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