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bugfix in Map by Keir Mierle

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This commit is contained in:
Gael Guennebaud 2009-01-18 09:53:06 +00:00
parent 22792c696f
commit 0c7974dd4d
3 changed files with 117 additions and 15 deletions
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2
Eigen/src/Core/Map.h
View File

@ -85,7 +85,7 @@ template<typename MatrixType, int PacketAccess> class Map
EIGEN_ONLY_USED_FOR_DEBUG(rows);
EIGEN_ONLY_USED_FOR_DEBUG(cols);
ei_assert(rows == this->rows());
ei_assert(rows == this->cols());
ei_assert(cols == this->cols());
}
inline void resize(int size)

73
Eigen/src/Sparse/RandomSetter.h
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@ -25,6 +25,10 @@
#ifndef EIGEN_RANDOMSETTER_H
#define EIGEN_RANDOMSETTER_H
/** Represents a std::map
*
* \see RandomSetter
*/
template<typename Scalar> struct StdMapTraits
{
typedef int KeyType;
@ -37,6 +41,10 @@ template<typename Scalar> struct StdMapTraits
};
#ifdef _HASH_MAP
/** Represents a __gnu_cxx::hash_map
*
* \see RandomSetter
*/
template<typename Scalar> struct GnuHashMapTraits
{
typedef int KeyType;
@ -50,6 +58,10 @@ template<typename Scalar> struct GnuHashMapTraits
#endif
#ifdef _DENSE_HASH_MAP_H_
/** Represents a google::dense_hash_map
*
* \see RandomSetter
*/
template<typename Scalar> struct GoogleDenseHashMapTraits
{
typedef int KeyType;
@ -64,6 +76,10 @@ template<typename Scalar> struct GoogleDenseHashMapTraits
#endif
#ifdef _SPARSE_HASH_MAP_H_
/** Represents a google::sparse_hash_map
*
* \see RandomSetter
*/
template<typename Scalar> struct GoogleSparseHashMapTraits
{
typedef int KeyType;
@ -78,7 +94,19 @@ template<typename Scalar> struct GoogleSparseHashMapTraits
/** \class RandomSetter
*
* Typical usage:
* \brief The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access
*
* \param SparseMatrixType the type of the sparse matrix we are updating
* \param MapTraits a traits class representing the map implementation used for the temporary sparse storage.
* Its default value depends on the system.
* \param OuterPacketBits defines the number of rows (or columns) manage by a single map object
* as a power of two exponent.
*
* This class temporarily represents a sparse matrix object using a generic map implementation allowing for
* efficient random access. The conversion from the compressed representation to a hash_map object is performed
* in the RandomSetter constructor, while the sparse matrix is updated back at destruction time. This strategy
* suggest the use of nested blocks as in this example:
*
* \code
* SparseMatrix<double> m(rows,cols);
* {
@ -91,11 +119,28 @@ template<typename Scalar> struct GoogleSparseHashMapTraits
* // and m is ready to use.
* \endcode
*
* \note for performance and memory consumption reasons it is highly recommended to use
* Google's hash library. To do so you have two options:
* - include <google/dense_hash_map> yourself \b before Eigen/Sparse header
* Since hash_map objects are not fully sorted, representing a full matrix as a single hash_map would
* involve a big and costly sort to update the compressed matrix back. To overcome this issue, a RandomSetter
* use multiple hash_map, each representing 2^OuterPacketBits columns or rows according to the storage order.
* To reach optimal performance, this value should be adjusted according to the average number of nonzeros
* per rows/columns.
*
* The possible values for the template parameter MapTraits are:
* - \b StdMapTraits: corresponds to std::map. (does not perform very well)
* - \b GnuHashMapTraits: corresponds to __gnu_cxx::hash_map (available only with GCC)
* - \b GoogleDenseHashMapTraits: corresponds to google::dense_hash_map (best efficiency, reasonable memory consumption)
* - \b GoogleSparseHashMapTraits: corresponds to google::sparse_hash_map (best memory consumption, relatively good performance)
*
* The default map implementation depends on the availability, and the preferred order is:
* GoogleSparseHashMapTraits, GnuHashMapTraits, and finally StdMapTraits.
*
* For performance and memory consumption reasons it is highly recommended to use one of
* the Google's hash_map implementation. To enable the support for them, you have two options:
* - \#include <google/dense_hash_map> yourself \b before Eigen/Sparse header
* - define EIGEN_GOOGLEHASH_SUPPORT
* In the later case the inclusion of <google/dense_hash_map> is made for you.
*
* \see http://code.google.com/p/google-sparsehash/
*/
template<typename SparseMatrixType,
template <typename T> class MapTraits =
@ -121,11 +166,19 @@ class RandomSetter
enum {
SwapStorage = 1 - MapTraits<ScalarWrapper>::IsSorted,
TargetRowMajor = (SparseMatrixType::Flags & RowMajorBit) ? 1 : 0,
SetterRowMajor = SwapStorage ? 1-TargetRowMajor : TargetRowMajor
SetterRowMajor = SwapStorage ? 1-TargetRowMajor : TargetRowMajor,
IsUpperTriangular = SparseMatrixType::Flags & UpperTriangularBit,
IsLowerTriangular = SparseMatrixType::Flags & LowerTriangularBit
};
public:
/** Constructs a random setter object from the sparse matrix \a target
*
* Note that the initial value of \a target are imported. If you want to re-set
* a sparse matrix from scratch, then you must set it to zero first using the
* setZero() function.
*/
inline RandomSetter(SparseMatrixType& target)
: mp_target(&target)
{
@ -153,6 +206,7 @@ class RandomSetter
(*this)(TargetRowMajor?j:it.index(), TargetRowMajor?it.index():j) = it.value();
}
/** Destructor updating back the sparse matrix target */
~RandomSetter()
{
KeyType keyBitsMask = (1<<m_keyBitsOffset)-1;
@ -226,8 +280,11 @@ class RandomSetter
delete[] m_hashmaps;
}
/** \returns a reference to the coefficient at given coordinates \a row, \a col */
Scalar& operator() (int row, int col)
{
ei_assert(((!IsUpperTriangular) || (row<=col)) && "Invalid access to an upper triangular matrix");
ei_assert(((!IsLowerTriangular) || (col<=row)) && "Invalid access to an upper triangular matrix");
const int outer = SetterRowMajor ? row : col;
const int inner = SetterRowMajor ? col : row;
const int outerMajor = outer >> OuterPacketBits; // index of the packet/map
@ -236,7 +293,11 @@ class RandomSetter
return m_hashmaps[outerMajor][key].value;
}
// might be slow
/** \returns the number of non zero coefficients
*
* \note According to the underlying map/hash_map implementation,
* this function might be quite expensive.
*/
int nonZeros() const
{
int nz = 0;

57
bench/sparse_setter.cpp
View File

@ -4,7 +4,7 @@
// -DNOGMM -DNOMTL -DCSPARSE
// -I /home/gael/Coding/LinearAlgebra/CSparse/Include/ /home/gael/Coding/LinearAlgebra/CSparse/Lib/libcsparse.a
#ifndef SIZE
#define SIZE 1000000
#define SIZE 100000
#endif
#ifndef NBPERROW
@ -22,6 +22,8 @@
#include "BenchSparseUtil.h"
#define CHECK_MEM
// #define CHECK_MEM std/**/::cout << "check mem\n"; getchar();
#define BENCH(X) \
timer.reset(); \
@ -34,6 +36,7 @@
typedef std::vector<Vector2i> Coordinates;
typedef std::vector<float> Values;
EIGEN_DONT_INLINE Scalar* setinnerrand_eigen(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_eigen_gnu_hash(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_eigen_google_dense(const Coordinates& coords, const Values& vals);
EIGEN_DONT_INLINE Scalar* setrand_eigen_google_sparse(const Coordinates& coords, const Values& vals);
@ -47,17 +50,31 @@ int main(int argc, char *argv[])
{
int rows = SIZE;
int cols = SIZE;
bool fullyrand = false;
//float density = float(NBPERROW)/float(SIZE);
BenchTimer timer;
Coordinates coords;
Values values;
for (int i=0; i<cols*NBPERROW; ++i)
if(fullyrand)
{
coords.push_back(Vector2i(ei_random<int>(0,rows-1),ei_random<int>(0,cols-1)));
values.push_back(ei_random<Scalar>());
for (int i=0; i<cols*NBPERROW; ++i)
{
coords.push_back(Vector2i(ei_random<int>(0,rows-1),ei_random<int>(0,cols-1)));
values.push_back(ei_random<Scalar>());
}
}
else
{
for (int j=0; j<cols; ++j)
for (int i=0; i<NBPERROW; ++i)
{
coords.push_back(Vector2i(ei_random<int>(0,rows-1),j));
values.push_back(ei_random<Scalar>());
}
}
std::cout << "nnz = " << coords.size() << "\n";
CHECK_MEM
// dense matrices
#ifdef DENSEMATRIX
@ -72,6 +89,15 @@ int main(int argc, char *argv[])
#endif
// eigen sparse matrices
if (!fullyrand)
{
timer.reset();
timer.start();
for (int k=0; k<REPEAT; ++k)
setinnerrand_eigen(coords,values);
timer.stop();
std::cout << "Eigen fillrand\t" << timer.value() << "\n";
}
{
timer.reset();
timer.start();
@ -150,6 +176,20 @@ int main(int argc, char *argv[])
return 0;
}
EIGEN_DONT_INLINE Scalar* setinnerrand_eigen(const Coordinates& coords, const Values& vals)
{
using namespace Eigen;
SparseMatrix<Scalar> mat(SIZE,SIZE);
mat.startFill(2000000/*coords.size()*/);
for (int i=0; i<coords.size(); ++i)
{
mat.fillrand(coords[i].x(), coords[i].y()) = vals[i];
}
mat.endFill();
CHECK_MEM;
return 0;
}
EIGEN_DONT_INLINE Scalar* setrand_eigen_gnu_hash(const Coordinates& coords, const Values& vals)
{
using namespace Eigen;
@ -160,7 +200,7 @@ EIGEN_DONT_INLINE Scalar* setrand_eigen_gnu_hash(const Coordinates& coords, cons
{
setter(coords[i].x(), coords[i].y()) = vals[i];
}
// std::cout << "check mem\n"; getchar();
CHECK_MEM;
}
return 0;//&mat.coeffRef(coords[0].x(), coords[0].y());
}
@ -174,7 +214,7 @@ EIGEN_DONT_INLINE Scalar* setrand_eigen_google_dense(const Coordinates& coords,
RandomSetter<SparseMatrix<Scalar>, GoogleDenseHashMapTraits> setter(mat);
for (int i=0; i<coords.size(); ++i)
setter(coords[i].x(), coords[i].y()) = vals[i];
// std::cout << "check mem\n"; getchar();
CHECK_MEM;
}
return 0;//&mat.coeffRef(coords[0].x(), coords[0].y());
}
@ -187,7 +227,7 @@ EIGEN_DONT_INLINE Scalar* setrand_eigen_google_sparse(const Coordinates& coords,
RandomSetter<SparseMatrix<Scalar>, GoogleSparseHashMapTraits> setter(mat);
for (int i=0; i<coords.size(); ++i)
setter(coords[i].x(), coords[i].y()) = vals[i];
// std::cout << "check mem\n"; getchar();
CHECK_MEM;
}
return 0;//&mat.coeffRef(coords[0].x(), coords[0].y());
}
@ -204,7 +244,7 @@ EIGEN_DONT_INLINE Scalar* setrand_ublas_mapped(const Coordinates& coords, const
{
aux(coords[i].x(), coords[i].y()) = vals[i];
}
// std::cout << "check mem\n"; getchar();
CHECK_MEM;
compressed_matrix<Scalar> mat(aux);
return 0;// &mat(coords[0].x(), coords[0].y());
}
@ -245,6 +285,7 @@ EIGEN_DONT_INLINE Scalar* setrand_ublas_genvec(const Coordinates& coords, const
{
aux(coords[i].x(), coords[i].y()) = vals[i];
}
CHECK_MEM;
compressed_matrix<Scalar,row_major> mat(aux);
return 0;//&mat(coords[0].x(), coords[0].y());
}