444 lines
19 KiB
Plaintext
444 lines
19 KiB
Plaintext
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//---------------------------------------------------------------------------//
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// Copyright (c) 2015 Jakub Szuppe <j.szuppe@gmail.com>
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//
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// Distributed under the Boost Software License, Version 1.0
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// 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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//
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// See http://boostorg.github.com/compute for more information.
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//---------------------------------------------------------------------------//
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#ifndef BOOST_COMPUTE_ALGORITHM_DETAIL_FIND_EXTREMA_WITH_REDUCE_HPP
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#define BOOST_COMPUTE_ALGORITHM_DETAIL_FIND_EXTREMA_WITH_REDUCE_HPP
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#include <algorithm>
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#include <boost/compute/types.hpp>
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#include <boost/compute/command_queue.hpp>
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#include <boost/compute/algorithm/copy.hpp>
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#include <boost/compute/allocator/pinned_allocator.hpp>
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#include <boost/compute/container/vector.hpp>
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#include <boost/compute/detail/meta_kernel.hpp>
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#include <boost/compute/detail/iterator_range_size.hpp>
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#include <boost/compute/detail/parameter_cache.hpp>
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#include <boost/compute/memory/local_buffer.hpp>
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#include <boost/compute/type_traits/type_name.hpp>
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#include <boost/compute/utility/program_cache.hpp>
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namespace boost {
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namespace compute {
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namespace detail {
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template<class InputIterator>
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bool find_extrema_with_reduce_requirements_met(InputIterator first,
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InputIterator last,
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command_queue &queue)
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{
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typedef typename std::iterator_traits<InputIterator>::value_type input_type;
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const device &device = queue.get_device();
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// device must have dedicated local memory storage
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// otherwise reduction would be highly inefficient
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if(device.get_info<CL_DEVICE_LOCAL_MEM_TYPE>() != CL_LOCAL)
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{
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return false;
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}
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const size_t max_work_group_size = device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>();
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// local memory size in bytes (per compute unit)
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const size_t local_mem_size = device.get_info<CL_DEVICE_LOCAL_MEM_SIZE>();
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std::string cache_key = std::string("__boost_find_extrema_reduce_")
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+ type_name<input_type>();
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// load parameters
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boost::shared_ptr<parameter_cache> parameters =
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detail::parameter_cache::get_global_cache(device);
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// Get preferred work group size
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size_t work_group_size = parameters->get(cache_key, "wgsize", 256);
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work_group_size = (std::min)(max_work_group_size, work_group_size);
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// local memory size needed to perform parallel reduction
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size_t required_local_mem_size = 0;
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// indices size
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required_local_mem_size += sizeof(uint_) * work_group_size;
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// values size
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required_local_mem_size += sizeof(input_type) * work_group_size;
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// at least 4 work groups per compute unit otherwise reduction
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// would be highly inefficient
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return ((required_local_mem_size * 4) <= local_mem_size);
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}
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/// \internal_
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/// Algorithm finds the first extremum in given range, i.e., with the lowest
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/// index.
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///
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/// If \p use_input_idx is false, it's assumed that input data is ordered by
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/// increasing index and \p input_idx is not used in the algorithm.
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template<class InputIterator, class ResultIterator, class Compare>
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inline void find_extrema_with_reduce(InputIterator input,
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vector<uint_>::iterator input_idx,
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size_t count,
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ResultIterator result,
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vector<uint_>::iterator result_idx,
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size_t work_groups_no,
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size_t work_group_size,
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Compare compare,
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const bool find_minimum,
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const bool use_input_idx,
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command_queue &queue)
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{
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typedef typename std::iterator_traits<InputIterator>::value_type input_type;
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const context &context = queue.get_context();
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meta_kernel k("find_extrema_reduce");
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size_t count_arg = k.add_arg<uint_>("count");
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size_t block_arg = k.add_arg<input_type *>(memory_object::local_memory, "block");
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size_t block_idx_arg = k.add_arg<uint_ *>(memory_object::local_memory, "block_idx");
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k <<
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// Work item global id
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k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
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// Index of element that will be read from input buffer
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k.decl<uint_>("idx") << " = gid;\n" <<
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k.decl<input_type>("acc") << ";\n" <<
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k.decl<uint_>("acc_idx") << ";\n" <<
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"if(gid < count) {\n" <<
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// Real index of currently best element
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"#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
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k.var<uint_>("acc_idx") << " = " << input_idx[k.var<uint_>("idx")] << ";\n" <<
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"#else\n" <<
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k.var<uint_>("acc_idx") << " = idx;\n" <<
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"#endif\n" <<
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// Init accumulator with first[get_global_id(0)]
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"acc = " << input[k.var<uint_>("idx")] << ";\n" <<
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"idx += get_global_size(0);\n" <<
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"}\n" <<
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k.decl<bool>("compare_result") << ";\n" <<
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k.decl<bool>("equal") << ";\n\n" <<
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"while( idx < count ){\n" <<
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// Next element
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k.decl<input_type>("next") << " = " << input[k.var<uint_>("idx")] << ";\n" <<
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"#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
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k.decl<uint_>("next_idx") << " = " << input_idx[k.var<uint_>("idx")] << ";\n" <<
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"#endif\n" <<
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// Comparison between currently best element (acc) and next element
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"#ifdef BOOST_COMPUTE_FIND_MAXIMUM\n" <<
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"compare_result = " << compare(k.var<input_type>("next"),
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k.var<input_type>("acc")) << ";\n" <<
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"# ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
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"equal = !compare_result && !" <<
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compare(k.var<input_type>("acc"),
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k.var<input_type>("next")) << ";\n" <<
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"# endif\n" <<
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"#else\n" <<
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"compare_result = " << compare(k.var<input_type>("acc"),
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k.var<input_type>("next")) << ";\n" <<
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"# ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
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"equal = !compare_result && !" <<
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compare(k.var<input_type>("next"),
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k.var<input_type>("acc")) << ";\n" <<
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"# endif\n" <<
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"#endif\n" <<
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// save the winner
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"acc = compare_result ? acc : next;\n" <<
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"#ifdef BOOST_COMPUTE_USE_INPUT_IDX\n" <<
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"acc_idx = compare_result ? " <<
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"acc_idx : " <<
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"(equal ? min(acc_idx, next_idx) : next_idx);\n" <<
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"#else\n" <<
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"acc_idx = compare_result ? acc_idx : idx;\n" <<
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"#endif\n" <<
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"idx += get_global_size(0);\n" <<
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"}\n\n" <<
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// Work item local id
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k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
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"block[lid] = acc;\n" <<
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"block_idx[lid] = acc_idx;\n" <<
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"barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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k.decl<uint_>("group_offset") <<
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" = count - (get_local_size(0) * get_group_id(0));\n\n";
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k <<
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"#pragma unroll\n"
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"for(" << k.decl<uint_>("offset") << " = " << uint_(work_group_size) << " / 2; offset > 0; " <<
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"offset = offset / 2) {\n" <<
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"if((lid < offset) && ((lid + offset) < group_offset)) { \n" <<
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k.decl<input_type>("mine") << " = block[lid];\n" <<
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k.decl<input_type>("other") << " = block[lid+offset];\n" <<
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"#ifdef BOOST_COMPUTE_FIND_MAXIMUM\n" <<
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"compare_result = " << compare(k.var<input_type>("other"),
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k.var<input_type>("mine")) << ";\n" <<
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"equal = !compare_result && !" <<
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compare(k.var<input_type>("mine"),
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k.var<input_type>("other")) << ";\n" <<
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"#else\n" <<
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"compare_result = " << compare(k.var<input_type>("mine"),
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k.var<input_type>("other")) << ";\n" <<
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"equal = !compare_result && !" <<
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compare(k.var<input_type>("other"),
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k.var<input_type>("mine")) << ";\n" <<
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"#endif\n" <<
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"block[lid] = compare_result ? mine : other;\n" <<
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k.decl<uint_>("mine_idx") << " = block_idx[lid];\n" <<
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k.decl<uint_>("other_idx") << " = block_idx[lid+offset];\n" <<
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"block_idx[lid] = compare_result ? " <<
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"mine_idx : " <<
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"(equal ? min(mine_idx, other_idx) : other_idx);\n" <<
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"}\n"
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"barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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"}\n\n" <<
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// write block result to global output
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"if(lid == 0){\n" <<
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result[k.var<uint_>("get_group_id(0)")] << " = block[0];\n" <<
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result_idx[k.var<uint_>("get_group_id(0)")] << " = block_idx[0];\n" <<
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"}";
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std::string options;
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if(!find_minimum){
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options = "-DBOOST_COMPUTE_FIND_MAXIMUM";
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}
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if(use_input_idx){
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options += " -DBOOST_COMPUTE_USE_INPUT_IDX";
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}
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kernel kernel = k.compile(context, options);
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kernel.set_arg(count_arg, static_cast<uint_>(count));
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kernel.set_arg(block_arg, local_buffer<input_type>(work_group_size));
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kernel.set_arg(block_idx_arg, local_buffer<uint_>(work_group_size));
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queue.enqueue_1d_range_kernel(kernel,
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0,
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work_groups_no * work_group_size,
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work_group_size);
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}
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template<class InputIterator, class ResultIterator, class Compare>
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inline void find_extrema_with_reduce(InputIterator input,
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size_t count,
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ResultIterator result,
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vector<uint_>::iterator result_idx,
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size_t work_groups_no,
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size_t work_group_size,
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Compare compare,
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const bool find_minimum,
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command_queue &queue)
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{
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// dummy will not be used
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buffer_iterator<uint_> dummy = result_idx;
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return find_extrema_with_reduce(
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input, dummy, count, result, result_idx, work_groups_no,
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work_group_size, compare, find_minimum, false, queue
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);
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}
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template<class InputIterator, class Compare>
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InputIterator find_extrema_with_reduce(InputIterator first,
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InputIterator last,
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Compare compare,
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const bool find_minimum,
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command_queue &queue)
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{
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typedef typename std::iterator_traits<InputIterator>::difference_type difference_type;
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typedef typename std::iterator_traits<InputIterator>::value_type input_type;
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const context &context = queue.get_context();
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const device &device = queue.get_device();
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// Getting information about used queue and device
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const size_t compute_units_no = device.get_info<CL_DEVICE_MAX_COMPUTE_UNITS>();
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const size_t max_work_group_size = device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>();
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const size_t count = detail::iterator_range_size(first, last);
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std::string cache_key = std::string("__boost_find_extrema_with_reduce_")
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+ type_name<input_type>();
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// load parameters
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boost::shared_ptr<parameter_cache> parameters =
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detail::parameter_cache::get_global_cache(device);
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// get preferred work group size and preferred number
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// of work groups per compute unit
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size_t work_group_size = parameters->get(cache_key, "wgsize", 256);
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size_t work_groups_per_cu = parameters->get(cache_key, "wgpcu", 100);
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// calculate work group size and number of work groups
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work_group_size = (std::min)(max_work_group_size, work_group_size);
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size_t work_groups_no = compute_units_no * work_groups_per_cu;
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work_groups_no = (std::min)(
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work_groups_no,
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static_cast<size_t>(std::ceil(float(count) / work_group_size))
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);
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// phase I: finding candidates for extremum
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// device buffors for extremum candidates and their indices
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// each work-group computes its candidate
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vector<input_type> candidates(work_groups_no, context);
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vector<uint_> candidates_idx(work_groups_no, context);
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// finding candidates for first extremum and their indices
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find_extrema_with_reduce(
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first, count, candidates.begin(), candidates_idx.begin(),
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work_groups_no, work_group_size, compare, find_minimum, queue
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);
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// phase II: finding extremum from among the candidates
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// zero-copy buffers for final result (value and index)
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vector<input_type, ::boost::compute::pinned_allocator<input_type> >
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result(1, context);
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vector<uint_, ::boost::compute::pinned_allocator<uint_> >
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result_idx(1, context);
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// get extremum from among the candidates
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find_extrema_with_reduce(
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candidates.begin(), candidates_idx.begin(), work_groups_no, result.begin(),
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result_idx.begin(), 1, work_group_size, compare, find_minimum, true, queue
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);
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// mapping extremum index to host
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uint_* result_idx_host_ptr =
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static_cast<uint_*>(
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queue.enqueue_map_buffer(
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result_idx.get_buffer(), command_queue::map_read,
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0, sizeof(uint_)
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)
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);
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return first + static_cast<difference_type>(*result_idx_host_ptr);
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}
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template<class InputIterator>
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InputIterator find_extrema_with_reduce(InputIterator first,
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InputIterator last,
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::boost::compute::less<
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typename std::iterator_traits<
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InputIterator
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>::value_type
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>
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compare,
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const bool find_minimum,
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|
command_queue &queue)
|
||
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|
{
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||
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|
typedef typename std::iterator_traits<InputIterator>::difference_type difference_type;
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||
|
|
typedef typename std::iterator_traits<InputIterator>::value_type input_type;
|
||
|
|
|
||
|
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const context &context = queue.get_context();
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|
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const device &device = queue.get_device();
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|
|
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||
|
|
// Getting information about used queue and device
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||
|
|
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>();
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||
|
|
|
||
|
|
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
|