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- //---------------------------------------------------------------------------//
- // Copyright (c) 2013 Kyle Lutz <kyle.r.lutz@gmail.com>
- //
- // Distributed under the Boost Software License, Version 1.0
- // See accompanying file LICENSE_1_0.txt or copy at
- // http://www.boost.org/LICENSE_1_0.txt
- //
- // See http://boostorg.github.com/compute for more information.
- //---------------------------------------------------------------------------//
- #ifndef BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_ON_GPU_HPP
- #define BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_ON_GPU_HPP
- #include <iterator>
- #include <boost/compute/utility/source.hpp>
- #include <boost/compute/program.hpp>
- #include <boost/compute/command_queue.hpp>
- #include <boost/compute/detail/vendor.hpp>
- #include <boost/compute/detail/parameter_cache.hpp>
- #include <boost/compute/detail/work_size.hpp>
- #include <boost/compute/detail/meta_kernel.hpp>
- #include <boost/compute/type_traits/type_name.hpp>
- #include <boost/compute/utility/program_cache.hpp>
- namespace boost {
- namespace compute {
- namespace detail {
- /// \internal
- /// body reduction inside a warp
- template<typename T,bool isNvidiaDevice>
- struct ReduceBody
- {
- static std::string body()
- {
- std::stringstream k;
- // local reduction
- k << "for(int i = 1; i < TPB; i <<= 1){\n" <<
- " barrier(CLK_LOCAL_MEM_FENCE);\n" <<
- " uint mask = (i << 1) - 1;\n" <<
- " if((lid & mask) == 0){\n" <<
- " scratch[lid] += scratch[lid+i];\n" <<
- " }\n" <<
- "}\n";
- return k.str();
- }
- };
- /// \internal
- /// body reduction inside a warp
- /// for nvidia device we can use the "unsafe"
- /// memory optimisation
- template<typename T>
- struct ReduceBody<T,true>
- {
- static std::string body()
- {
- std::stringstream k;
- // local reduction
- // we use TPB to compile only useful instruction
- // local reduction when size is greater than warp size
- k << "barrier(CLK_LOCAL_MEM_FENCE);\n" <<
- "if(TPB >= 1024){\n" <<
- "if(lid < 512) { sum += scratch[lid + 512]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);}\n" <<
- "if(TPB >= 512){\n" <<
- "if(lid < 256) { sum += scratch[lid + 256]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);}\n" <<
- "if(TPB >= 256){\n" <<
- "if(lid < 128) { sum += scratch[lid + 128]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);}\n" <<
- "if(TPB >= 128){\n" <<
- "if(lid < 64) { sum += scratch[lid + 64]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);} \n" <<
- // warp reduction
- "if(lid < 32){\n" <<
- // volatile this way we don't need any barrier
- "volatile __local " << type_name<T>() << " *lmem = scratch;\n" <<
- "if(TPB >= 64) { lmem[lid] = sum = sum + lmem[lid+32];} \n" <<
- "if(TPB >= 32) { lmem[lid] = sum = sum + lmem[lid+16];} \n" <<
- "if(TPB >= 16) { lmem[lid] = sum = sum + lmem[lid+ 8];} \n" <<
- "if(TPB >= 8) { lmem[lid] = sum = sum + lmem[lid+ 4];} \n" <<
- "if(TPB >= 4) { lmem[lid] = sum = sum + lmem[lid+ 2];} \n" <<
- "if(TPB >= 2) { lmem[lid] = sum = sum + lmem[lid+ 1];} \n" <<
- "}\n";
- return k.str();
- }
- };
- template<class InputIterator, class Function>
- inline void initial_reduce(InputIterator first,
- InputIterator last,
- buffer result,
- const Function &function,
- kernel &reduce_kernel,
- const uint_ vpt,
- const uint_ tpb,
- command_queue &queue)
- {
- (void) function;
- (void) reduce_kernel;
- typedef typename std::iterator_traits<InputIterator>::value_type Arg;
- typedef typename boost::tr1_result_of<Function(Arg, Arg)>::type T;
- size_t count = std::distance(first, last);
- detail::meta_kernel k("initial_reduce");
- k.add_set_arg<const uint_>("count", uint_(count));
- size_t output_arg = k.add_arg<T *>(memory_object::global_memory, "output");
- k <<
- k.decl<const uint_>("offset") << " = get_group_id(0) * VPT * TPB;\n" <<
- k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
- "__local " << type_name<T>() << " scratch[TPB];\n" <<
- // private reduction
- k.decl<T>("sum") << " = 0;\n" <<
- "for(uint i = 0; i < VPT; i++){\n" <<
- " if(offset + lid + i*TPB < count){\n" <<
- " sum = sum + " << first[k.var<uint_>("offset+lid+i*TPB")] << ";\n" <<
- " }\n" <<
- "}\n" <<
- "scratch[lid] = sum;\n" <<
- // local reduction
- ReduceBody<T,false>::body() <<
- // write sum to output
- "if(lid == 0){\n" <<
- " output[get_group_id(0)] = scratch[0];\n" <<
- "}\n";
- const context &context = queue.get_context();
- std::stringstream options;
- options << "-DVPT=" << vpt << " -DTPB=" << tpb;
- kernel generic_reduce_kernel = k.compile(context, options.str());
- generic_reduce_kernel.set_arg(output_arg, result);
- size_t work_size = calculate_work_size(count, vpt, tpb);
- queue.enqueue_1d_range_kernel(generic_reduce_kernel, 0, work_size, tpb);
- }
- template<class T>
- inline void initial_reduce(const buffer_iterator<T> &first,
- const buffer_iterator<T> &last,
- const buffer &result,
- const plus<T> &function,
- kernel &reduce_kernel,
- const uint_ vpt,
- const uint_ tpb,
- command_queue &queue)
- {
- (void) function;
- size_t count = std::distance(first, last);
- reduce_kernel.set_arg(0, first.get_buffer());
- reduce_kernel.set_arg(1, uint_(first.get_index()));
- reduce_kernel.set_arg(2, uint_(count));
- reduce_kernel.set_arg(3, result);
- reduce_kernel.set_arg(4, uint_(0));
- size_t work_size = calculate_work_size(count, vpt, tpb);
- queue.enqueue_1d_range_kernel(reduce_kernel, 0, work_size, tpb);
- }
- template<class InputIterator, class T, class Function>
- inline void reduce_on_gpu(InputIterator first,
- InputIterator last,
- buffer_iterator<T> result,
- Function function,
- command_queue &queue)
- {
- const device &device = queue.get_device();
- const context &context = queue.get_context();
- detail::meta_kernel k("reduce");
- k.add_arg<const T*>(memory_object::global_memory, "input");
- k.add_arg<const uint_>("offset");
- k.add_arg<const uint_>("count");
- k.add_arg<T*>(memory_object::global_memory, "output");
- k.add_arg<const uint_>("output_offset");
- k <<
- k.decl<const uint_>("block_offset") << " = get_group_id(0) * VPT * TPB;\n" <<
- "__global const " << type_name<T>() << " *block = input + offset + block_offset;\n" <<
- k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
- "__local " << type_name<T>() << " scratch[TPB];\n" <<
- // private reduction
- k.decl<T>("sum") << " = 0;\n" <<
- "for(uint i = 0; i < VPT; i++){\n" <<
- " if(block_offset + lid + i*TPB < count){\n" <<
- " sum = sum + block[lid+i*TPB]; \n" <<
- " }\n" <<
- "}\n" <<
- "scratch[lid] = sum;\n";
- // discrimination on vendor name
- if(is_nvidia_device(device))
- k << ReduceBody<T,true>::body();
- else
- k << ReduceBody<T,false>::body();
- k <<
- // write sum to output
- "if(lid == 0){\n" <<
- " output[output_offset + get_group_id(0)] = scratch[0];\n" <<
- "}\n";
- std::string cache_key = std::string("__boost_reduce_on_gpu_") + type_name<T>();
- // load parameters
- boost::shared_ptr<parameter_cache> parameters =
- detail::parameter_cache::get_global_cache(device);
- uint_ vpt = parameters->get(cache_key, "vpt", 8);
- uint_ tpb = parameters->get(cache_key, "tpb", 128);
- // reduce program compiler flags
- std::stringstream options;
- options << "-DT=" << type_name<T>()
- << " -DVPT=" << vpt
- << " -DTPB=" << tpb;
- // load program
- boost::shared_ptr<program_cache> cache =
- program_cache::get_global_cache(context);
- program reduce_program = cache->get_or_build(
- cache_key, options.str(), k.source(), context
- );
- // create reduce kernel
- kernel reduce_kernel(reduce_program, "reduce");
- size_t count = std::distance(first, last);
- // first pass, reduce from input to ping
- buffer ping(context, std::ceil(float(count) / vpt / tpb) * sizeof(T));
- initial_reduce(first, last, ping, function, reduce_kernel, vpt, tpb, queue);
- // update count after initial reduce
- count = static_cast<size_t>(std::ceil(float(count) / vpt / tpb));
- // middle pass(es), reduce between ping and pong
- const buffer *input_buffer = &ping;
- buffer pong(context, static_cast<size_t>(count / vpt / tpb * sizeof(T)));
- const buffer *output_buffer = &pong;
- if(count > vpt * tpb){
- while(count > vpt * tpb){
- reduce_kernel.set_arg(0, *input_buffer);
- reduce_kernel.set_arg(1, uint_(0));
- reduce_kernel.set_arg(2, uint_(count));
- reduce_kernel.set_arg(3, *output_buffer);
- reduce_kernel.set_arg(4, uint_(0));
- size_t work_size = static_cast<size_t>(std::ceil(float(count) / vpt));
- if(work_size % tpb != 0){
- work_size += tpb - work_size % tpb;
- }
- queue.enqueue_1d_range_kernel(reduce_kernel, 0, work_size, tpb);
- std::swap(input_buffer, output_buffer);
- count = static_cast<size_t>(std::ceil(float(count) / vpt / tpb));
- }
- }
- // final pass, reduce from ping/pong to result
- reduce_kernel.set_arg(0, *input_buffer);
- reduce_kernel.set_arg(1, uint_(0));
- reduce_kernel.set_arg(2, uint_(count));
- reduce_kernel.set_arg(3, result.get_buffer());
- reduce_kernel.set_arg(4, uint_(result.get_index()));
- queue.enqueue_1d_range_kernel(reduce_kernel, 0, tpb, tpb);
- }
- } // end detail namespace
- } // end compute namespace
- } // end boost namespace
- #endif // BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_ON_GPU_HPP
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