//---------------------------------------------------------------------------//
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// Copyright (c) 2016 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_ON_CPU_HPP
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#define BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_ON_CPU_HPP
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#include <algorithm>
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#include <boost/compute/buffer.hpp>
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#include <boost/compute/command_queue.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/iterator/buffer_iterator.hpp>
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#include <boost/compute/type_traits/result_of.hpp>
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#include <boost/compute/algorithm/detail/serial_reduce.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, class OutputIterator, class BinaryFunction>
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inline void reduce_on_cpu(InputIterator first,
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InputIterator last,
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OutputIterator result,
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BinaryFunction function,
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command_queue &queue)
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{
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typedef typename
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std::iterator_traits<InputIterator>::value_type T;
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typedef typename
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::boost::compute::result_of<BinaryFunction(T, T)>::type result_type;
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const device &device = queue.get_device();
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const uint_ compute_units = queue.get_device().compute_units();
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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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std::string cache_key =
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"__boost_reduce_cpu_" + boost::lexical_cast<std::string>(sizeof(T));
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// for inputs smaller than serial_reduce_threshold
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// serial_reduce algorithm is used
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uint_ serial_reduce_threshold =
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parameters->get(cache_key, "serial_reduce_threshold", 16384 * sizeof(T));
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serial_reduce_threshold =
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(std::max)(serial_reduce_threshold, uint_(compute_units));
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const context &context = queue.get_context();
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size_t count = detail::iterator_range_size(first, last);
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if(count == 0){
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return;
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}
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else if(count < serial_reduce_threshold) {
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return serial_reduce(first, last, result, function, queue);
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}
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meta_kernel k("reduce_on_cpu");
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buffer output(context, sizeof(result_type) * compute_units);
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size_t count_arg = k.add_arg<uint_>("count");
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size_t output_arg =
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k.add_arg<result_type *>(memory_object::global_memory, "output");
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k <<
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"uint block = " <<
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"(uint)ceil(((float)count)/get_global_size(0));\n" <<
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"uint index = get_global_id(0) * block;\n" <<
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"uint end = min(count, index + block);\n" <<
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k.decl<result_type>("result") << " = " << first[k.var<uint_>("index")] << ";\n" <<
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"index++;\n" <<
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"while(index < end){\n" <<
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"result = " << function(k.var<T>("result"),
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first[k.var<uint_>("index")]) << ";\n" <<
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"index++;\n" <<
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"}\n" <<
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"output[get_global_id(0)] = result;\n";
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size_t global_work_size = compute_units;
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kernel kernel = k.compile(context);
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// reduction to global_work_size elements
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kernel.set_arg(count_arg, static_cast<uint_>(count));
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kernel.set_arg(output_arg, output);
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queue.enqueue_1d_range_kernel(kernel, 0, global_work_size, 0);
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// final reduction
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reduce_on_cpu(
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make_buffer_iterator<result_type>(output),
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make_buffer_iterator<result_type>(output, global_work_size),
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result,
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function,
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queue
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);
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}
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} // end detail namespace
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} // end compute namespace
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} // end boost namespace
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#endif // BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_ON_CPU_HPP
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