542 lines
24 KiB
Plaintext
542 lines
24 KiB
Plaintext
//---------------------------------------------------------------------------//
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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_REDUCE_BY_KEY_WITH_SCAN_HPP
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#define BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_BY_KEY_WITH_SCAN_HPP
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#include <algorithm>
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#include <iterator>
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#include <boost/compute/command_queue.hpp>
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#include <boost/compute/functional.hpp>
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#include <boost/compute/algorithm/inclusive_scan.hpp>
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#include <boost/compute/container/vector.hpp>
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#include <boost/compute/container/detail/scalar.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/read_write_single_value.hpp>
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#include <boost/compute/type_traits.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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/// \internal_
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///
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/// Fills \p new_keys_first with unsigned integer keys generated from vector
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/// of original keys \p keys_first. New keys can be distinguish by simple equality
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/// predicate.
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///
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/// \param keys_first iterator pointing to the first key
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/// \param number_of_keys number of keys
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/// \param predicate binary predicate for key comparison
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/// \param new_keys_first iterator pointing to the new keys vector
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/// \param preferred_work_group_size preferred work group size
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/// \param queue command queue to perform the operation
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///
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/// Binary function \p predicate must take two keys as arguments and
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/// return true only if they are considered the same.
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///
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/// The first new key equals zero and the last equals number of unique keys
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/// minus one.
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///
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/// No local memory usage.
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template<class InputKeyIterator, class BinaryPredicate>
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inline void generate_uint_keys(InputKeyIterator keys_first,
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size_t number_of_keys,
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BinaryPredicate predicate,
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vector<uint_>::iterator new_keys_first,
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size_t preferred_work_group_size,
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command_queue &queue)
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{
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typedef typename
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std::iterator_traits<InputKeyIterator>::value_type key_type;
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detail::meta_kernel k("reduce_by_key_new_key_flags");
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k.add_set_arg<const uint_>("count", uint_(number_of_keys));
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k <<
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k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
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k.decl<uint_>("value") << " = 0;\n" <<
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"if(gid >= count){\n return;\n}\n" <<
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"if(gid > 0){ \n" <<
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k.decl<key_type>("key") << " = " <<
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keys_first[k.var<const uint_>("gid")] << ";\n" <<
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k.decl<key_type>("previous_key") << " = " <<
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keys_first[k.var<const uint_>("gid - 1")] << ";\n" <<
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" value = " << predicate(k.var<key_type>("previous_key"),
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k.var<key_type>("key")) <<
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" ? 0 : 1;\n" <<
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"}\n else {\n" <<
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" value = 0;\n" <<
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"}\n" <<
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new_keys_first[k.var<const uint_>("gid")] << " = value;\n";
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const context &context = queue.get_context();
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kernel kernel = k.compile(context);
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size_t work_group_size = preferred_work_group_size;
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size_t work_groups_no = static_cast<size_t>(
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std::ceil(float(number_of_keys) / work_group_size)
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);
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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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inclusive_scan(new_keys_first, new_keys_first + number_of_keys,
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new_keys_first, queue);
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}
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/// \internal_
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/// Calculate carry-out for each work group.
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/// Carry-out is a pair of the last key processed by a work group and sum of all
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/// values under this key in this work group.
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template<class InputValueIterator, class OutputValueIterator, class BinaryFunction>
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inline void carry_outs(vector<uint_>::iterator keys_first,
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InputValueIterator values_first,
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size_t count,
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vector<uint_>::iterator carry_out_keys_first,
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OutputValueIterator carry_out_values_first,
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BinaryFunction function,
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size_t work_group_size,
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command_queue &queue)
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{
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typedef typename
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std::iterator_traits<OutputValueIterator>::value_type value_out_type;
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detail::meta_kernel k("reduce_by_key_with_scan_carry_outs");
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k.add_set_arg<const uint_>("count", uint_(count));
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size_t local_keys_arg = k.add_arg<uint_ *>(memory_object::local_memory, "lkeys");
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size_t local_vals_arg = k.add_arg<value_out_type *>(memory_object::local_memory, "lvals");
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k <<
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k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
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k.decl<const uint_>("wg_size") << " = get_local_size(0);\n" <<
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k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
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k.decl<const uint_>("group_id") << " = get_group_id(0);\n" <<
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k.decl<uint_>("key") << ";\n" <<
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k.decl<value_out_type>("value") << ";\n" <<
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"if(gid < count){\n" <<
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k.var<uint_>("key") << " = " <<
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keys_first[k.var<const uint_>("gid")] << ";\n" <<
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k.var<value_out_type>("value") << " = " <<
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values_first[k.var<const uint_>("gid")] << ";\n" <<
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"lkeys[lid] = key;\n" <<
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"lvals[lid] = value;\n" <<
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"}\n" <<
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// Calculate carry out for each work group by performing Hillis/Steele scan
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// where only last element (key-value pair) is saved
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k.decl<value_out_type>("result") << " = value;\n" <<
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k.decl<uint_>("other_key") << ";\n" <<
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k.decl<value_out_type>("other_value") << ";\n" <<
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"for(" << k.decl<uint_>("offset") << " = 1; " <<
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"offset < wg_size; offset *= 2){\n"
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" barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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" if(lid >= offset){\n"
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" other_key = lkeys[lid - offset];\n" <<
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" if(other_key == key){\n" <<
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" other_value = lvals[lid - offset];\n" <<
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" result = " << function(k.var<value_out_type>("result"),
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k.var<value_out_type>("other_value")) << ";\n" <<
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" }\n" <<
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" }\n" <<
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" barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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" lvals[lid] = result;\n" <<
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"}\n" <<
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// save carry out
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"if(lid == (wg_size - 1)){\n" <<
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carry_out_keys_first[k.var<const uint_>("group_id")] << " = key;\n" <<
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carry_out_values_first[k.var<const uint_>("group_id")] << " = result;\n" <<
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"}\n";
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size_t work_groups_no = static_cast<size_t>(
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std::ceil(float(count) / work_group_size)
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);
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const context &context = queue.get_context();
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kernel kernel = k.compile(context);
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kernel.set_arg(local_keys_arg, local_buffer<uint_>(work_group_size));
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kernel.set_arg(local_vals_arg, local_buffer<value_out_type>(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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/// \internal_
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/// Calculate carry-in by performing inclusive scan by key on carry-outs vector.
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template<class OutputValueIterator, class BinaryFunction>
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inline void carry_ins(vector<uint_>::iterator carry_out_keys_first,
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OutputValueIterator carry_out_values_first,
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OutputValueIterator carry_in_values_first,
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size_t carry_out_size,
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BinaryFunction function,
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size_t work_group_size,
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command_queue &queue)
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{
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typedef typename
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std::iterator_traits<OutputValueIterator>::value_type value_out_type;
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uint_ values_pre_work_item = static_cast<uint_>(
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std::ceil(float(carry_out_size) / work_group_size)
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);
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detail::meta_kernel k("reduce_by_key_with_scan_carry_ins");
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k.add_set_arg<const uint_>("carry_out_size", uint_(carry_out_size));
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k.add_set_arg<const uint_>("values_per_work_item", values_pre_work_item);
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size_t local_keys_arg = k.add_arg<uint_ *>(memory_object::local_memory, "lkeys");
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size_t local_vals_arg = k.add_arg<value_out_type *>(memory_object::local_memory, "lvals");
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k <<
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k.decl<uint_>("id") << " = get_global_id(0) * values_per_work_item;\n" <<
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k.decl<uint_>("idx") << " = id;\n" <<
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k.decl<const uint_>("wg_size") << " = get_local_size(0);\n" <<
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k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
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k.decl<const uint_>("group_id") << " = get_group_id(0);\n" <<
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k.decl<uint_>("key") << ";\n" <<
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k.decl<value_out_type>("value") << ";\n" <<
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k.decl<uint_>("previous_key") << ";\n" <<
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k.decl<value_out_type>("result") << ";\n" <<
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"if(id < carry_out_size){\n" <<
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k.var<uint_>("previous_key") << " = " <<
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carry_out_keys_first[k.var<const uint_>("id")] << ";\n" <<
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k.var<value_out_type>("result") << " = " <<
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carry_out_values_first[k.var<const uint_>("id")] << ";\n" <<
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carry_in_values_first[k.var<const uint_>("id")] << " = result;\n" <<
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"}\n" <<
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k.decl<const uint_>("end") << " = (id + values_per_work_item) <= carry_out_size" <<
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" ? (values_per_work_item + id) : carry_out_size;\n" <<
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"for(idx = idx + 1; idx < end; idx += 1){\n" <<
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" key = " << carry_out_keys_first[k.var<const uint_>("idx")] << ";\n" <<
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" value = " << carry_out_values_first[k.var<const uint_>("idx")] << ";\n" <<
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" if(previous_key == key){\n" <<
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" result = " << function(k.var<value_out_type>("result"),
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k.var<value_out_type>("value")) << ";\n" <<
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" }\n else { \n" <<
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" result = value;\n"
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" }\n" <<
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" " << carry_in_values_first[k.var<const uint_>("idx")] << " = result;\n" <<
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" previous_key = key;\n"
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"}\n" <<
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// save the last key and result to local memory
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"lkeys[lid] = previous_key;\n" <<
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"lvals[lid] = result;\n" <<
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// Hillis/Steele scan
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"for(" << k.decl<uint_>("offset") << " = 1; " <<
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"offset < wg_size; offset *= 2){\n"
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" barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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" if(lid >= offset){\n"
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" key = lkeys[lid - offset];\n" <<
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" if(previous_key == key){\n" <<
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" value = lvals[lid - offset];\n" <<
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" result = " << function(k.var<value_out_type>("result"),
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k.var<value_out_type>("value")) << ";\n" <<
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" }\n" <<
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" }\n" <<
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" barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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" lvals[lid] = result;\n" <<
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"}\n" <<
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"barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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"if(lid > 0){\n" <<
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// load key-value reduced by previous work item
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" previous_key = lkeys[lid - 1];\n" <<
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" result = lvals[lid - 1];\n" <<
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"}\n" <<
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// add key-value reduced by previous work item
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"for(idx = id; idx < id + values_per_work_item; idx += 1){\n" <<
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// make sure all carry-ins are saved in global memory
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" barrier( CLK_GLOBAL_MEM_FENCE );\n" <<
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" if(lid > 0 && idx < carry_out_size) {\n"
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" key = " << carry_out_keys_first[k.var<const uint_>("idx")] << ";\n" <<
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" value = " << carry_in_values_first[k.var<const uint_>("idx")] << ";\n" <<
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" if(previous_key == key){\n" <<
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" value = " << function(k.var<value_out_type>("result"),
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k.var<value_out_type>("value")) << ";\n" <<
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" }\n" <<
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" " << carry_in_values_first[k.var<const uint_>("idx")] << " = value;\n" <<
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" }\n" <<
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"}\n";
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const context &context = queue.get_context();
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kernel kernel = k.compile(context);
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kernel.set_arg(local_keys_arg, local_buffer<uint_>(work_group_size));
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kernel.set_arg(local_vals_arg, local_buffer<value_out_type>(work_group_size));
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queue.enqueue_1d_range_kernel(kernel,
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0,
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work_group_size,
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work_group_size);
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}
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/// \internal_
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///
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/// Perform final reduction by key. Each work item:
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/// 1. Perform local work-group reduction (Hillis/Steele scan)
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/// 2. Add carry-in (if keys are right)
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/// 3. Save reduced value if next key is different than processed one
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template<class InputKeyIterator, class InputValueIterator,
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class OutputKeyIterator, class OutputValueIterator,
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class BinaryFunction>
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inline void final_reduction(InputKeyIterator keys_first,
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InputValueIterator values_first,
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OutputKeyIterator keys_result,
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OutputValueIterator values_result,
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size_t count,
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BinaryFunction function,
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vector<uint_>::iterator new_keys_first,
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vector<uint_>::iterator carry_in_keys_first,
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OutputValueIterator carry_in_values_first,
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size_t carry_in_size,
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size_t work_group_size,
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command_queue &queue)
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{
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typedef typename
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std::iterator_traits<OutputValueIterator>::value_type value_out_type;
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detail::meta_kernel k("reduce_by_key_with_scan_final_reduction");
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k.add_set_arg<const uint_>("count", uint_(count));
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size_t local_keys_arg = k.add_arg<uint_ *>(memory_object::local_memory, "lkeys");
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size_t local_vals_arg = k.add_arg<value_out_type *>(memory_object::local_memory, "lvals");
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k <<
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k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
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k.decl<const uint_>("wg_size") << " = get_local_size(0);\n" <<
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k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
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k.decl<const uint_>("group_id") << " = get_group_id(0);\n" <<
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k.decl<uint_>("key") << ";\n" <<
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k.decl<value_out_type>("value") << ";\n"
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"if(gid < count){\n" <<
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k.var<uint_>("key") << " = " <<
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new_keys_first[k.var<const uint_>("gid")] << ";\n" <<
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k.var<value_out_type>("value") << " = " <<
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values_first[k.var<const uint_>("gid")] << ";\n" <<
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"lkeys[lid] = key;\n" <<
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"lvals[lid] = value;\n" <<
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"}\n" <<
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// Hillis/Steele scan
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k.decl<value_out_type>("result") << " = value;\n" <<
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k.decl<uint_>("other_key") << ";\n" <<
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k.decl<value_out_type>("other_value") << ";\n" <<
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"for(" << k.decl<uint_>("offset") << " = 1; " <<
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"offset < wg_size ; offset *= 2){\n"
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" barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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" if(lid >= offset) {\n" <<
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" other_key = lkeys[lid - offset];\n" <<
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" if(other_key == key){\n" <<
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" other_value = lvals[lid - offset];\n" <<
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" result = " << function(k.var<value_out_type>("result"),
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k.var<value_out_type>("other_value")) << ";\n" <<
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" }\n" <<
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" }\n" <<
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" barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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" lvals[lid] = result;\n" <<
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"}\n" <<
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"if(gid >= count) {\n return;\n};\n" <<
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k.decl<const bool>("save") << " = (gid < (count - 1)) ?"
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<< new_keys_first[k.var<const uint_>("gid + 1")] << " != key" <<
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": true;\n" <<
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// Add carry in
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k.decl<uint_>("carry_in_key") << ";\n" <<
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"if(group_id > 0 && save) {\n" <<
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" carry_in_key = " << carry_in_keys_first[k.var<const uint_>("group_id - 1")] << ";\n" <<
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" if(key == carry_in_key){\n" <<
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" other_value = " << carry_in_values_first[k.var<const uint_>("group_id - 1")] << ";\n" <<
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" result = " << function(k.var<value_out_type>("result"),
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k.var<value_out_type>("other_value")) << ";\n" <<
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" }\n" <<
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"}\n" <<
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// Save result only if the next key is different or it's the last element.
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"if(save){\n" <<
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keys_result[k.var<uint_>("key")] << " = " << keys_first[k.var<const uint_>("gid")] << ";\n" <<
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values_result[k.var<uint_>("key")] << " = result;\n" <<
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"}\n"
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;
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size_t work_groups_no = static_cast<size_t>(
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std::ceil(float(count) / work_group_size)
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);
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const context &context = queue.get_context();
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kernel kernel = k.compile(context);
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kernel.set_arg(local_keys_arg, local_buffer<uint_>(work_group_size));
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kernel.set_arg(local_vals_arg, local_buffer<value_out_type>(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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/// \internal_
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/// Returns preferred work group size for reduce by key with scan algorithm.
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template<class KeyType, class ValueType>
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inline size_t get_work_group_size(const device& device)
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{
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std::string cache_key = std::string("__boost_reduce_by_key_with_scan")
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+ "k_" + type_name<KeyType>() + "_v_" + type_name<ValueType>();
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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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return (std::max)(
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static_cast<size_t>(parameters->get(cache_key, "wgsize", 256)),
|
|
static_cast<size_t>(device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>())
|
|
);
|
|
}
|
|
|
|
/// \internal_
|
|
///
|
|
/// 1. For each work group carry-out value is calculated (it's done by key-oriented
|
|
/// Hillis/Steele scan). Carry-out is a pair of the last key processed by work
|
|
/// group and sum of all values under this key in work group.
|
|
/// 2. From every carry-out carry-in is calculated by performing inclusive scan
|
|
/// by key.
|
|
/// 3. Final reduction by key is performed (key-oriented Hillis/Steele scan),
|
|
/// carry-in values are added where needed.
|
|
template<class InputKeyIterator, class InputValueIterator,
|
|
class OutputKeyIterator, class OutputValueIterator,
|
|
class BinaryFunction, class BinaryPredicate>
|
|
inline size_t reduce_by_key_with_scan(InputKeyIterator keys_first,
|
|
InputKeyIterator keys_last,
|
|
InputValueIterator values_first,
|
|
OutputKeyIterator keys_result,
|
|
OutputValueIterator values_result,
|
|
BinaryFunction function,
|
|
BinaryPredicate predicate,
|
|
command_queue &queue)
|
|
{
|
|
typedef typename
|
|
std::iterator_traits<InputValueIterator>::value_type value_type;
|
|
typedef typename
|
|
std::iterator_traits<InputKeyIterator>::value_type key_type;
|
|
typedef typename
|
|
std::iterator_traits<OutputValueIterator>::value_type value_out_type;
|
|
|
|
const context &context = queue.get_context();
|
|
size_t count = detail::iterator_range_size(keys_first, keys_last);
|
|
|
|
if(count == 0){
|
|
return size_t(0);
|
|
}
|
|
|
|
const device &device = queue.get_device();
|
|
size_t work_group_size = get_work_group_size<value_type, key_type>(device);
|
|
|
|
// Replace original key with unsigned integer keys generated based on given
|
|
// predicate. New key is also an index for keys_result and values_result vectors,
|
|
// which points to place where reduced value should be saved.
|
|
vector<uint_> new_keys(count, context);
|
|
vector<uint_>::iterator new_keys_first = new_keys.begin();
|
|
generate_uint_keys(keys_first, count, predicate, new_keys_first,
|
|
work_group_size, queue);
|
|
|
|
// Calculate carry-out and carry-in vectors size
|
|
const size_t carry_out_size = static_cast<size_t>(
|
|
std::ceil(float(count) / work_group_size)
|
|
);
|
|
vector<uint_> carry_out_keys(carry_out_size, context);
|
|
vector<value_out_type> carry_out_values(carry_out_size, context);
|
|
carry_outs(new_keys_first, values_first, count, carry_out_keys.begin(),
|
|
carry_out_values.begin(), function, work_group_size, queue);
|
|
|
|
vector<value_out_type> carry_in_values(carry_out_size, context);
|
|
carry_ins(carry_out_keys.begin(), carry_out_values.begin(),
|
|
carry_in_values.begin(), carry_out_size, function, work_group_size,
|
|
queue);
|
|
|
|
final_reduction(keys_first, values_first, keys_result, values_result,
|
|
count, function, new_keys_first, carry_out_keys.begin(),
|
|
carry_in_values.begin(), carry_out_size, work_group_size,
|
|
queue);
|
|
|
|
const size_t result = read_single_value<uint_>(new_keys.get_buffer(),
|
|
count - 1, queue);
|
|
return result + 1;
|
|
}
|
|
|
|
/// \internal_
|
|
/// Return true if requirements for running reduce by key with scan on given
|
|
/// device are met (at least one work group of preferred size can be run).
|
|
template<class InputKeyIterator, class InputValueIterator,
|
|
class OutputKeyIterator, class OutputValueIterator>
|
|
bool reduce_by_key_with_scan_requirements_met(InputKeyIterator keys_first,
|
|
InputValueIterator values_first,
|
|
OutputKeyIterator keys_result,
|
|
OutputValueIterator values_result,
|
|
const size_t count,
|
|
command_queue &queue)
|
|
{
|
|
typedef typename
|
|
std::iterator_traits<InputValueIterator>::value_type value_type;
|
|
typedef typename
|
|
std::iterator_traits<InputKeyIterator>::value_type key_type;
|
|
typedef typename
|
|
std::iterator_traits<OutputValueIterator>::value_type value_out_type;
|
|
|
|
(void) keys_first;
|
|
(void) values_first;
|
|
(void) keys_result;
|
|
(void) values_result;
|
|
|
|
const device &device = queue.get_device();
|
|
// device must have dedicated local memory storage
|
|
if(device.get_info<CL_DEVICE_LOCAL_MEM_TYPE>() != CL_LOCAL)
|
|
{
|
|
return false;
|
|
}
|
|
|
|
// local memory size in bytes (per compute unit)
|
|
const size_t local_mem_size = device.get_info<CL_DEVICE_LOCAL_MEM_SIZE>();
|
|
|
|
// preferred work group size
|
|
size_t work_group_size = get_work_group_size<key_type, value_type>(device);
|
|
|
|
// local memory size needed to perform parallel reduction
|
|
size_t required_local_mem_size = 0;
|
|
// keys size
|
|
required_local_mem_size += sizeof(uint_) * work_group_size;
|
|
// reduced values size
|
|
required_local_mem_size += sizeof(value_out_type) * work_group_size;
|
|
|
|
return (required_local_mem_size <= local_mem_size);
|
|
}
|
|
|
|
} // end detail namespace
|
|
} // end compute namespace
|
|
} // end boost namespace
|
|
|
|
#endif // BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_BY_KEY_WITH_SCAN_HPP
|