Hi,
Mikael Mortensen has written a function compress_sparse for removing all
the non-zeros in a Matrix. There can be many! For instance for
a vector mass or stiffness matrix in 3D, 2/3 or more of the matrix
entries are zeros. Naturally, in such applications, we get a big speed
up.
Mikael has put this function in GenericMatrix. Is this the right place?
Should a new matrix be made instead ?
The only side effect I can think of is that the sparsity pattern is
changed. Hence, the matrix may no longer be added to another
matrix created using the same function space.
Kent
// Copyright (C) 2006-2009 Garth N. Wells.
// Licensed under the GNU LGPL Version 2.1.
//
// Modified by Johan Jansson, 2006.
// Modified by Anders Logg, 2006-2008.
// Modified by Ola Skavhaug, 2007-2008.
// Modified by Kent-Andre Mardal, 2008.
// Modified by Martin Alnæs, 2008.
//
// First added: 2006-04-24
// Last changed: 2009-09-08
#ifndef __GENERIC_MATRIX_H
#define __GENERIC_MATRIX_H
#include <tr1/tuple>
#include <vector>
#include "GenericTensor.h"
//
#include "SparsityPattern.h"
#include "GenericSparsityPattern.h"
#include <dolfin/fem/SparsityPatternBuilder.h>
#include <dolfin/common/Timer.h>
namespace dolfin
{
class GenericVector;
class XMLMatrix;
/// This class defines a common interface for matrices.
class GenericMatrix : public GenericTensor
{
public:
/// Destructor
virtual ~GenericMatrix() {}
//--- Implementation of the GenericTensor interface ---
/// Resize tensor with given dimensions
virtual void resize(uint rank, const uint* dims)
{ assert(rank == 2); resize(dims[0], dims[1]); }
/// Initialize zero tensor using sparsity pattern
virtual void init(const GenericSparsityPattern& sparsity_pattern) = 0;
/// Return copy of tensor
virtual GenericMatrix* copy() const = 0;
/// Return tensor rank (number of dimensions)
virtual uint rank() const
{ return 2; }
/// Return size of given dimension
virtual uint size(uint dim) const = 0;
/// Get block of values
virtual void get(double* block, const uint* num_rows,
const uint * const * rows) const
{ get(block, num_rows[0], rows[0], num_rows[1], rows[1]); }
/// Set block of values
virtual void set(const double* block, const uint* num_rows,
const uint * const * rows)
{ set(block, num_rows[0], rows[0], num_rows[1], rows[1]); }
/// Add block of values
virtual void add(const double* block, const uint* num_rows,
const uint * const * rows)
{ add(block, num_rows[0], rows[0], num_rows[1], rows[1]); }
/// Set all entries to zero and keep any sparse structure
virtual void zero() = 0;
/// Finalize assembly of tensor
virtual void apply() = 0;
/// Return informal string representation (pretty-print)
virtual std::string str(bool verbose) const = 0;
//--- Matrix interface ---
/// Resize matrix to M x N
virtual void resize(uint M, uint N) = 0;
/// Get block of values
virtual void get(double* block, uint m, const uint* rows, uint n,
const uint* cols) const = 0;
/// Set block of values
virtual void set(const double* block, uint m, const uint* rows, uint n,
const uint* cols) = 0;
/// Add block of values
virtual void add(const double* block, uint m, const uint* rows, uint n,
const uint* cols) = 0;
/// Add multiple of given matrix (AXPY operation)
virtual void axpy(double a, const GenericMatrix& A,
bool same_nonzero_pattern) = 0;
/// Return norm of matrix
virtual double norm(std::string norm_type) const = 0;
/// Get non-zero values of given row
virtual void getrow(uint row, std::vector<uint>& columns,
std::vector<double>& values) const = 0;
/// Set values for given row
virtual void setrow(uint row, const std::vector<uint>& columns,
const std::vector<double>& values) = 0;
/// Set given rows to zero
virtual void zero(uint m, const uint* rows) = 0;
/// Set given rows to identity matrix
virtual void ident(uint m, const uint* rows) = 0;
/// Matrix-vector product, y = Ax
virtual void mult(const GenericVector& x, GenericVector& y) const = 0;
/// Matrix-vector product, y = A^T x
virtual void transpmult(const GenericVector& x, GenericVector& y) const = 0;
/// Multiply matrix by given number
virtual const GenericMatrix& operator*= (double a) = 0;
/// Divide matrix by given number
virtual const GenericMatrix& operator/= (double a) = 0;
/// Add given matrix
const GenericMatrix& operator+= (const GenericMatrix& A)
{
axpy(1.0, A, false);
return *this;
}
/// Subtract given matrix
const GenericMatrix& operator-= (const GenericMatrix& A)
{
axpy(-1.0, A, false);
return *this;
}
/// Assignment operator
virtual const GenericMatrix& operator= (const GenericMatrix& x) = 0;
/// Return pointers to underlying compresssed row/column storage data
/// For compressed row storage, data = (row_pointer[#rows +1],
/// column_index[#nz], matrix_values[#nz], nz)
virtual std::tr1::tuple<const std::size_t*, const std::size_t*,
const double*, int> data() const
{
error("Unable to return pointers to underlying matrix data.");
return std::tr1::tuple<const std::size_t*, const std::size_t*,
const double*, int>(0, 0, 0, 0);
}
//--- Convenience functions ---
/// Get value of given entry
virtual double operator() (uint i, uint j) const
{ double value(0); get(&value, 1, &i, 1, &j); return value; }
/// Get value of given entry
virtual double getitem(std::pair<uint, uint> ij) const
{ double value(0); get(&value, 1, &ij.first, 1, &ij.second); return value; }
/// Set given entry to value
virtual void setitem(std::pair<uint, uint> ij, double value)
{ set(&value, 1, &ij.first, 1, &ij.second); }
typedef XMLMatrix XMLHandler;
void compress_sparse()
{
GenericMatrix* B;
Timer timer("Compress matrix ");
// Make a copy of this because (*this) will be zeroed when recreated with a new sparsity pattern
B = (*this).copy();
// New sparsity pattern
GenericSparsityPattern* new_sparsity_pattern = new SparsityPattern(SparsityPattern::unsorted);
uint* global_dimensions = new uint[2];
global_dimensions[0] = this->size(0);
global_dimensions[1] = this->size(1);
const uint N = global_dimensions[0];
new_sparsity_pattern->init(2, global_dimensions);
std::vector<uint> columns;
std::vector<double> values;
std::vector<uint> newcounts(N);
uint* num_rows = new uint[2];
uint count = 0;
num_rows[0] = 1;
uint** dofs = new uint*[2];
dofs[0] = new uint[1];
dofs[1] = new uint[N];
for (uint i = 0; i < N; i++)
{
// Get row and locate nonzeros
this->getrow(i, columns, values);
count = 0;
for (uint j = 0; j < columns.size(); j++)
{
if(values[j]*values[j] > 1.e-32)
{
dofs[1][count]=columns[j];
count++;
}
}
newcounts[i]=count;
dofs[0][0] = i;
num_rows[1] = count;
// Build new improved sparsity pattern
new_sparsity_pattern->insert(num_rows,dofs);
}
delete [] dofs[0];
delete [] dofs[1];
delete [] dofs;
// Finalize sparsity pattern
new_sparsity_pattern->apply();
// Recreate with the new sparsity pattern. This is zeroed in the process.
this->init(*new_sparsity_pattern);
delete new_sparsity_pattern;
// Put the old values back
uint* cols = new uint[N];
double* vals = new double[N];
num_rows[0]=1;
for (uint i = 0; i < N; i++)
{
B->getrow(i, columns, values);
count=0;
for (uint j = 0; j < columns.size(); j++)
{
if(values[j]*values[j] > 1.e-32)
{
cols[count]=columns[j];
vals[count]=values[j];
count++;
}
}
this->set(vals, 1, &i, count, cols);
}
delete [] cols;
delete [] vals;
delete [] num_rows;
this->apply();
}
};
}
#endif
_______________________________________________
Mailing list: https://launchpad.net/~dolfin
Post to : dolfin@lists.launchpad.net
Unsubscribe : https://launchpad.net/~dolfin
More help : https://help.launchpad.net/ListHelp
