//---------------------------------------------------------------------------//
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// Copyright (c) 2013 Kyle Lutz <kyle.r.lutz@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_GPU_HPP
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#define BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_ON_GPU_HPP
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#include <iterator>
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#include <boost/compute/utility/source.hpp>
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#include <boost/compute/program.hpp>
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#include <boost/compute/command_queue.hpp>
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#include <boost/compute/detail/vendor.hpp>
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#include <boost/compute/detail/parameter_cache.hpp>
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#include <boost/compute/detail/work_size.hpp>
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#include <boost/compute/detail/meta_kernel.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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/// \internal
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/// body reduction inside a warp
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template<typename T,bool isNvidiaDevice>
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struct ReduceBody
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{
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static std::string body()
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{
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std::stringstream k;
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// local reduction
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k << "for(int i = 1; i < TPB; i <<= 1){\n" <<
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" barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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" uint mask = (i << 1) - 1;\n" <<
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" if((lid & mask) == 0){\n" <<
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" scratch[lid] += scratch[lid+i];\n" <<
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" }\n" <<
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"}\n";
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return k.str();
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}
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};
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/// \internal
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/// body reduction inside a warp
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/// for nvidia device we can use the "unsafe"
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/// memory optimisation
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template<typename T>
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struct ReduceBody<T,true>
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{
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static std::string body()
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{
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std::stringstream k;
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// local reduction
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// we use TPB to compile only useful instruction
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// local reduction when size is greater than warp size
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k << "barrier(CLK_LOCAL_MEM_FENCE);\n" <<
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"if(TPB >= 1024){\n" <<
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"if(lid < 512) { sum += scratch[lid + 512]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);}\n" <<
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"if(TPB >= 512){\n" <<
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"if(lid < 256) { sum += scratch[lid + 256]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);}\n" <<
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"if(TPB >= 256){\n" <<
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"if(lid < 128) { sum += scratch[lid + 128]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);}\n" <<
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"if(TPB >= 128){\n" <<
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"if(lid < 64) { sum += scratch[lid + 64]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);} \n" <<
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// warp reduction
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"if(lid < 32){\n" <<
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// volatile this way we don't need any barrier
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"volatile __local " << type_name<T>() << " *lmem = scratch;\n" <<
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"if(TPB >= 64) { lmem[lid] = sum = sum + lmem[lid+32];} \n" <<
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"if(TPB >= 32) { lmem[lid] = sum = sum + lmem[lid+16];} \n" <<
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"if(TPB >= 16) { lmem[lid] = sum = sum + lmem[lid+ 8];} \n" <<
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"if(TPB >= 8) { lmem[lid] = sum = sum + lmem[lid+ 4];} \n" <<
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"if(TPB >= 4) { lmem[lid] = sum = sum + lmem[lid+ 2];} \n" <<
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"if(TPB >= 2) { lmem[lid] = sum = sum + lmem[lid+ 1];} \n" <<
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"}\n";
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return k.str();
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}
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};
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template<class InputIterator, class Function>
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inline void initial_reduce(InputIterator first,
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InputIterator last,
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buffer result,
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const Function &function,
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kernel &reduce_kernel,
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const uint_ vpt,
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const uint_ tpb,
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command_queue &queue)
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{
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(void) function;
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(void) reduce_kernel;
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typedef typename std::iterator_traits<InputIterator>::value_type Arg;
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typedef typename boost::tr1_result_of<Function(Arg, Arg)>::type T;
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size_t count = std::distance(first, last);
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detail::meta_kernel k("initial_reduce");
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k.add_set_arg<const uint_>("count", uint_(count));
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size_t output_arg = k.add_arg<T *>(memory_object::global_memory, "output");
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k <<
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k.decl<const uint_>("offset") << " = get_group_id(0) * VPT * TPB;\n" <<
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k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
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"__local " << type_name<T>() << " scratch[TPB];\n" <<
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// private reduction
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k.decl<T>("sum") << " = 0;\n" <<
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"for(uint i = 0; i < VPT; i++){\n" <<
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" if(offset + lid + i*TPB < count){\n" <<
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" sum = sum + " << first[k.var<uint_>("offset+lid+i*TPB")] << ";\n" <<
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" }\n" <<
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"}\n" <<
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"scratch[lid] = sum;\n" <<
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// local reduction
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ReduceBody<T,false>::body() <<
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// write sum to output
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"if(lid == 0){\n" <<
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" output[get_group_id(0)] = scratch[0];\n" <<
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"}\n";
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const context &context = queue.get_context();
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std::stringstream options;
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options << "-DVPT=" << vpt << " -DTPB=" << tpb;
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kernel generic_reduce_kernel = k.compile(context, options.str());
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generic_reduce_kernel.set_arg(output_arg, result);
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size_t work_size = calculate_work_size(count, vpt, tpb);
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queue.enqueue_1d_range_kernel(generic_reduce_kernel, 0, work_size, tpb);
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}
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template<class T>
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inline void initial_reduce(const buffer_iterator<T> &first,
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const buffer_iterator<T> &last,
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const buffer &result,
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const plus<T> &function,
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kernel &reduce_kernel,
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const uint_ vpt,
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const uint_ tpb,
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command_queue &queue)
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{
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(void) function;
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size_t count = std::distance(first, last);
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reduce_kernel.set_arg(0, first.get_buffer());
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reduce_kernel.set_arg(1, uint_(first.get_index()));
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reduce_kernel.set_arg(2, uint_(count));
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reduce_kernel.set_arg(3, result);
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reduce_kernel.set_arg(4, uint_(0));
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size_t work_size = calculate_work_size(count, vpt, tpb);
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queue.enqueue_1d_range_kernel(reduce_kernel, 0, work_size, tpb);
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}
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template<class InputIterator, class T, class Function>
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inline void reduce_on_gpu(InputIterator first,
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InputIterator last,
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buffer_iterator<T> result,
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Function function,
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command_queue &queue)
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{
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const device &device = queue.get_device();
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const context &context = queue.get_context();
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detail::meta_kernel k("reduce");
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k.add_arg<const T*>(memory_object::global_memory, "input");
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k.add_arg<const uint_>("offset");
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k.add_arg<const uint_>("count");
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k.add_arg<T*>(memory_object::global_memory, "output");
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k.add_arg<const uint_>("output_offset");
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k <<
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k.decl<const uint_>("block_offset") << " = get_group_id(0) * VPT * TPB;\n" <<
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"__global const " << type_name<T>() << " *block = input + offset + block_offset;\n" <<
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k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
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"__local " << type_name<T>() << " scratch[TPB];\n" <<
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// private reduction
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k.decl<T>("sum") << " = 0;\n" <<
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"for(uint i = 0; i < VPT; i++){\n" <<
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" if(block_offset + lid + i*TPB < count){\n" <<
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" sum = sum + block[lid+i*TPB]; \n" <<
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" }\n" <<
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"}\n" <<
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"scratch[lid] = sum;\n";
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// discrimination on vendor name
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if(is_nvidia_device(device))
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k << ReduceBody<T,true>::body();
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else
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k << ReduceBody<T,false>::body();
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k <<
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// write sum to output
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"if(lid == 0){\n" <<
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" output[output_offset + get_group_id(0)] = scratch[0];\n" <<
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"}\n";
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std::string cache_key = std::string("__boost_reduce_on_gpu_") + type_name<T>();
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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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uint_ vpt = parameters->get(cache_key, "vpt", 8);
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uint_ tpb = parameters->get(cache_key, "tpb", 128);
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// reduce program compiler flags
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std::stringstream options;
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options << "-DT=" << type_name<T>()
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<< " -DVPT=" << vpt
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<< " -DTPB=" << tpb;
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// load program
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boost::shared_ptr<program_cache> cache =
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program_cache::get_global_cache(context);
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program reduce_program = cache->get_or_build(
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cache_key, options.str(), k.source(), context
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);
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// create reduce kernel
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kernel reduce_kernel(reduce_program, "reduce");
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size_t count = std::distance(first, last);
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// first pass, reduce from input to ping
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buffer ping(context, std::ceil(float(count) / vpt / tpb) * sizeof(T));
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initial_reduce(first, last, ping, function, reduce_kernel, vpt, tpb, queue);
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// update count after initial reduce
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count = static_cast<size_t>(std::ceil(float(count) / vpt / tpb));
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// middle pass(es), reduce between ping and pong
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const buffer *input_buffer = &ping;
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buffer pong(context, static_cast<size_t>(count / vpt / tpb * sizeof(T)));
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const buffer *output_buffer = &pong;
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if(count > vpt * tpb){
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while(count > vpt * tpb){
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reduce_kernel.set_arg(0, *input_buffer);
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reduce_kernel.set_arg(1, uint_(0));
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reduce_kernel.set_arg(2, uint_(count));
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reduce_kernel.set_arg(3, *output_buffer);
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reduce_kernel.set_arg(4, uint_(0));
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size_t work_size = static_cast<size_t>(std::ceil(float(count) / vpt));
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if(work_size % tpb != 0){
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work_size += tpb - work_size % tpb;
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}
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queue.enqueue_1d_range_kernel(reduce_kernel, 0, work_size, tpb);
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std::swap(input_buffer, output_buffer);
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count = static_cast<size_t>(std::ceil(float(count) / vpt / tpb));
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}
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}
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// final pass, reduce from ping/pong to result
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reduce_kernel.set_arg(0, *input_buffer);
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reduce_kernel.set_arg(1, uint_(0));
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reduce_kernel.set_arg(2, uint_(count));
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reduce_kernel.set_arg(3, result.get_buffer());
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reduce_kernel.set_arg(4, uint_(result.get_index()));
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queue.enqueue_1d_range_kernel(reduce_kernel, 0, tpb, tpb);
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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_GPU_HPP
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