Declaring an object variable makes all the previous instructions slower - c++

Question

I ran into a fastidious problem and I have no idea of what is causing it. I hope you can help me find a solution.
Framework: I implemented a sparse_matrix class using the CSR representation and I used this object as the basis for a recommendation system. The class is defined as follows:
class sparse_matrix_csr
{
public:
sparse_matrix_csr();
sparse_matrix_csr(const std::vector<int> &row_indices);
sparse_matrix_csr(const std::vector<int> &row_indices, const size_t nb_columns);
// other member functions omitted
private:
std::vector<int> _row_indices;
std::vector<int> _row_start_indices;
std::vector<int> _column_indices;
std::vector<double> _values;
bool _column_sorted_by_index;
};
The _row_indices vector contains the indices of the rows of the matrix. _row_start_indices contains the index of the first element of a given row in the _column_indices, which contains the column indices, and _values vector, which contains the elements of the matrix. In particular, the constructor sparse_matrix_csr(const std::vector<int> &row_indices, const size_t nb_columns) is implemented as follows:
sparse_matrix_csr::sparse_matrix_csr(const std::vector<int> &row_indices,
const size_t nb_columns):
_row_indices(row_indices),
_row_start_indices(row_indices.size()),
_column_indices(row_indices.size() * nb_columns, 0),
_values(row_indices.size() * nb_columns, 0.0),
_column_sorted_by_index(true)
{
for (size_t i = 0; i < _row_start_indices.size(); ++i)
_row_start_indices[i] = i * nb_columns;
}
This constructor takes in the indices of the rows of the sparse matrix and the number of elements that will be contained in each row. In fact, in the application I am considering, I have same matrices that are sparse only wrt the rows.
Problem: The algorithm is structured as follows
\\ Instruction block 1
{
\\ Do something
}
sparse_matrix_csr mat(list_indices, nb_columns);
\\ Instruction block 2
{
\\ Do something
}
If I run the first block of instructions alone (commenting out all that follows), my algorithm runs smoothly. However, if I uncomment the second part of the algorithm, the first part of the algorithm slows down a lot. I have been able to identify the critical line for this slow-down in the declaration of mat. However, I cannot explain this retro-action on the first part of the algorithm. The full algorithm is reported at the end of my question.
My considerations: I have never observed such a retro-action and therefore I am a little confused. One possibility that I have considered is that I have a problem in memory management which causes the slow-down of the previous part of the algorithm. I am currently working on some matrices that have around 2e6 elements, i.e. around 4e6 values stored in 2 vectors. I read on another post on stack overflow that the data contained in a std::vector are allocated in the heap. Is that always true? even if I initialize a vector with a given size as I do in the sparse matrix constructors above?
If you need some clarification, do not hesitate!
Thanks in advance,
Pierpaolo
Full algorithm:
void collaborative_filtering_mpi (std::string ratings_file, std::string targets_file,
std::string output_file, int k_neighbors, double shrinkage_factor,
bool output_debug_data)
{
std::cout << "COLLABORATIVE FILTERING ALGORITHM - k = " << k_neighbors
<< " , d = " << shrinkage_factor << std::endl;
stopwatch sw_total;
sw_total.start();
//-------------------------------------
// STEP 1: Read data from input files
//-------------------------------------
std::cout << "1) Read input files: ";
stopwatch sw;
sw.start();
//-------------------------------
// 1.1) Read user rating matrix
//-------------------------------
// Initialization
sparse_matrix_csr user_item_rating_matrix(ratings_file, true, true, true); // file is sorted, skip header
//-------------------------------------------------
// 1.2) Read targets (user, item) to be predicted
//-------------------------------------------------
// Initialization
sparse_matrix_csr targets(targets_file, true, false, false); // do not read ratings column
sw.stop();
double time_input = sw.get_duration();
std::cout << time_input << std::endl;
//-----------------------------------------
// STEP 2: Pre-computations
//-----------------------------------------
std::cout << "2) Pre-computations: ";
sw.start();
//-----------------------------------------------------------------------
// 2.1) Sort user_item_rating_matrixand compute relevant sizes of the problem
//-----------------------------------------------------------------------
// Sort: if file is sorted, sort should do nothing.
user_item_rating_matrix.sort_columns_by_index();
// Compute list of users and list of items
std::vector<int> list_users;
user_item_rating_matrix.rows(list_users);
std::vector<int> list_items;
user_item_rating_matrix.columns(list_items);
// [DEBUG]: print user_rating_matrix
if (output_debug_data)
{
std::ofstream ofs_debug("data_debug/debug_user_rating_matrix.txt");
ofs_debug << user_item_rating_matrix;
ofs_debug.close();
}
//-------------------------------------------------------------
// 2.2) Compute inverse user rating matrix (indexed by column)
//-------------------------------------------------------------
// Initialize item_user_rating_matrix: it is a sparse matrix with
// items on the rows, users on the columns and rating as values.
// This variable will be helpful when computing similarities.
sparse_matrix_csr item_user_rating_matrix;
// Compute item_user_rating<_matrix by transposing the user_rating_matrix
transpose (user_item_rating_matrix, item_user_rating_matrix);
// [DEBUG]: print item_user_matrix_on_file
if (output_debug_data)
{
std::ofstream ofs_debug("data_debug/debug_item_user_matrix.txt");
ofs_debug << item_user_rating_matrix;
ofs_debug.close();
}
//-----------------------------------------------
// 2.3) sort targets and compute relevant sizes
//-----------------------------------------------
// Compute list of target items
std::vector<int> list_target_items;
targets.columns(list_target_items);
// [DEBUG]: print targets
if (output_debug_data)
{
std::ofstream ofs_debug("data_debug/debug_targets.txt");
ofs_debug << targets;
ofs_debug.close();
}
//--------------------------------------------------------------
// 2.4) Compute difference between list_items and list_targets
//--------------------------------------------------------------
std::vector<int> list_non_target_items;
compute_difference_vector (list_items, list_target_items, list_non_target_items);
// [DEBUG]
if (output_debug_data)
{
std::ofstream ofs_debug("data_debug/debug_difference_vector.txt");
ofs_debug << "list_items - size: " << list_items.size() << std::endl;
for (std::vector<int>::const_iterator iter = list_items.begin(); iter != list_items.end(); ++iter)
ofs_debug << (*iter) << ",";
ofs_debug << std::endl << std::endl;
ofs_debug << "list_target_items - size: " << list_target_items.size() << std::endl;
for (std::vector<int>::const_iterator iter = list_target_items.begin(); iter != list_target_items.end(); ++iter)
ofs_debug << (*iter) << ",";
ofs_debug << std::endl << std::endl;
ofs_debug << "list_non_target_items - size: " << list_non_target_items.size() << std::endl;
for (std::vector<int>::const_iterator iter = list_non_target_items.begin(); iter != list_non_target_items.end(); ++iter)
ofs_debug << (*iter) << ",";
ofs_debug << std::endl << std::endl;
ofs_debug.close();
}
//--------------------------------------------
// 2.5) Compute average rating for each user
//--------------------------------------------
dictionary<int, double> average_rating_vector;
compute_average_rating(user_item_rating_matrix, average_rating_vector);
if (output_debug_data)
{
std::ofstream ofs_debug("data_debug/debug_average_rating_vector.txt");
for (dictionary<int, double>::const_iterator iter = average_rating_vector.begin();
iter != average_rating_vector.end(); ++iter)
ofs_debug << (*iter).get_key() << ": " << (*iter).get_value() << std::endl;
ofs_debug.close();
}
sw.stop();
std::cout << sw.get_duration() << std::endl;
//-------------------------------------
// STEP 3: Similarity matrix
//-------------------------------------
std::cout << "3) Compute similarity matrix: ";
sw.start();
// Initialize similarity_matrix with target items on the rows.
sparse_matrix_csr similarity_matrix(list_target_items,
list_target_items.size() + list_non_target_items.size());
// compute similarity matrix
compute_similarity_matrix_mpi(similarity_matrix,
item_user_rating_matrix,
average_rating_vector,
list_target_items,
list_non_target_items,
shrinkage_factor);
// [DEBUG]: print similarity matrix sorted by similarity
if (output_debug_data)
{
std::ofstream ofs("data_debug/debug_similarity_matrix.txt");
ofs << similarity_matrix;
ofs.close();
}
sw.stop();
std::cout << sw.get_duration() << std::endl;
//---------------------------------------------------------------
// STEP 4: Find top-K similar elements with positive similarity
//---------------------------------------------------------------
std::cout << "4) Find top-K similar elements:" << std::endl;
sw.start();
if (k_neighbors > 0)
{
//---------------------------------------------------
// 4.1) Sort similarity matrix by rating (row-wise)
//---------------------------------------------------
std::cout << " .... Sort similarity matrix by rating: ";
sw.start();
// Sort all the rows of the similarity_matrix by similarity.
// If two items have the same rating, sort them in descending order of item.
//similarity_matrix.sort_columns_by_value();
similarity_matrix.sort_columns_by_value();
// [DEBUG]: print similarity matrix sorted by similarity
if (output_debug_data)
{
std::ofstream ofs ("data_debug/debug_similarity_matrix_sorted_by_rating.txt");
ofs << similarity_matrix;
ofs.close();
}
sw.stop();
std::cout << sw.get_duration() << std::endl;
//--------------------------------------------------------
// 4.2) Cut the useless columns of the similarity matrix
//--------------------------------------------------------
std::cout << " .... Reduce similarity matrix: ";
sw.start();
sparse_matrix_csr similarity_matrix_reduced(list_target_items,
k_neighbors);
reduce_similarity_matrix_mpi (similarity_matrix,
similarity_matrix_reduced,
k_neighbors);
// [DEBUG]: print similarity matrix sorted by similarity
if (output_debug_data)
{
std::ofstream ofs ("data_debug/debug_similarity_matrix_reduced.txt");
ofs << similarity_matrix_reduced;
ofs.close();
}
sw.stop();
std::cout << sw.get_duration() << std::endl;
//---------------------------------------------------
// 4.3) Sort the reduced similarity matrix by items
//---------------------------------------------------
std::cout << " .... Sort similarity matrix by index: ";
sw.start();
// Sort all the rows of the similarity_matrix by index.
similarity_matrix_reduced.sort_columns_by_index();
// [DEBUG]: print similarity matrix sorted by similarity
if (output_debug_data)
{
std::ofstream ofs ("data_debug/debug_similarity_matrix_sorted.txt");
ofs << similarity_matrix_reduced;
ofs.close();
}
sw.stop();
std::cout << sw.get_duration() << std::endl;
//-----------------------------------------
// STEP 5: Compute predictions for targets
//-----------------------------------------
std::cout << "5) Compute predictions: ";
sw.start();
compute_predicted_ratings_mpi (targets,
user_item_rating_matrix,
similarity_matrix_reduced);
sw.stop();
std::cout << sw.get_duration() << std::endl;
}
else
{
//---------------------------------------------------
// 4.3) Sort the reduced similarity matrix by items
//---------------------------------------------------
std::cout << " .... Sort similarity matrix by index: ";
sw.start();
// Sort all the rows of the similarity_matrix by index.
// similarity_matrix.sort_columns_by_index();
similarity_matrix.sort_columns_by_index();
// [DEBUG]: print similarity matrix sorted by similarity
if (output_debug_data)
{
std::ofstream ofs ("data_debug/debug_similarity_matrix_sorted.txt");
ofs << similarity_matrix;
ofs.close();
}
sw.stop();
std::cout << sw.get_duration() << std::endl;
//-----------------------------------------
// STEP 5: Compute predictions for targets
//-----------------------------------------
std::cout << "5) Compute predictions: ";
sw.start();
compute_predicted_ratings_mpi (targets,
user_item_rating_matrix,
similarity_matrix);
sw.stop();
std::cout << sw.get_duration() << std::endl;
}
//-----------------------------------------------------
// STEP 6: Print the prediction matrix in output file
//-----------------------------------------------------
std::cout << "6) Print predictions on file: ";
sw.start();
std::ofstream ofs_output(output_file);
targets.print(ofs_output);
sw.stop();
double time_output = sw.get_duration();
std::cout << time_output << std::endl;
sw_total.stop();
double time_total = sw_total.get_duration();
std::cout << ">> Total computation time: " << time_total << std::endl;
std::cout << ">> Total computation time - no input/output: " << (time_total - time_input - time_output) << std::flush;
}
UPDATE - 05 February 2015: I don't know what happened, but the code is now running fine on my 64-bit machine. However, it is still very slow on a 32-bit VM that I need to use to run my code. I measured the size of a sparse_matrix object using sizeof and it occupies 52 bit. I think this might be causing a cache problem (credit goes to #Dark Falcon). Do you have any ideas on how I could solve this problem and make my algorithm run efficiently on the 32-bit VM?
UPDATE - 06 February 2015: Well, the problem comes and go. I tried to change the implementation of the sparse_matrix_csr class wrapping the data in a shared_ptr in the following way:
class sparse_matrix_csr
{
public:
\\ public methods omitted
private:
class sparse_matrix_csr_data
{
public:
sparse_matrix_csr_data() {}
sparse_matrix_csr_data(const std::vector<int> &row_indices): _row_indices(row_indices) {}
sparse_matrix_csr_data(const std::vector<int> &row_indices, const size_t nb_columns);
sparse_matrix_csr_data(const std::string file, bool file_is_sorted, bool skip_header, bool read_values);
// data
std::vector<int> _row_indices;
std::vector<int> _row_start_indices;
std::vector<int> _column_indices;
std::vector<double> _values;
bool _column_sorted_by_index;
};
std::shared_ptr<sparse_matrix_csr_data> _data;
};
This modification did not improve things. I am currently having problems both on the 32-bit VM and the 64-bit laptop. I have no idea about what is causing the program to be so slow.