// Copyright 2004 The Trustees of Indiana University. | |
// Use, modification and distribution is subject to the Boost Software | |
// License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at | |
// http://www.boost.org/LICENSE_1_0.txt) | |
// Authors: Douglas Gregor | |
// Peter Gottschling | |
// Andrew Lumsdaine | |
#ifndef BOOST_PARALLEL_DISTRIBUTION_HPP | |
#define BOOST_PARALLEL_DISTRIBUTION_HPP | |
#ifndef BOOST_GRAPH_USE_MPI | |
#error "Parallel BGL files should not be included unless <boost/graph/use_mpi.hpp> has been included" | |
#endif | |
#include <cstddef> | |
#include <vector> | |
#include <algorithm> | |
#include <numeric> | |
#include <boost/assert.hpp> | |
#include <boost/iterator/counting_iterator.hpp> | |
#include <boost/random/uniform_int.hpp> | |
#include <boost/shared_ptr.hpp> | |
#include <typeinfo> | |
namespace boost { namespace parallel { | |
template<typename ProcessGroup, typename SizeType = std::size_t> | |
class variant_distribution | |
{ | |
public: | |
typedef typename ProcessGroup::process_id_type process_id_type; | |
typedef typename ProcessGroup::process_size_type process_size_type; | |
typedef SizeType size_type; | |
private: | |
struct basic_distribution | |
{ | |
virtual ~basic_distribution() {} | |
virtual size_type block_size(process_id_type, size_type) const = 0; | |
virtual process_id_type in_process(size_type) const = 0; | |
virtual size_type local(size_type) const = 0; | |
virtual size_type global(size_type) const = 0; | |
virtual size_type global(process_id_type, size_type) const = 0; | |
virtual void* address() = 0; | |
virtual const void* address() const = 0; | |
virtual const std::type_info& type() const = 0; | |
}; | |
template<typename Distribution> | |
struct poly_distribution : public basic_distribution | |
{ | |
explicit poly_distribution(const Distribution& distribution) | |
: distribution_(distribution) { } | |
virtual size_type block_size(process_id_type id, size_type n) const | |
{ return distribution_.block_size(id, n); } | |
virtual process_id_type in_process(size_type i) const | |
{ return distribution_(i); } | |
virtual size_type local(size_type i) const | |
{ return distribution_.local(i); } | |
virtual size_type global(size_type n) const | |
{ return distribution_.global(n); } | |
virtual size_type global(process_id_type id, size_type n) const | |
{ return distribution_.global(id, n); } | |
virtual void* address() { return &distribution_; } | |
virtual const void* address() const { return &distribution_; } | |
virtual const std::type_info& type() const { return typeid(Distribution); } | |
private: | |
Distribution distribution_; | |
}; | |
public: | |
variant_distribution() { } | |
template<typename Distribution> | |
variant_distribution(const Distribution& distribution) | |
: distribution_(new poly_distribution<Distribution>(distribution)) { } | |
size_type block_size(process_id_type id, size_type n) const | |
{ return distribution_->block_size(id, n); } | |
process_id_type operator()(size_type i) const | |
{ return distribution_->in_process(i); } | |
size_type local(size_type i) const | |
{ return distribution_->local(i); } | |
size_type global(size_type n) const | |
{ return distribution_->global(n); } | |
size_type global(process_id_type id, size_type n) const | |
{ return distribution_->global(id, n); } | |
operator bool() const { return distribution_; } | |
void clear() { distribution_.reset(); } | |
template<typename T> | |
T* as() | |
{ | |
if (distribution_->type() == typeid(T)) | |
return static_cast<T*>(distribution_->address()); | |
else | |
return 0; | |
} | |
template<typename T> | |
const T* as() const | |
{ | |
if (distribution_->type() == typeid(T)) | |
return static_cast<T*>(distribution_->address()); | |
else | |
return 0; | |
} | |
private: | |
shared_ptr<basic_distribution> distribution_; | |
}; | |
struct block | |
{ | |
template<typename LinearProcessGroup> | |
explicit block(const LinearProcessGroup& pg, std::size_t n) | |
: id(process_id(pg)), p(num_processes(pg)), n(n) { } | |
// If there are n elements in the distributed data structure, returns the number of elements stored locally. | |
template<typename SizeType> | |
SizeType block_size(SizeType n) const | |
{ return (n / p) + ((std::size_t)(n % p) > id? 1 : 0); } | |
// If there are n elements in the distributed data structure, returns the number of elements stored on processor ID | |
template<typename SizeType, typename ProcessID> | |
SizeType block_size(ProcessID id, SizeType n) const | |
{ return (n / p) + ((ProcessID)(n % p) > id? 1 : 0); } | |
// Returns the processor on which element with global index i is stored | |
template<typename SizeType> | |
SizeType operator()(SizeType i) const | |
{ | |
SizeType cutoff_processor = n % p; | |
SizeType cutoff = cutoff_processor * (n / p + 1); | |
if (i < cutoff) return i / (n / p + 1); | |
else return cutoff_processor + (i - cutoff) / (n / p); | |
} | |
// Find the starting index for processor with the given id | |
template<typename ID> | |
std::size_t start(ID id) const | |
{ | |
std::size_t estimate = id * (n / p + 1); | |
ID cutoff_processor = n % p; | |
if (id < cutoff_processor) return estimate; | |
else return estimate - (id - cutoff_processor); | |
} | |
// Find the local index for the ith global element | |
template<typename SizeType> | |
SizeType local(SizeType i) const | |
{ | |
SizeType owner = (*this)(i); | |
return i - start(owner); | |
} | |
// Returns the global index of local element i | |
template<typename SizeType> | |
SizeType global(SizeType i) const | |
{ return global(id, i); } | |
// Returns the global index of the ith local element on processor id | |
template<typename ProcessID, typename SizeType> | |
SizeType global(ProcessID id, SizeType i) const | |
{ return i + start(id); } | |
private: | |
std::size_t id; //< The ID number of this processor | |
std::size_t p; //< The number of processors | |
std::size_t n; //< The size of the problem space | |
}; | |
// Block distribution with arbitrary block sizes | |
struct uneven_block | |
{ | |
typedef std::vector<std::size_t> size_vector; | |
template<typename LinearProcessGroup> | |
explicit uneven_block(const LinearProcessGroup& pg, const std::vector<std::size_t>& local_sizes) | |
: id(process_id(pg)), p(num_processes(pg)), local_sizes(local_sizes) | |
{ | |
BOOST_ASSERT(local_sizes.size() == p); | |
local_starts.resize(p + 1); | |
local_starts[0] = 0; | |
std::partial_sum(local_sizes.begin(), local_sizes.end(), &local_starts[1]); | |
n = local_starts[p]; | |
} | |
// To do maybe: enter local size in each process and gather in constructor (much handier) | |
// template<typename LinearProcessGroup> | |
// explicit uneven_block(const LinearProcessGroup& pg, std::size_t my_local_size) | |
// If there are n elements in the distributed data structure, returns the number of elements stored locally. | |
template<typename SizeType> | |
SizeType block_size(SizeType) const | |
{ return local_sizes[id]; } | |
// If there are n elements in the distributed data structure, returns the number of elements stored on processor ID | |
template<typename SizeType, typename ProcessID> | |
SizeType block_size(ProcessID id, SizeType) const | |
{ return local_sizes[id]; } | |
// Returns the processor on which element with global index i is stored | |
template<typename SizeType> | |
SizeType operator()(SizeType i) const | |
{ | |
BOOST_ASSERT (i >= (SizeType) 0 && i < (SizeType) n); // check for valid range | |
size_vector::const_iterator lb = std::lower_bound(local_starts.begin(), local_starts.end(), (std::size_t) i); | |
return ((SizeType)(*lb) == i ? lb : --lb) - local_starts.begin(); | |
} | |
// Find the starting index for processor with the given id | |
template<typename ID> | |
std::size_t start(ID id) const | |
{ | |
return local_starts[id]; | |
} | |
// Find the local index for the ith global element | |
template<typename SizeType> | |
SizeType local(SizeType i) const | |
{ | |
SizeType owner = (*this)(i); | |
return i - start(owner); | |
} | |
// Returns the global index of local element i | |
template<typename SizeType> | |
SizeType global(SizeType i) const | |
{ return global(id, i); } | |
// Returns the global index of the ith local element on processor id | |
template<typename ProcessID, typename SizeType> | |
SizeType global(ProcessID id, SizeType i) const | |
{ return i + start(id); } | |
private: | |
std::size_t id; //< The ID number of this processor | |
std::size_t p; //< The number of processors | |
std::size_t n; //< The size of the problem space | |
std::vector<std::size_t> local_sizes; //< The sizes of all blocks | |
std::vector<std::size_t> local_starts; //< Lowest global index of each block | |
}; | |
struct oned_block_cyclic | |
{ | |
template<typename LinearProcessGroup> | |
explicit oned_block_cyclic(const LinearProcessGroup& pg, std::size_t size) | |
: id(process_id(pg)), p(num_processes(pg)), size(size) { } | |
template<typename SizeType> | |
SizeType block_size(SizeType n) const | |
{ | |
return block_size(id, n); | |
} | |
template<typename SizeType, typename ProcessID> | |
SizeType block_size(ProcessID id, SizeType n) const | |
{ | |
SizeType all_blocks = n / size; | |
SizeType extra_elements = n % size; | |
SizeType everyone_gets = all_blocks / p; | |
SizeType extra_blocks = all_blocks % p; | |
SizeType my_blocks = everyone_gets + (p < extra_blocks? 1 : 0); | |
SizeType my_elements = my_blocks * size | |
+ (p == extra_blocks? extra_elements : 0); | |
return my_elements; | |
} | |
template<typename SizeType> | |
SizeType operator()(SizeType i) const | |
{ | |
return (i / size) % p; | |
} | |
template<typename SizeType> | |
SizeType local(SizeType i) const | |
{ | |
return ((i / size) / p) * size + i % size; | |
} | |
template<typename SizeType> | |
SizeType global(SizeType i) const | |
{ return global(id, i); } | |
template<typename ProcessID, typename SizeType> | |
SizeType global(ProcessID id, SizeType i) const | |
{ | |
return ((i / size) * p + id) * size + i % size; | |
} | |
private: | |
std::size_t id; //< The ID number of this processor | |
std::size_t p; //< The number of processors | |
std::size_t size; //< Block size | |
}; | |
struct twod_block_cyclic | |
{ | |
template<typename LinearProcessGroup> | |
explicit twod_block_cyclic(const LinearProcessGroup& pg, | |
std::size_t block_rows, std::size_t block_columns, | |
std::size_t data_columns_per_row) | |
: id(process_id(pg)), p(num_processes(pg)), | |
block_rows(block_rows), block_columns(block_columns), | |
data_columns_per_row(data_columns_per_row) | |
{ } | |
template<typename SizeType> | |
SizeType block_size(SizeType n) const | |
{ | |
return block_size(id, n); | |
} | |
template<typename SizeType, typename ProcessID> | |
SizeType block_size(ProcessID id, SizeType n) const | |
{ | |
// TBD: This is really lame :) | |
int result = -1; | |
while (n > 0) { | |
--n; | |
if ((*this)(n) == id && (int)local(n) > result) result = local(n); | |
} | |
++result; | |
// std::cerr << "Block size of id " << id << " is " << result << std::endl; | |
return result; | |
} | |
template<typename SizeType> | |
SizeType operator()(SizeType i) const | |
{ | |
SizeType result = get_block_num(i) % p; | |
// std::cerr << "Item " << i << " goes on processor " << result << std::endl; | |
return result; | |
} | |
template<typename SizeType> | |
SizeType local(SizeType i) const | |
{ | |
// Compute the start of the block | |
std::size_t block_num = get_block_num(i); | |
// std::cerr << "Item " << i << " is in block #" << block_num << std::endl; | |
std::size_t local_block_num = block_num / p; | |
std::size_t block_start = local_block_num * block_rows * block_columns; | |
// Compute the offset into the block | |
std::size_t data_row = i / data_columns_per_row; | |
std::size_t data_col = i % data_columns_per_row; | |
std::size_t block_offset = (data_row % block_rows) * block_columns | |
+ (data_col % block_columns); | |
// std::cerr << "Item " << i << " maps to local index " << block_start+block_offset << std::endl; | |
return block_start + block_offset; | |
} | |
template<typename SizeType> | |
SizeType global(SizeType i) const | |
{ | |
// Compute the (global) block in which this element resides | |
SizeType local_block_num = i / (block_rows * block_columns); | |
SizeType block_offset = i % (block_rows * block_columns); | |
SizeType block_num = local_block_num * p + id; | |
// Compute the position of the start of the block (globally) | |
SizeType block_start = block_num * block_rows * block_columns; | |
std::cerr << "Block " << block_num << " starts at index " << block_start | |
<< std::endl; | |
// Compute the row and column of this block | |
SizeType block_row = block_num / (data_columns_per_row / block_columns); | |
SizeType block_col = block_num % (data_columns_per_row / block_columns); | |
SizeType row_in_block = block_offset / block_columns; | |
SizeType col_in_block = block_offset % block_columns; | |
std::cerr << "Local index " << i << " is in block at row " << block_row | |
<< ", column " << block_col << ", in-block row " << row_in_block | |
<< ", in-block col " << col_in_block << std::endl; | |
SizeType result = block_row * block_rows + block_col * block_columns | |
+ row_in_block * block_rows + col_in_block; | |
std::cerr << "global(" << i << "@" << id << ") = " << result | |
<< " =? " << local(result) << std::endl; | |
BOOST_ASSERT(i == local(result)); | |
return result; | |
} | |
private: | |
template<typename SizeType> | |
std::size_t get_block_num(SizeType i) const | |
{ | |
std::size_t data_row = i / data_columns_per_row; | |
std::size_t data_col = i % data_columns_per_row; | |
std::size_t block_row = data_row / block_rows; | |
std::size_t block_col = data_col / block_columns; | |
std::size_t blocks_in_row = data_columns_per_row / block_columns; | |
std::size_t block_num = block_col * blocks_in_row + block_row; | |
return block_num; | |
} | |
std::size_t id; //< The ID number of this processor | |
std::size_t p; //< The number of processors | |
std::size_t block_rows; //< The # of rows in each block | |
std::size_t block_columns; //< The # of columns in each block | |
std::size_t data_columns_per_row; //< The # of columns per row of data | |
}; | |
class twod_random | |
{ | |
template<typename RandomNumberGen> | |
struct random_int | |
{ | |
explicit random_int(RandomNumberGen& gen) : gen(gen) { } | |
template<typename T> | |
T operator()(T n) const | |
{ | |
uniform_int<T> distrib(0, n-1); | |
return distrib(gen); | |
} | |
private: | |
RandomNumberGen& gen; | |
}; | |
public: | |
template<typename LinearProcessGroup, typename RandomNumberGen> | |
explicit twod_random(const LinearProcessGroup& pg, | |
std::size_t block_rows, std::size_t block_columns, | |
std::size_t data_columns_per_row, | |
std::size_t n, | |
RandomNumberGen& gen) | |
: id(process_id(pg)), p(num_processes(pg)), | |
block_rows(block_rows), block_columns(block_columns), | |
data_columns_per_row(data_columns_per_row), | |
global_to_local(n / (block_rows * block_columns)) | |
{ | |
std::copy(make_counting_iterator(std::size_t(0)), | |
make_counting_iterator(global_to_local.size()), | |
global_to_local.begin()); | |
random_int<RandomNumberGen> rand(gen); | |
std::random_shuffle(global_to_local.begin(), global_to_local.end(), rand); | |
} | |
template<typename SizeType> | |
SizeType block_size(SizeType n) const | |
{ | |
return block_size(id, n); | |
} | |
template<typename SizeType, typename ProcessID> | |
SizeType block_size(ProcessID id, SizeType n) const | |
{ | |
// TBD: This is really lame :) | |
int result = -1; | |
while (n > 0) { | |
--n; | |
if ((*this)(n) == id && (int)local(n) > result) result = local(n); | |
} | |
++result; | |
// std::cerr << "Block size of id " << id << " is " << result << std::endl; | |
return result; | |
} | |
template<typename SizeType> | |
SizeType operator()(SizeType i) const | |
{ | |
SizeType result = get_block_num(i) % p; | |
// std::cerr << "Item " << i << " goes on processor " << result << std::endl; | |
return result; | |
} | |
template<typename SizeType> | |
SizeType local(SizeType i) const | |
{ | |
// Compute the start of the block | |
std::size_t block_num = get_block_num(i); | |
// std::cerr << "Item " << i << " is in block #" << block_num << std::endl; | |
std::size_t local_block_num = block_num / p; | |
std::size_t block_start = local_block_num * block_rows * block_columns; | |
// Compute the offset into the block | |
std::size_t data_row = i / data_columns_per_row; | |
std::size_t data_col = i % data_columns_per_row; | |
std::size_t block_offset = (data_row % block_rows) * block_columns | |
+ (data_col % block_columns); | |
// std::cerr << "Item " << i << " maps to local index " << block_start+block_offset << std::endl; | |
return block_start + block_offset; | |
} | |
private: | |
template<typename SizeType> | |
std::size_t get_block_num(SizeType i) const | |
{ | |
std::size_t data_row = i / data_columns_per_row; | |
std::size_t data_col = i % data_columns_per_row; | |
std::size_t block_row = data_row / block_rows; | |
std::size_t block_col = data_col / block_columns; | |
std::size_t blocks_in_row = data_columns_per_row / block_columns; | |
std::size_t block_num = block_col * blocks_in_row + block_row; | |
return global_to_local[block_num]; | |
} | |
std::size_t id; //< The ID number of this processor | |
std::size_t p; //< The number of processors | |
std::size_t block_rows; //< The # of rows in each block | |
std::size_t block_columns; //< The # of columns in each block | |
std::size_t data_columns_per_row; //< The # of columns per row of data | |
std::vector<std::size_t> global_to_local; | |
}; | |
class random_distribution | |
{ | |
template<typename RandomNumberGen> | |
struct random_int | |
{ | |
explicit random_int(RandomNumberGen& gen) : gen(gen) { } | |
template<typename T> | |
T operator()(T n) const | |
{ | |
uniform_int<T> distrib(0, n-1); | |
return distrib(gen); | |
} | |
private: | |
RandomNumberGen& gen; | |
}; | |
public: | |
template<typename LinearProcessGroup, typename RandomNumberGen> | |
random_distribution(const LinearProcessGroup& pg, RandomNumberGen& gen, | |
std::size_t n) | |
: base(pg, n), local_to_global(n), global_to_local(n) | |
{ | |
std::copy(make_counting_iterator(std::size_t(0)), | |
make_counting_iterator(n), | |
local_to_global.begin()); | |
random_int<RandomNumberGen> rand(gen); | |
std::random_shuffle(local_to_global.begin(), local_to_global.end(), rand); | |
for (std::vector<std::size_t>::size_type i = 0; i < n; ++i) | |
global_to_local[local_to_global[i]] = i; | |
} | |
template<typename SizeType> | |
SizeType block_size(SizeType n) const | |
{ return base.block_size(n); } | |
template<typename SizeType, typename ProcessID> | |
SizeType block_size(ProcessID id, SizeType n) const | |
{ return base.block_size(id, n); } | |
template<typename SizeType> | |
SizeType operator()(SizeType i) const | |
{ | |
return base(global_to_local[i]); | |
} | |
template<typename SizeType> | |
SizeType local(SizeType i) const | |
{ | |
return base.local(global_to_local[i]); | |
} | |
template<typename ProcessID, typename SizeType> | |
SizeType global(ProcessID p, SizeType i) const | |
{ | |
return local_to_global[base.global(p, i)]; | |
} | |
template<typename SizeType> | |
SizeType global(SizeType i) const | |
{ | |
return local_to_global[base.global(i)]; | |
} | |
private: | |
block base; | |
std::vector<std::size_t> local_to_global; | |
std::vector<std::size_t> global_to_local; | |
}; | |
} } // end namespace boost::parallel | |
#endif // BOOST_PARALLEL_DISTRIBUTION_HPP | |