208 lines
		
	
	
		
			7.1 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
			
		
		
	
	
			208 lines
		
	
	
		
			7.1 KiB
		
	
	
	
		
			Plaintext
		
	
	
	
	
	
//---------------------------------------------------------------------------//
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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_SCAN_ON_CPU_HPP
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#define BOOST_COMPUTE_ALGORITHM_DETAIL_SCAN_ON_CPU_HPP
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#include <iterator>
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#include <boost/compute/device.hpp>
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#include <boost/compute/kernel.hpp>
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#include <boost/compute/command_queue.hpp>
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#include <boost/compute/algorithm/detail/serial_scan.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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namespace boost {
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namespace compute {
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namespace detail {
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template<class InputIterator, class OutputIterator, class T, class BinaryOperator>
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inline OutputIterator scan_on_cpu(InputIterator first,
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                                  InputIterator last,
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                                  OutputIterator result,
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                                  bool exclusive,
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                                  T init,
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                                  BinaryOperator op,
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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 input_type;
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    typedef typename
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        std::iterator_traits<OutputIterator>::value_type output_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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    const size_t 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_scan_cpu_" + boost::lexical_cast<std::string>(sizeof(T));
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    // for inputs smaller than serial_scan_threshold
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    // serial_scan algorithm is used
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    uint_ serial_scan_threshold =
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        parameters->get(cache_key, "serial_scan_threshold", 16384 * sizeof(T));
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    serial_scan_threshold =
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        (std::max)(serial_scan_threshold, uint_(compute_units));
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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 result;
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    }
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    else if(count < serial_scan_threshold) {
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        return serial_scan(first, last, result, exclusive, init, op, queue);
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    }
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    buffer block_partial_sums(context, sizeof(output_type) * compute_units );
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    // create scan kernel
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    meta_kernel k("scan_on_cpu_block_scan");
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    // Arguments
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    size_t count_arg = k.add_arg<uint_>("count");
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    size_t init_arg = k.add_arg<output_type>("initial_value");
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    size_t block_partial_sums_arg =
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        k.add_arg<output_type *>(memory_object::global_memory, "block_partial_sums");
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    k <<
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        "uint block = " <<
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            "(uint)ceil(((float)count)/(get_global_size(0) + 1));\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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    if(!exclusive){
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        k <<
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            k.decl<output_type>("sum") << " = " <<
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                first[k.var<uint_>("index")] << ";\n" <<
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            result[k.var<uint_>("index")] << " = sum;\n" <<
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            "index++;\n";
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    }
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    else {
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        k <<
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            k.decl<output_type>("sum") << ";\n" <<
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            "if(index == 0){\n" <<
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                "sum = initial_value;\n" <<
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            "}\n" <<
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            "else {\n" <<
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                "sum = " << first[k.var<uint_>("index")] << ";\n" <<
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                "index++;\n" <<
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            "}\n";
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    }
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    k <<
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        "while(index < end){\n" <<
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            // load next value
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            k.decl<const input_type>("value") << " = "
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                << first[k.var<uint_>("index")] << ";\n";
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    if(exclusive){
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        k <<
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            "if(get_global_id(0) == 0){\n" <<
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                result[k.var<uint_>("index")] << " = sum;\n" <<
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            "}\n";
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    }
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    k <<
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            "sum = " << op(k.var<output_type>("sum"),
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                           k.var<output_type>("value")) << ";\n";
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    if(!exclusive){
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        k <<
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            "if(get_global_id(0) == 0){\n" <<
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                result[k.var<uint_>("index")] << " = sum;\n" <<
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            "}\n";
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    }
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    k <<
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            "index++;\n" <<
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        "}\n" << // end while
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        "block_partial_sums[get_global_id(0)] = sum;\n";
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    // compile scan kernel
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    kernel block_scan_kernel = k.compile(context);
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    // setup kernel arguments
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    block_scan_kernel.set_arg(count_arg, static_cast<uint_>(count));
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    block_scan_kernel.set_arg(init_arg, static_cast<output_type>(init));
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    block_scan_kernel.set_arg(block_partial_sums_arg, block_partial_sums);
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    // execute the kernel
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    size_t global_work_size = compute_units;
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    queue.enqueue_1d_range_kernel(block_scan_kernel, 0, global_work_size, 0);
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    // scan is done
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    if(compute_units < 2) {
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        return result + count;
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    }
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    // final scan kernel
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    meta_kernel l("scan_on_cpu_final_scan");
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    // Arguments
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    count_arg = l.add_arg<uint_>("count");
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    block_partial_sums_arg =
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        l.add_arg<output_type *>(memory_object::global_memory, "block_partial_sums");
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    l <<
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        "uint block = " <<
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            "(uint)ceil(((float)count)/(get_global_size(0) + 1));\n" <<
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        "uint index = block + get_global_id(0) * block;\n" <<
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        "uint end = min(count, index + block);\n" <<
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        k.decl<output_type>("sum") << " = block_partial_sums[0];\n" <<
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        "for(uint i = 0; i < get_global_id(0); i++) {\n" <<
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            "sum = " << op(k.var<output_type>("sum"),
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                           k.var<output_type>("block_partial_sums[i + 1]")) << ";\n" <<
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        "}\n" <<
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        "while(index < end){\n";
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    if(exclusive){
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        l <<
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            l.decl<output_type>("value") << " = "
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                << first[k.var<uint_>("index")] << ";\n" <<
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            result[k.var<uint_>("index")] << " = sum;\n" <<
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            "sum = " << op(k.var<output_type>("sum"),
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                           k.var<output_type>("value")) << ";\n";
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    }
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    else {
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        l <<
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            "sum = " << op(k.var<output_type>("sum"),
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                           first[k.var<uint_>("index")]) << ";\n" <<
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            result[k.var<uint_>("index")] << " = sum;\n";
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    }
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    l <<
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            "index++;\n" <<
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        "}\n";
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    // compile scan kernel
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    kernel final_scan_kernel = l.compile(context);
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    // setup kernel arguments
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    final_scan_kernel.set_arg(count_arg, static_cast<uint_>(count));
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    final_scan_kernel.set_arg(block_partial_sums_arg, block_partial_sums);
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    // execute the kernel
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    global_work_size = compute_units;
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    queue.enqueue_1d_range_kernel(final_scan_kernel, 0, global_work_size, 0);
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    // return iterator pointing to the end of the result range
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    return result + count;
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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_SCAN_ON_CPU_HPP
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