps
ParSparseMatrix.cpp
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#include "ParSparseMatrix.h"
#include <fstream>
#ifdef TIMING_ON
double
time_diff
(
timespec
&
start
,
timespec
&
end
)
{
return
(
double
)(
1e9
*
(
end
.
tv_sec
-
start
.
tv_sec
)
+
end
.
tv_nsec
-
start
.
tv_nsec
)
/
1e9
;
}
#endif
using
namespace
MPI_Wrappers
;
using
namespace
std
;
namespace
ATC_matrix
{
// All the same constructors as for SparseMatrix
ParSparseMatrix
<
double
>::
ParSparseMatrix
(
MPI_Comm
comm
,
INDEX
rows
,
INDEX
cols
)
:
SparseMatrix
<
double
>
(
rows
,
cols
),
_comm
(
comm
)
{}
ParSparseMatrix
<
double
>::
ParSparseMatrix
(
MPI_Comm
comm
,
const
SparseMatrix
<
double
>
&
c
)
:
SparseMatrix
<
double
>
(
c
),
_comm
(
comm
)
{}
ParSparseMatrix
<
double
>::
ParSparseMatrix
(
MPI_Comm
comm
,
INDEX
*
rows
,
INDEX
*
cols
,
double
*
vals
,
INDEX
size
,
INDEX
nRows
,
INDEX
nCols
,
INDEX
nRowsCRS
)
:
SparseMatrix
<
double
>
(
rows
,
cols
,
vals
,
size
,
nRows
,
nCols
,
nRowsCRS
),
_comm
(
comm
)
{}
//============================================================
void
ParSparseMatrix
<
double
>::
MultMv
(
const
Vector
<
double
>&
v
,
DenseVector
<
double
>&
c
)
const
{
int
numProcs
=
MPI_Wrappers
::
size
(
_comm
);
#ifdef DISABLE_PAR_HEURISTICS
// Use much more lenient heuristics to exercise parallel code
if
(
numProcs
==
1
||
_size
<
300
)
{
#else
// These are simple heuristics to perform multiplication in serial if
// parallel will be slower. They were determined experimentally.
if
(
numProcs
==
1
||
(
_size
<
50000
||
_size
>
10000000
)
||
((
_size
<
150000
||
_size
>
5000000
)
&&
numProcs
>
8
)
||
((
_size
<
500000
||
_size
>
2500000
)
&&
numProcs
>
16
)
||
(
numProcs
>
32
))
{
#endif
SparseMatrix
<
double
>::
MultMv
(
v
,
c
);
return
;
}
SparseMatrix
<
double
>::
compress
(
*
this
);
GCK
(
*
this
,
v
,
this
->
nCols
()
!=
v
.
size
(),
"ParSparseMatrix * Vector"
)
SparseMatrix
<
double
>
A_local
;
// Split the sparse matrix. partition() takes a ParSparMat, so we cast.
partition
(
*
static_cast
<
ParSparseMatrix
<
double
>*>
(
&
A_local
));
// actually do multiplication - end up with partial result vector
// on each processor
#ifdef TIMING_ON
timespec
before
,
after
;
// barrier(MPI_COMM_WORLD);
clock_gettime
(
CLOCK_MONOTONIC
,
&
before
);
#endif
DenseVector
<
double
>
c_local
=
A_local
*
v
;
#ifdef TIMING_ON
// barrier(MPI_COMM_WORLD);
clock_gettime
(
CLOCK_MONOTONIC
,
&
after
);
cout
<<
"P"
<<
MPI_Wrappers
::
rank
(
MPI_COMM_WORLD
)
<<
" "
<<
time_diff
(
before
,
after
)
<<
" mat.vec time
\n
"
;
//LammpsInterface::instance()->all_print((after-before),"mat.vec time");
barrier
(
MPI_COMM_WORLD
);
#endif
// destroy A_local intelligently
static_cast
<
ParSparseMatrix
<
double
>*>
(
&
A_local
)
->
finalize
();
// Add all the result vectors together on each processor.
#ifdef TIMING_ON
barrier
(
MPI_COMM_WORLD
);
//barrier(MPI_COMM_WORLD);
clock_gettime
(
CLOCK_MONOTONIC
,
&
before
);
#endif
allsum
(
_comm
,
c_local
.
ptr
(),
c
.
ptr
(),
c_local
.
size
());
#ifdef TIMING_ON
//barrier(MPI_COMM_WORLD);
clock_gettime
(
CLOCK_MONOTONIC
,
&
after
);
cout
<<
"P"
<<
MPI_Wrappers
::
rank
(
MPI_COMM_WORLD
)
<<
" "
<<
time_diff
(
before
,
after
)
<<
" allsum time
\n
"
;
//LammpsInterface::instance()->print_msg_once((after-before),"allsum time");
#endif
}
DenseVector
<
double
>
ParSparseMatrix
<
double
>::
transMat
(
const
Vector
<
double
>&
v
)
const
{
SparseMatrix
<
double
>::
compress
(
*
this
);
GCK
(
*
this
,
v
,
this
->
nRows
()
!=
v
.
size
(),
"ParSparseMatrix transpose * Vector"
)
DenseVector
<
double
>
c
(
nCols
(),
true
);
SparseMatrix
<
double
>
A_local
;
partition
(
*
static_cast
<
ParSparseMatrix
<
double
>*>
(
&
A_local
));
// actually do multiplication - end up with partial result vector
// on each processor
DenseVector
<
double
>
c_local
=
A_local
.
transMat
(
v
);
static_cast
<
ParSparseMatrix
<
double
>*>
(
&
A_local
)
->
finalize
();
// Add all the result vectors together on each processor.
allsum
(
_comm
,
c_local
.
ptr
(),
c
.
ptr
(),
c_local
.
size
());
return
c
;
}
void
ParSparseMatrix
<
double
>::
MultAB
(
const
Matrix
<
double
>&
B
,
DenseMatrix
<
double
>&
C
)
const
{
SparseMatrix
<
double
>::
compress
(
*
this
);
GCK
(
*
this
,
B
,
this
->
nCols
()
!=
B
.
nRows
(),
"ParSparseMatrix * Matrix"
)
SparseMatrix
<
double
>
A_local
;
partition
(
*
static_cast
<
ParSparseMatrix
<
double
>*>
(
&
A_local
));
// actually do multiplication - end up with partial result matrix
// on each processor
#ifdef TIMING_ON
timespec
before
,
after
;
barrier
(
MPI_COMM_WORLD
);
clock_gettime
(
CLOCK_MONOTONIC
,
&
before
);
#endif
DenseMatrix
<
double
>
C_local
=
A_local
*
B
;
#ifdef TIMING_ON
barrier
(
MPI_COMM_WORLD
);
clock_gettime
(
CLOCK_MONOTONIC
,
&
after
);
cout
<<
"P"
<<
MPI_Wrappers
::
rank
(
MPI_COMM_WORLD
)
<<
" "
<<
time_diff
(
after
,
before
)
<<
" mat.vec time
\n
"
;
//LammpsInterface::instance()->all_print((after-before),"mat.vec time");
#endif
static_cast
<
ParSparseMatrix
<
double
>*>
(
&
A_local
)
->
finalize
();
// Add all the result vectors together on each processor.
#ifdef TIMING_ON
barrier
(
MPI_COMM_WORLD
);
clock_gettime
(
CLOCK_MONOTONIC
,
&
before
);
#endif
allsum
(
_comm
,
C_local
.
ptr
(),
C
.
ptr
(),
C_local
.
size
());
#ifdef TIMING_ON
barrier
(
MPI_COMM_WORLD
);
clock_gettime
(
CLOCK_MONOTONIC
,
&
after
);
cout
<<
"P"
<<
MPI_Wrappers
::
rank
(
MPI_COMM_WORLD
)
<<
" "
<<
time_diff
(
after
,
before
)
<<
" allsum time
\n
"
;
//LammpsInterface::instance()->print_msg_once((after-before),"allsum time");
#endif
}
DenseMatrix
<
double
>
ParSparseMatrix
<
double
>::
transMat
(
const
DenseMatrix
<
double
>&
B
)
const
{
SparseMatrix
<
double
>::
compress
(
*
this
);
GCK
(
*
this
,
B
,
this
->
nRows
()
!=
B
.
nRows
(),
"ParSparseMatrix transpose * Matrix"
)
DenseMatrix
<
double
>
C
(
nCols
(),
B
.
nCols
(),
true
);
SparseMatrix
<
double
>
A_local
;
partition
(
*
static_cast
<
ParSparseMatrix
<
double
>*>
(
&
A_local
));
// actually do multiplication - end up with partial result matrix
// on each processor
DenseMatrix
<
double
>
C_local
=
A_local
.
transMat
(
B
);
static_cast
<
ParSparseMatrix
<
double
>*>
(
&
A_local
)
->
finalize
();
// Add all the result vectors together on each processor.
allsum
(
_comm
,
C_local
.
ptr
(),
C
.
ptr
(),
C_local
.
size
());
return
C
;
}
/*
The two commented-out functions both need to return SparseMatrices. It's hard
to combine sparse matrices between processors, so this has not yet been completed.
void ParMultAB(const SparseMatrix<double> &B, SparseMatrix<double> &C) const
{
//SparseMatrix<T>::compress(*this);
GCK(*this, B, this->nCols()!=B.nRows(), "ParSparseMatrix * SparseMatrix")
ParSparseMatrix<double> A_local(this->_comm);
this->partition(A_local);
// actually do multiplication - end up with partial result matrix
// on each processor
SparseMatrix<double> C_local = ((SparseMatrix<double>)A_local) * B;
// destroy newA intelligently
static_cast<ParSparseMatrix<double>*>(&A_local)->finalize();
// Add all the result vectors together on each processor.
sumSparse(C_local, C);
}*/
DenseMatrix
<
double
>
ParSparseMatrix
<
double
>::
transMat
(
const
SparseMatrix
<
double
>&
B
)
const
{
SparseMatrix
<
double
>::
compress
(
*
this
);
GCK
(
*
this
,
B
,
this
->
nRows
()
!=
B
.
nRows
(),
"ParSparseMatrix transpose * SparseMatrix"
)
DenseMatrix
<
double
>
C
(
nCols
(),
B
.
nCols
(),
true
);
SparseMatrix
<
double
>
A_local
;
partition
(
*
static_cast
<
ParSparseMatrix
<
double
>*>
(
&
A_local
));
// actually do multiplication - end up with partial result matrix
// on each processor
DenseMatrix
<
double
>
C_local
=
A_local
.
transMat
(
B
);
static_cast
<
ParSparseMatrix
<
double
>*>
(
&
A_local
)
->
finalize
();
// Add all the result vectors together on each processor.
allsum
(
_comm
,
C_local
.
ptr
(),
C
.
ptr
(),
C_local
.
size
());
return
C
;
}
/*void ParMultAB(const DiagonalMatrix<double> &B, SparseMatrix<double> &C) const
{
//SparseMatrix<T>::compress(*this);
GCK(*this, B, this->nCols()!=B.nRows(), "ParSparseMatrix * DiagonalMatrix")
ParSparseMatrix<double> A_local(this->_comm);
this->partition(A_local);
// actually do multiplication - end up with partial result matrix
// on each processor
SparseMatrix<double> C_local = ((SparseMatrix<double>)A_local) * B;
// destroy newA intelligently
A_local._val = NULL;
A_local._ja = NULL;
// Add all the result vectors together on each processor.
sumSparse(C_local, C);
}*/
void
ParSparseMatrix
<
double
>::
partition
(
ParSparseMatrix
<
double
>&
A_local
)
const
{
// create new sparse matrix on each processor, with same size and
// a disjoint subset of A's elements.
//
// Ex: on two processors,
//
// |0 1 0| |0 1 0| |0 0 0|
// |2 6 0| splits into |2 0 0| on proc 1 and |0 6 0| on proc 2
// |0 0 3| |0 0 0| |0 0 3|
//
// We compute the subproducts individually on each processor, then
// sum up all the vectors to get our final result.
//
// decide which elements will be in each submatrix
INDEX
startIndex
=
(
MPI_Wrappers
::
rank
(
_comm
)
*
size
())
/
MPI_Wrappers
::
size
(
_comm
);
INDEX
endIndex
=
((
MPI_Wrappers
::
rank
(
_comm
)
+
1
)
*
size
())
/
MPI_Wrappers
::
size
(
_comm
);
// update number of elements
A_local
.
_nRows
=
_nRows
;
A_local
.
_nCols
=
_nCols
;
A_local
.
_size
=
endIndex
-
startIndex
;
A_local
.
_nRowsCRS
=
_nRowsCRS
;
// use pointer arithmetic to:
// set newA's _val (to inside A's _val)
A_local
.
_val
=
_val
+
startIndex
;
// set newA's _ja (to inside A's _ja)
A_local
.
_ja
=
_ja
+
startIndex
;
// set newA's _ia (from scratch)
A_local
.
_ia
=
new
INDEX
[
nRowsCRS
()
+
1
];
INDEX
numRows
=
nRowsCRS
();
if
(
A_local
.
_size
>
0
)
{
for
(
INDEX
i
=
0
;
i
<
numRows
+
1
;
i
++
)
{
A_local
.
_ia
[
i
]
=
std
::
min
(
std
::
max
((
_ia
[
i
]
-
startIndex
),
0
),
endIndex
-
startIndex
);
}
}
else
{
A_local
.
_nRowsCRS
=
0
;
}
}
// Prepare an A_local matrix for deletion after it has been loaded with
// data members from another matrix.
void
ParSparseMatrix
<
double
>::
finalize
()
{
_val
=
NULL
;
_ja
=
NULL
;
}
void
ParSparseMatrix
<
double
>::
operator
=
(
const
SparseMatrix
<
double
>
&
source
)
{
copy
(
source
);
}
/*void sumSparse(SparseMatrix<double> &C_local, SparseMatrix<double> &C)
{
}*/
}
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