EVOLUTION-MANAGER

Edit File: find_extrema_with_reduce.hpp

//---------------------------------------------------------------------------// // Copyright (c) 2015 Jakub Szuppe <j.szuppe@gmail.com> // // Distributed under 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 // // See http://boostorg.github.com/compute for more information. //---------------------------------------------------------------------------// #ifndef BOOST_COMPUTE_ALGORITHM_DETAIL_FIND_EXTREMA_WITH_REDUCE_HPP #define BOOST_COMPUTE_ALGORITHM_DETAIL_FIND_EXTREMA_WITH_REDUCE_HPP #include <algorithm> #include <boost/compute/types.hpp> #include <boost/compute/command_queue.hpp> #include <boost/compute/algorithm/copy.hpp> #include <boost/compute/allocator/pinned_allocator.hpp> #include <boost/compute/container/vector.hpp> #include <boost/compute/detail/meta_kernel.hpp> #include <boost/compute/detail/iterator_range_size.hpp> #include <boost/compute/detail/parameter_cache.hpp> #include <boost/compute/memory/local_buffer.hpp> #include <boost/compute/type_traits/type_name.hpp> #include <boost/compute/utility/program_cache.hpp> namespace boost { namespace compute { namespace detail { template<class InputIterator> bool find_extrema_with_reduce_requirements_met(InputIterator first, InputIterator last, command_queue &queue) { typedef typename std::iterator_traits<InputIterator>::value_type input_type; const device &device = queue.get_device(); // device must have dedicated local memory storage // otherwise reduction would be highly inefficient if(device.get_info<CL_DEVICE_LOCAL_MEM_TYPE>() != CL_LOCAL) { return false; } const size_t max_work_group_size = device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>(); // local memory size in bytes (per compute unit) const size_t local_mem_size = device.get_info<CL_DEVICE_LOCAL_MEM_SIZE>(); std::string cache_key = std::string("__boost_find_extrema_reduce_") + type_name<input_type>(); // load parameters boost::shared_ptr<parameter_cache> parameters = detail::parameter_cache::get_global_cache(device); // Get preferred work group size size_t work_group_size = parameters->get(cache_key, "wgsize", 256); work_group_size = (std::min)(max_work_group_size, work_group_size); // local memory size needed to perform parallel reduction size_t required_local_mem_size = 0; // indices size required_local_mem_size += sizeof(uint_) * work_group_size; // values size required_local_mem_size += sizeof(input_type) * work_group_size; // at least 4 work groups per compute unit otherwise reduction // would be highly inefficient return ((required_local_mem_size * 4) <= local_mem_size); } /// \internal_ /// Algorithm finds the first extremum in given range, i.e., with the lowest /// index. /// /// If \p use_input_idx is false, it's assumed that input data is ordered by /// increasing index and \p input_idx is not used in the algorithm. template<class InputIterator, class ResultIterator, class Compare> inline void find_extrema_with_reduce(InputIterator input, vector<uint_>::iterator input_idx, size_t count, ResultIterator result, vector<uint_>::iterator result_idx, size_t work_groups_no, size_t work_group_size, Compare compare, const bool find_minimum, const bool use_input_idx, command_queue &queue) { typedef typename std::iterator_traits<InputIterator>::value_type input_type; const context &context = queue.get_context(); meta_kernel k("find_extrema_reduce"); size_t count_arg = k.add_arg<uint_>("count"); size_t block_arg = k.add_arg<input_type *>(memory_object::local_memory, "block"); size_t block_idx_arg = k.add_arg<uint_ *>(memory_object::local_memory, "block_idx"); k << // Work item global id k.decl<const uint_>("gid") << " = get_global_id(0);\n" << // Index of element that will be read from input buffer k.decl<uint_>("idx") << " = gid;\n" << k.decl<input_type>("acc") << ";\n" << k.decl<uint_>("acc_idx") << ";\n" << "if(gid < count) {\n" << // Real index of currently best element "#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" << k.var<uint_>("acc_idx") << " = " << input_idx[k.var<uint_>("idx")] << ";\n" << "#else\n" << k.var<uint_>("acc_idx") << " = idx;\n" << "#endif\n" << // Init accumulator with first[get_global_id(0)] "acc = " << input[k.var<uint_>("idx")] << ";\n" << "idx += get_global_size(0);\n" << "}\n" << k.decl<bool>("compare_result") << ";\n" << k.decl<bool>("equal") << ";\n\n" << "while( idx < count ){\n" << // Next element k.decl<input_type>("next") << " = " << input[k.var<uint_>("idx")] << ";\n" << "#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" << k.decl<uint_>("next_idx") << " = " << input_idx[k.var<uint_>("idx")] << ";\n" << "#endif\n" << // Comparison between currently best element (acc) and next element "#ifdef BOOST_COMPUTE_FIND_MAXIMUM\n" << "compare_result = " << compare(k.var<input_type>("next"), k.var<input_type>("acc")) << ";\n" << "# ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" << "equal = !compare_result && !" << compare(k.var<input_type>("acc"), k.var<input_type>("next")) << ";\n" << "# endif\n" << "#else\n" << "compare_result = " << compare(k.var<input_type>("acc"), k.var<input_type>("next")) << ";\n" << "# ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" << "equal = !compare_result && !" << compare(k.var<input_type>("next"), k.var<input_type>("acc")) << ";\n" << "# endif\n" << "#endif\n" << // save the winner "acc = compare_result ? acc : next;\n" << "#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" << "acc_idx = compare_result ? " << "acc_idx : " << "(equal ? min(acc_idx, next_idx) : next_idx);\n" << "#else\n" << "acc_idx = compare_result ? acc_idx : idx;\n" << "#endif\n" << "idx += get_global_size(0);\n" << "}\n\n" << // Work item local id k.decl<const uint_>("lid") << " = get_local_id(0);\n" << "block[lid] = acc;\n" << "block_idx[lid] = acc_idx;\n" << "barrier(CLK_LOCAL_MEM_FENCE);\n" << k.decl<uint_>("group_offset") << " = count - (get_local_size(0) * get_group_id(0));\n\n"; k << "#pragma unroll\n" "for(" << k.decl<uint_>("offset") << " = " << uint_(work_group_size) << " / 2; offset > 0; " << "offset = offset / 2) {\n" << "if((lid < offset) && ((lid + offset) < group_offset)) { \n" << k.decl<input_type>("mine") << " = block[lid];\n" << k.decl<input_type>("other") << " = block[lid+offset];\n" << "#ifdef BOOST_COMPUTE_FIND_MAXIMUM\n" << "compare_result = " << compare(k.var<input_type>("other"), k.var<input_type>("mine")) << ";\n" << "equal = !compare_result && !" << compare(k.var<input_type>("mine"), k.var<input_type>("other")) << ";\n" << "#else\n" << "compare_result = " << compare(k.var<input_type>("mine"), k.var<input_type>("other")) << ";\n" << "equal = !compare_result && !" << compare(k.var<input_type>("other"), k.var<input_type>("mine")) << ";\n" << "#endif\n" << "block[lid] = compare_result ? mine : other;\n" << k.decl<uint_>("mine_idx") << " = block_idx[lid];\n" << k.decl<uint_>("other_idx") << " = block_idx[lid+offset];\n" << "block_idx[lid] = compare_result ? " << "mine_idx : " << "(equal ? min(mine_idx, other_idx) : other_idx);\n" << "}\n" "barrier(CLK_LOCAL_MEM_FENCE);\n" << "}\n\n" << // write block result to global output "if(lid == 0){\n" << result[k.var<uint_>("get_group_id(0)")] << " = block[0];\n" << result_idx[k.var<uint_>("get_group_id(0)")] << " = block_idx[0];\n" << "}"; std::string options; if(!find_minimum){ options = "-DBOOST_COMPUTE_FIND_MAXIMUM"; } if(use_input_idx){ options += " -DBOOST_COMPUTE_USE_INPUT_IDX"; } kernel kernel = k.compile(context, options); kernel.set_arg(count_arg, static_cast<uint_>(count)); kernel.set_arg(block_arg, local_buffer<input_type>(work_group_size)); kernel.set_arg(block_idx_arg, local_buffer<uint_>(work_group_size)); queue.enqueue_1d_range_kernel(kernel, 0, work_groups_no * work_group_size, work_group_size); } template<class InputIterator, class ResultIterator, class Compare> inline void find_extrema_with_reduce(InputIterator input, size_t count, ResultIterator result, vector<uint_>::iterator result_idx, size_t work_groups_no, size_t work_group_size, Compare compare, const bool find_minimum, command_queue &queue) { // dummy will not be used buffer_iterator<uint_> dummy = result_idx; return find_extrema_with_reduce( input, dummy, count, result, result_idx, work_groups_no, work_group_size, compare, find_minimum, false, queue ); } // Space complexity: \Omega(2 * work-group-size * work-groups-per-compute-unit) template<class InputIterator, class Compare> InputIterator find_extrema_with_reduce(InputIterator first, InputIterator last, Compare compare, const bool find_minimum, command_queue &queue) { typedef typename std::iterator_traits<InputIterator>::difference_type difference_type; typedef typename std::iterator_traits<InputIterator>::value_type input_type; const context &context = queue.get_context(); const device &device = queue.get_device(); // Getting information about used queue and device const size_t compute_units_no = device.get_info<CL_DEVICE_MAX_COMPUTE_UNITS>(); const size_t max_work_group_size = device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>(); const size_t count = detail::iterator_range_size(first, last); std::string cache_key = std::string("__boost_find_extrema_with_reduce_") + type_name<input_type>(); // load parameters boost::shared_ptr<parameter_cache> parameters = detail::parameter_cache::get_global_cache(device); // get preferred work group size and preferred number // of work groups per compute unit size_t work_group_size = parameters->get(cache_key, "wgsize", 256); size_t work_groups_per_cu = parameters->get(cache_key, "wgpcu", 100); // calculate work group size and number of work groups work_group_size = (std::min)(max_work_group_size, work_group_size); size_t work_groups_no = compute_units_no * work_groups_per_cu; work_groups_no = (std::min)( work_groups_no, static_cast<size_t>(std::ceil(float(count) / work_group_size)) ); // phase I: finding candidates for extremum // device buffors for extremum candidates and their indices // each work-group computes its candidate vector<input_type> candidates(work_groups_no, context); vector<uint_> candidates_idx(work_groups_no, context); // finding candidates for first extremum and their indices find_extrema_with_reduce( first, count, candidates.begin(), candidates_idx.begin(), work_groups_no, work_group_size, compare, find_minimum, queue ); // phase II: finding extremum from among the candidates // zero-copy buffers for final result (value and index) vector<input_type, ::boost::compute::pinned_allocator<input_type> > result(1, context); vector<uint_, ::boost::compute::pinned_allocator<uint_> > result_idx(1, context); // get extremum from among the candidates find_extrema_with_reduce( candidates.begin(), candidates_idx.begin(), work_groups_no, result.begin(), result_idx.begin(), 1, work_group_size, compare, find_minimum, true, queue ); // mapping extremum index to host uint_* result_idx_host_ptr = static_cast<uint_*>( queue.enqueue_map_buffer( result_idx.get_buffer(), command_queue::map_read, 0, sizeof(uint_) ) ); return first + static_cast<difference_type>(*result_idx_host_ptr); } template<class InputIterator> InputIterator find_extrema_with_reduce(InputIterator first, InputIterator last, ::boost::compute::less< typename std::iterator_traits< InputIterator >::value_type > compare, const bool find_minimum, command_queue &queue) { typedef typename std::iterator_traits<InputIterator>::difference_type difference_type; typedef typename std::iterator_traits<InputIterator>::value_type input_type; const context &context = queue.get_context(); const device &device = queue.get_device(); // Getting information about used queue and device const size_t compute_units_no = device.get_info<CL_DEVICE_MAX_COMPUTE_UNITS>(); const size_t max_work_group_size = device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>(); const size_t count = detail::iterator_range_size(first, last); std::string cache_key = std::string("__boost_find_extrema_with_reduce_") + type_name<input_type>(); // load parameters boost::shared_ptr<parameter_cache> parameters = detail::parameter_cache::get_global_cache(device); // get preferred work group size and preferred number // of work groups per compute unit size_t work_group_size = parameters->get(cache_key, "wgsize", 256); size_t work_groups_per_cu = parameters->get(cache_key, "wgpcu", 64); // calculate work group size and number of work groups work_group_size = (std::min)(max_work_group_size, work_group_size); size_t work_groups_no = compute_units_no * work_groups_per_cu; work_groups_no = (std::min)( work_groups_no, static_cast<size_t>(std::ceil(float(count) / work_group_size)) ); // phase I: finding candidates for extremum // device buffors for extremum candidates and their indices // each work-group computes its candidate // zero-copy buffers are used to eliminate copying data back to host vector<input_type, ::boost::compute::pinned_allocator<input_type> > candidates(work_groups_no, context); vector<uint_, ::boost::compute::pinned_allocator <uint_> > candidates_idx(work_groups_no, context); // finding candidates for first extremum and their indices find_extrema_with_reduce( first, count, candidates.begin(), candidates_idx.begin(), work_groups_no, work_group_size, compare, find_minimum, queue ); // phase II: finding extremum from among the candidates // mapping candidates and their indices to host input_type* candidates_host_ptr = static_cast<input_type*>( queue.enqueue_map_buffer( candidates.get_buffer(), command_queue::map_read, 0, work_groups_no * sizeof(input_type) ) ); uint_* candidates_idx_host_ptr = static_cast<uint_*>( queue.enqueue_map_buffer( candidates_idx.get_buffer(), command_queue::map_read, 0, work_groups_no * sizeof(uint_) ) ); input_type* i = candidates_host_ptr; uint_* idx = candidates_idx_host_ptr; uint_* extremum_idx = idx; input_type extremum = *candidates_host_ptr; i++; idx++; // find extremum (serial) from among the candidates on host if(!find_minimum) { while(idx != (candidates_idx_host_ptr + work_groups_no)) { input_type next = *i; bool compare_result = next > extremum; bool equal = next == extremum; extremum = compare_result ? next : extremum; extremum_idx = compare_result ? idx : extremum_idx; extremum_idx = equal ? ((*extremum_idx < *idx) ? extremum_idx : idx) : extremum_idx; idx++, i++; } } else { while(idx != (candidates_idx_host_ptr + work_groups_no)) { input_type next = *i; bool compare_result = next < extremum; bool equal = next == extremum; extremum = compare_result ? next : extremum; extremum_idx = compare_result ? idx : extremum_idx; extremum_idx = equal ? ((*extremum_idx < *idx) ? extremum_idx : idx) : extremum_idx; idx++, i++; } } return first + static_cast<difference_type>(*extremum_idx); } } // end detail namespace } // end compute namespace } // end boost namespace #endif // BOOST_COMPUTE_ALGORITHM_DETAIL_FIND_EXTREMA_WITH_REDUCE_HPP