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1 | //---------------------------------------------------------------------------// |
2 | // Copyright (c) 2013 Kyle Lutz <kyle.r.lutz@gmail.com> | |
3 | // | |
4 | // Distributed under the Boost Software License, Version 1.0 | |
5 | // See accompanying file LICENSE_1_0.txt or copy at | |
6 | // http://www.boost.org/LICENSE_1_0.txt | |
7 | // | |
8 | // See http://boostorg.github.com/compute for more information. | |
9 | //---------------------------------------------------------------------------// | |
10 | ||
11 | #ifndef BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_ON_GPU_HPP | |
12 | #define BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_ON_GPU_HPP | |
13 | ||
14 | #include <iterator> | |
15 | ||
16 | #include <boost/compute/utility/source.hpp> | |
17 | #include <boost/compute/program.hpp> | |
18 | #include <boost/compute/command_queue.hpp> | |
19 | #include <boost/compute/detail/vendor.hpp> | |
20 | #include <boost/compute/detail/parameter_cache.hpp> | |
21 | #include <boost/compute/detail/work_size.hpp> | |
22 | #include <boost/compute/detail/meta_kernel.hpp> | |
23 | #include <boost/compute/type_traits/type_name.hpp> | |
24 | #include <boost/compute/utility/program_cache.hpp> | |
25 | ||
26 | namespace boost { | |
27 | namespace compute { | |
28 | namespace detail { | |
29 | ||
30 | /// \internal | |
31 | /// body reduction inside a warp | |
32 | template<typename T,bool isNvidiaDevice> | |
33 | struct ReduceBody | |
34 | { | |
35 | static std::string body() | |
36 | { | |
37 | std::stringstream k; | |
38 | // local reduction | |
39 | k << "for(int i = 1; i < TPB; i <<= 1){\n" << | |
40 | " barrier(CLK_LOCAL_MEM_FENCE);\n" << | |
41 | " uint mask = (i << 1) - 1;\n" << | |
42 | " if((lid & mask) == 0){\n" << | |
43 | " scratch[lid] += scratch[lid+i];\n" << | |
44 | " }\n" << | |
45 | "}\n"; | |
46 | return k.str(); | |
47 | } | |
48 | }; | |
49 | ||
50 | /// \internal | |
51 | /// body reduction inside a warp | |
52 | /// for nvidia device we can use the "unsafe" | |
53 | /// memory optimisation | |
54 | template<typename T> | |
55 | struct ReduceBody<T,true> | |
56 | { | |
57 | static std::string body() | |
58 | { | |
59 | std::stringstream k; | |
60 | // local reduction | |
61 | // we use TPB to compile only useful instruction | |
62 | // local reduction when size is greater than warp size | |
63 | k << "barrier(CLK_LOCAL_MEM_FENCE);\n" << | |
64 | "if(TPB >= 1024){\n" << | |
65 | "if(lid < 512) { sum += scratch[lid + 512]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);}\n" << | |
66 | "if(TPB >= 512){\n" << | |
67 | "if(lid < 256) { sum += scratch[lid + 256]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);}\n" << | |
68 | "if(TPB >= 256){\n" << | |
69 | "if(lid < 128) { sum += scratch[lid + 128]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);}\n" << | |
70 | "if(TPB >= 128){\n" << | |
71 | "if(lid < 64) { sum += scratch[lid + 64]; scratch[lid] = sum;} barrier(CLK_LOCAL_MEM_FENCE);} \n" << | |
72 | ||
73 | // warp reduction | |
74 | "if(lid < 32){\n" << | |
75 | // volatile this way we don't need any barrier | |
76 | "volatile __local " << type_name<T>() << " *lmem = scratch;\n" << | |
77 | "if(TPB >= 64) { lmem[lid] = sum = sum + lmem[lid+32];} \n" << | |
78 | "if(TPB >= 32) { lmem[lid] = sum = sum + lmem[lid+16];} \n" << | |
79 | "if(TPB >= 16) { lmem[lid] = sum = sum + lmem[lid+ 8];} \n" << | |
80 | "if(TPB >= 8) { lmem[lid] = sum = sum + lmem[lid+ 4];} \n" << | |
81 | "if(TPB >= 4) { lmem[lid] = sum = sum + lmem[lid+ 2];} \n" << | |
82 | "if(TPB >= 2) { lmem[lid] = sum = sum + lmem[lid+ 1];} \n" << | |
83 | "}\n"; | |
84 | return k.str(); | |
85 | } | |
86 | }; | |
87 | ||
88 | template<class InputIterator, class Function> | |
89 | inline void initial_reduce(InputIterator first, | |
90 | InputIterator last, | |
91 | buffer result, | |
92 | const Function &function, | |
93 | kernel &reduce_kernel, | |
94 | const uint_ vpt, | |
95 | const uint_ tpb, | |
96 | command_queue &queue) | |
97 | { | |
98 | (void) function; | |
99 | (void) reduce_kernel; | |
100 | ||
101 | typedef typename std::iterator_traits<InputIterator>::value_type Arg; | |
102 | typedef typename boost::tr1_result_of<Function(Arg, Arg)>::type T; | |
103 | ||
104 | size_t count = std::distance(first, last); | |
105 | detail::meta_kernel k("initial_reduce"); | |
106 | k.add_set_arg<const uint_>("count", uint_(count)); | |
107 | size_t output_arg = k.add_arg<T *>(memory_object::global_memory, "output"); | |
108 | ||
109 | k << | |
110 | k.decl<const uint_>("offset") << " = get_group_id(0) * VPT * TPB;\n" << | |
111 | k.decl<const uint_>("lid") << " = get_local_id(0);\n" << | |
112 | ||
113 | "__local " << type_name<T>() << " scratch[TPB];\n" << | |
114 | ||
115 | // private reduction | |
116 | k.decl<T>("sum") << " = 0;\n" << | |
117 | "for(uint i = 0; i < VPT; i++){\n" << | |
118 | " if(offset + lid + i*TPB < count){\n" << | |
119 | " sum = sum + " << first[k.var<uint_>("offset+lid+i*TPB")] << ";\n" << | |
120 | " }\n" << | |
121 | "}\n" << | |
122 | ||
123 | "scratch[lid] = sum;\n" << | |
124 | ||
125 | // local reduction | |
126 | ReduceBody<T,false>::body() << | |
127 | ||
128 | // write sum to output | |
129 | "if(lid == 0){\n" << | |
130 | " output[get_group_id(0)] = scratch[0];\n" << | |
131 | "}\n"; | |
132 | ||
133 | const context &context = queue.get_context(); | |
134 | std::stringstream options; | |
135 | options << "-DVPT=" << vpt << " -DTPB=" << tpb; | |
136 | kernel generic_reduce_kernel = k.compile(context, options.str()); | |
137 | generic_reduce_kernel.set_arg(output_arg, result); | |
138 | ||
139 | size_t work_size = calculate_work_size(count, vpt, tpb); | |
140 | ||
141 | queue.enqueue_1d_range_kernel(generic_reduce_kernel, 0, work_size, tpb); | |
142 | } | |
143 | ||
144 | template<class T> | |
145 | inline void initial_reduce(const buffer_iterator<T> &first, | |
146 | const buffer_iterator<T> &last, | |
147 | const buffer &result, | |
148 | const plus<T> &function, | |
149 | kernel &reduce_kernel, | |
150 | const uint_ vpt, | |
151 | const uint_ tpb, | |
152 | command_queue &queue) | |
153 | { | |
154 | (void) function; | |
155 | ||
156 | size_t count = std::distance(first, last); | |
157 | ||
158 | reduce_kernel.set_arg(0, first.get_buffer()); | |
159 | reduce_kernel.set_arg(1, uint_(first.get_index())); | |
160 | reduce_kernel.set_arg(2, uint_(count)); | |
161 | reduce_kernel.set_arg(3, result); | |
162 | reduce_kernel.set_arg(4, uint_(0)); | |
163 | ||
164 | size_t work_size = calculate_work_size(count, vpt, tpb); | |
165 | ||
166 | queue.enqueue_1d_range_kernel(reduce_kernel, 0, work_size, tpb); | |
167 | } | |
168 | ||
169 | template<class InputIterator, class T, class Function> | |
170 | inline void reduce_on_gpu(InputIterator first, | |
171 | InputIterator last, | |
172 | buffer_iterator<T> result, | |
173 | Function function, | |
174 | command_queue &queue) | |
175 | { | |
176 | const device &device = queue.get_device(); | |
177 | const context &context = queue.get_context(); | |
178 | ||
179 | detail::meta_kernel k("reduce"); | |
180 | k.add_arg<const T*>(memory_object::global_memory, "input"); | |
181 | k.add_arg<const uint_>("offset"); | |
182 | k.add_arg<const uint_>("count"); | |
183 | k.add_arg<T*>(memory_object::global_memory, "output"); | |
184 | k.add_arg<const uint_>("output_offset"); | |
185 | ||
186 | k << | |
187 | k.decl<const uint_>("block_offset") << " = get_group_id(0) * VPT * TPB;\n" << | |
188 | "__global const " << type_name<T>() << " *block = input + offset + block_offset;\n" << | |
189 | k.decl<const uint_>("lid") << " = get_local_id(0);\n" << | |
190 | ||
191 | "__local " << type_name<T>() << " scratch[TPB];\n" << | |
192 | // private reduction | |
193 | k.decl<T>("sum") << " = 0;\n" << | |
194 | "for(uint i = 0; i < VPT; i++){\n" << | |
195 | " if(block_offset + lid + i*TPB < count){\n" << | |
196 | " sum = sum + block[lid+i*TPB]; \n" << | |
197 | " }\n" << | |
198 | "}\n" << | |
199 | ||
200 | "scratch[lid] = sum;\n"; | |
201 | ||
202 | // discrimination on vendor name | |
203 | if(is_nvidia_device(device)) | |
204 | k << ReduceBody<T,true>::body(); | |
205 | else | |
206 | k << ReduceBody<T,false>::body(); | |
207 | ||
208 | k << | |
209 | // write sum to output | |
210 | "if(lid == 0){\n" << | |
211 | " output[output_offset + get_group_id(0)] = scratch[0];\n" << | |
212 | "}\n"; | |
213 | ||
214 | std::string cache_key = std::string("__boost_reduce_on_gpu_") + type_name<T>(); | |
215 | ||
216 | // load parameters | |
217 | boost::shared_ptr<parameter_cache> parameters = | |
218 | detail::parameter_cache::get_global_cache(device); | |
219 | ||
220 | uint_ vpt = parameters->get(cache_key, "vpt", 8); | |
221 | uint_ tpb = parameters->get(cache_key, "tpb", 128); | |
222 | ||
223 | // reduce program compiler flags | |
224 | std::stringstream options; | |
225 | options << "-DT=" << type_name<T>() | |
226 | << " -DVPT=" << vpt | |
227 | << " -DTPB=" << tpb; | |
228 | ||
229 | // load program | |
230 | boost::shared_ptr<program_cache> cache = | |
231 | program_cache::get_global_cache(context); | |
232 | ||
233 | program reduce_program = cache->get_or_build( | |
234 | cache_key, options.str(), k.source(), context | |
235 | ); | |
236 | ||
237 | // create reduce kernel | |
238 | kernel reduce_kernel(reduce_program, "reduce"); | |
239 | ||
240 | size_t count = std::distance(first, last); | |
241 | ||
242 | // first pass, reduce from input to ping | |
243 | buffer ping(context, std::ceil(float(count) / vpt / tpb) * sizeof(T)); | |
244 | initial_reduce(first, last, ping, function, reduce_kernel, vpt, tpb, queue); | |
245 | ||
246 | // update count after initial reduce | |
247 | count = static_cast<size_t>(std::ceil(float(count) / vpt / tpb)); | |
248 | ||
249 | // middle pass(es), reduce between ping and pong | |
250 | const buffer *input_buffer = &ping; | |
251 | buffer pong(context, static_cast<size_t>(count / vpt / tpb * sizeof(T))); | |
252 | const buffer *output_buffer = &pong; | |
253 | if(count > vpt * tpb){ | |
254 | while(count > vpt * tpb){ | |
255 | reduce_kernel.set_arg(0, *input_buffer); | |
256 | reduce_kernel.set_arg(1, uint_(0)); | |
257 | reduce_kernel.set_arg(2, uint_(count)); | |
258 | reduce_kernel.set_arg(3, *output_buffer); | |
259 | reduce_kernel.set_arg(4, uint_(0)); | |
260 | ||
261 | size_t work_size = static_cast<size_t>(std::ceil(float(count) / vpt)); | |
262 | if(work_size % tpb != 0){ | |
263 | work_size += tpb - work_size % tpb; | |
264 | } | |
265 | queue.enqueue_1d_range_kernel(reduce_kernel, 0, work_size, tpb); | |
266 | ||
267 | std::swap(input_buffer, output_buffer); | |
268 | count = static_cast<size_t>(std::ceil(float(count) / vpt / tpb)); | |
269 | } | |
270 | } | |
271 | ||
272 | // final pass, reduce from ping/pong to result | |
273 | reduce_kernel.set_arg(0, *input_buffer); | |
274 | reduce_kernel.set_arg(1, uint_(0)); | |
275 | reduce_kernel.set_arg(2, uint_(count)); | |
276 | reduce_kernel.set_arg(3, result.get_buffer()); | |
277 | reduce_kernel.set_arg(4, uint_(result.get_index())); | |
278 | ||
279 | queue.enqueue_1d_range_kernel(reduce_kernel, 0, tpb, tpb); | |
280 | } | |
281 | ||
282 | } // end detail namespace | |
283 | } // end compute namespace | |
284 | } // end boost namespace | |
285 | ||
286 | #endif // BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_ON_GPU_HPP |