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/* Copyright 2013 IST Austria
Contributed by: Ulrich Bauer, Michael Kerber, Jan Reininghaus
This file is part of PHAT.
PHAT 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.
PHAT 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 PHAT. If not, see <http://www.gnu.org/licenses/>. */
#pragma once
#include <phat/helpers/misc.h>
#include <phat/boundary_matrix.h>
namespace phat {
class chunk_reduction {
public:
enum column_type { GLOBAL
, LOCAL_POSITIVE
, LOCAL_NEGATIVE };
public:
template< typename Representation >
void operator() ( boundary_matrix< Representation >& boundary_matrix ) {
const index nr_columns = boundary_matrix.get_num_cols();
if( omp_get_max_threads( ) > nr_columns )
omp_set_num_threads( 1 );
const dimension max_dim = boundary_matrix.get_max_dim();
std::vector< index > lowest_one_lookup( nr_columns, -1 );
std::vector < column_type > column_type( nr_columns, GLOBAL );
std::vector< char > is_active( nr_columns, false );
const index chunk_size = omp_get_max_threads() == 1 ? (index)sqrt( (double)nr_columns ) : nr_columns / omp_get_max_threads();
std::vector< index > chunk_boundaries;
for( index cur_boundary = 0; cur_boundary < nr_columns; cur_boundary += chunk_size )
chunk_boundaries.push_back( cur_boundary );
chunk_boundaries.push_back( nr_columns );
for( dimension cur_dim = max_dim; cur_dim >= 1; cur_dim-- ) {
// Phase 1: Reduce chunks locally -- 1st pass
#pragma omp parallel for schedule( guided, 1 )
for( index chunk_id = 0; chunk_id < (index)chunk_boundaries.size() - 1; chunk_id++ )
_local_chunk_reduction( boundary_matrix, lowest_one_lookup, column_type, cur_dim,
chunk_boundaries[ chunk_id ], chunk_boundaries[ chunk_id + 1 ], chunk_boundaries[ chunk_id ] );
boundary_matrix.sync();
// Phase 1: Reduce chunks locally -- 2nd pass
#pragma omp parallel for schedule( guided, 1 )
for( index chunk_id = 1; chunk_id < (index)chunk_boundaries.size( ) - 1; chunk_id++ )
_local_chunk_reduction( boundary_matrix, lowest_one_lookup, column_type, cur_dim,
chunk_boundaries[ chunk_id ], chunk_boundaries[ chunk_id + 1 ], chunk_boundaries[ chunk_id - 1 ] );
boundary_matrix.sync( );
}
// get global columns
std::vector< index > global_columns;
for( index cur_col_idx = 0; cur_col_idx < nr_columns; cur_col_idx++ )
if( column_type[ cur_col_idx ] == GLOBAL )
global_columns.push_back( cur_col_idx );
// get active columns
#pragma omp parallel for
for( index idx = 0; idx < (index)global_columns.size(); idx++ )
is_active[ global_columns[ idx ] ] = true;
_get_active_columns( boundary_matrix, lowest_one_lookup, column_type, global_columns, is_active );
// Phase 2+3: Simplify columns and reduce them
for( dimension cur_dim = max_dim; cur_dim >= 1; cur_dim-- ) {
// Phase 2: Simplify columns
std::vector< index > temp_col;
#pragma omp parallel for schedule( guided, 1 ), private( temp_col )
for( index idx = 0; idx < (index)global_columns.size(); idx++ )
if( boundary_matrix.get_dim( global_columns[ idx ] ) == cur_dim )
_global_column_simplification( global_columns[ idx ], boundary_matrix, lowest_one_lookup, column_type, is_active, temp_col );
boundary_matrix.sync();
// Phase 3: Reduce columns
for( index idx = 0; idx < (index)global_columns.size(); idx++ ) {
index cur_col = global_columns[ idx ];
if( boundary_matrix.get_dim( cur_col ) == cur_dim && column_type[ cur_col ] == GLOBAL ) {
index lowest_one = boundary_matrix.get_max_index( cur_col );
while( lowest_one != -1 && lowest_one_lookup[ lowest_one ] != -1 ) {
boundary_matrix.add_to( lowest_one_lookup[ lowest_one ], cur_col );
lowest_one = boundary_matrix.get_max_index( cur_col );
}
if( lowest_one != -1 ) {
lowest_one_lookup[ lowest_one ] = cur_col;
boundary_matrix.clear( lowest_one );
}
boundary_matrix.finalize( cur_col );
}
}
}
boundary_matrix.sync();
}
protected:
template< typename Representation >
void _local_chunk_reduction( boundary_matrix< Representation >& boundary_matrix
, std::vector<index>& lowest_one_lookup
, std::vector< column_type >& column_type
, const dimension cur_dim
, const index chunk_begin
, const index chunk_end
, const index row_begin ) {
for( index cur_col = chunk_begin; cur_col < chunk_end; cur_col++ ) {
if( column_type[ cur_col ] == GLOBAL && boundary_matrix.get_dim( cur_col ) == cur_dim ) {
index lowest_one = boundary_matrix.get_max_index( cur_col );
while( lowest_one != -1 && lowest_one >= row_begin && lowest_one_lookup[ lowest_one ] != -1 ) {
boundary_matrix.add_to( lowest_one_lookup[ lowest_one ], cur_col );
lowest_one = boundary_matrix.get_max_index( cur_col );
}
if( lowest_one >= row_begin ) {
lowest_one_lookup[ lowest_one ] = cur_col;
column_type[ cur_col ] = LOCAL_NEGATIVE;
column_type[ lowest_one ] = LOCAL_POSITIVE;
boundary_matrix.clear( lowest_one );
boundary_matrix.finalize( cur_col );
}
}
}
}
template< typename Representation >
void _get_active_columns( const boundary_matrix< Representation >& boundary_matrix
, const std::vector< index >& lowest_one_lookup
, const std::vector< column_type >& column_type
, const std::vector< index >& global_columns
, std::vector< char >& is_active ) {
const index nr_columns = boundary_matrix.get_num_cols();
std::vector< char > finished( nr_columns, false );
std::vector< std::pair < index, index > > stack;
std::vector< index > cur_col_values;
#pragma omp parallel for schedule( guided, 1 ), private( stack, cur_col_values )
for( index idx = 0; idx < (index)global_columns.size(); idx++ ) {
bool pop_next = false;
index start_col = global_columns[ idx ];
stack.push_back( std::pair< index, index >( start_col, -1 ) );
while( !stack.empty() ) {
index cur_col = stack.back().first;
index prev_col = stack.back().second;
if( pop_next ) {
stack.pop_back();
pop_next = false;
if( prev_col != -1 ) {
if( is_active[ cur_col ] ) {
is_active[ prev_col ] = true;
}
if( prev_col == stack.back().first ) {
finished[ prev_col ] = true;
pop_next = true;
}
}
} else {
pop_next = true;
boundary_matrix.get_col( cur_col, cur_col_values );
for( index idx = 0; idx < (index) cur_col_values.size(); idx++ ) {
index cur_row = cur_col_values[ idx ];
if( ( column_type[ cur_row ] == GLOBAL ) ) {
is_active[ cur_col ] = true;
} else if( column_type[ cur_row ] == LOCAL_POSITIVE ) {
index next_col = lowest_one_lookup[ cur_row ];
if( next_col != cur_col && !finished[ cur_col ] ) {
stack.push_back( std::make_pair( next_col, cur_col ) );
pop_next = false;
}
}
}
}
}
}
}
template< typename Representation >
void _global_column_simplification( const index col_idx
, boundary_matrix< Representation >& boundary_matrix
, const std::vector< index >& lowest_one_lookup
, const std::vector< column_type >& column_type
, const std::vector< char >& is_active
, std::vector< index >& temp_col )
{
temp_col.clear();
while( !boundary_matrix.is_empty( col_idx ) ) {
index cur_row = boundary_matrix.get_max_index( col_idx );
switch( column_type[ cur_row ] ) {
case GLOBAL:
temp_col.push_back( cur_row );
boundary_matrix.remove_max( col_idx );
break;
case LOCAL_NEGATIVE:
boundary_matrix.remove_max( col_idx );
break;
case LOCAL_POSITIVE:
if( is_active[ lowest_one_lookup[ cur_row ] ] )
boundary_matrix.add_to( lowest_one_lookup[ cur_row ], col_idx );
else
boundary_matrix.remove_max( col_idx );
break;
}
}
std::reverse( temp_col.begin(), temp_col.end() );
boundary_matrix.set_col( col_idx, temp_col );
}
};
}
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