[compute] 06/49: Parallel reduce by key algorithm implementation
Ghislain Vaillant
ghisvail-guest at moszumanska.debian.org
Fri Dec 18 17:58:15 UTC 2015
This is an automated email from the git hooks/post-receive script.
ghisvail-guest pushed a commit to branch master
in repository compute.
commit c5409541ac027dc36b023485b353a3e14e3c7ebd
Author: Jakub Szuppe <j.szuppe at gmail.com>
Date: Sat Jul 11 22:12:36 2015 +0200
Parallel reduce by key algorithm implementation
---
.../compute/algorithm/detail/reduce_by_key.hpp | 56 ++-
.../algorithm/detail/reduce_by_key_with_scan.hpp | 519 +++++++++++++++++++++
perf/perf.py | 2 +
3 files changed, 571 insertions(+), 6 deletions(-)
diff --git a/include/boost/compute/algorithm/detail/reduce_by_key.hpp b/include/boost/compute/algorithm/detail/reduce_by_key.hpp
index bdcd14d..65844c9 100644
--- a/include/boost/compute/algorithm/detail/reduce_by_key.hpp
+++ b/include/boost/compute/algorithm/detail/reduce_by_key.hpp
@@ -19,8 +19,8 @@
#include <boost/compute/container/vector.hpp>
#include <boost/compute/detail/iterator_range_size.hpp>
#include <boost/compute/algorithm/detail/serial_reduce_by_key.hpp>
+#include <boost/compute/algorithm/detail/reduce_by_key_with_scan.hpp>
#include <boost/compute/type_traits.hpp>
-#include <boost/compute/utility/program_cache.hpp>
namespace boost {
namespace compute {
@@ -29,6 +29,41 @@ namespace detail {
template<class InputKeyIterator, class InputValueIterator,
class OutputKeyIterator, class OutputValueIterator,
class BinaryFunction, class BinaryPredicate>
+size_t reduce_by_key_on_gpu(InputKeyIterator keys_first,
+ InputKeyIterator keys_last,
+ InputValueIterator values_first,
+ OutputKeyIterator keys_result,
+ OutputValueIterator values_result,
+ BinaryFunction function,
+ BinaryPredicate predicate,
+ command_queue &queue)
+{
+ return detail::reduce_by_key_with_scan(keys_first, keys_last, values_first,
+ keys_result, values_result, function,
+ predicate, queue);
+}
+
+template<class InputKeyIterator, class InputValueIterator,
+ class OutputKeyIterator, class OutputValueIterator>
+bool reduce_by_key_on_gpu_requirements_met(InputKeyIterator keys_first,
+ InputValueIterator values_first,
+ OutputKeyIterator keys_result,
+ OutputValueIterator values_result,
+ const size_t count,
+ command_queue &queue)
+{
+ const device &device = queue.get_device();
+ return (count > 256)
+ && !(device.type() & device::cpu)
+ && reduce_by_key_with_scan_requirements_met(keys_first, values_first,
+ keys_result,values_result,
+ count, queue);
+ return true;
+}
+
+template<class InputKeyIterator, class InputValueIterator,
+ class OutputKeyIterator, class OutputValueIterator,
+ class BinaryFunction, class BinaryPredicate>
inline std::pair<OutputKeyIterator, OutputValueIterator>
dispatch_reduce_by_key(InputKeyIterator keys_first,
InputKeyIterator keys_last,
@@ -55,11 +90,20 @@ dispatch_reduce_by_key(InputKeyIterator keys_first,
);
}
- size_t result_size =
- detail::serial_reduce_by_key(keys_first, keys_last, values_first,
- keys_result, values_result, function,
- predicate, queue);
-
+ size_t result_size = 0;
+ if(reduce_by_key_on_gpu_requirements_met(keys_first, values_first, keys_result,
+ values_result, count, queue)){
+ result_size =
+ detail::reduce_by_key_on_gpu(keys_first, keys_last, values_first,
+ keys_result, values_result, function,
+ predicate, queue);
+ }
+ else {
+ result_size =
+ detail::serial_reduce_by_key(keys_first, keys_last, values_first,
+ keys_result, values_result, function,
+ predicate, queue);
+ }
return
std::make_pair<OutputKeyIterator, OutputValueIterator>(
diff --git a/include/boost/compute/algorithm/detail/reduce_by_key_with_scan.hpp b/include/boost/compute/algorithm/detail/reduce_by_key_with_scan.hpp
new file mode 100644
index 0000000..beb0cc9
--- /dev/null
+++ b/include/boost/compute/algorithm/detail/reduce_by_key_with_scan.hpp
@@ -0,0 +1,519 @@
+//---------------------------------------------------------------------------//
+// Copyright (c) 2015 Jakub Szuppe <j.szuppe at 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_BY_KEY_WITH_SCAN_HPP
+#define BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_BY_KEY_WITH_SCAN_HPP
+
+#include <algorithm>
+#include <iterator>
+
+#include <boost/compute/command_queue.hpp>
+#include <boost/compute/functional.hpp>
+#include <boost/compute/algorithm/inclusive_scan.hpp>
+#include <boost/compute/container/vector.hpp>
+#include <boost/compute/container/detail/scalar.hpp>
+#include <boost/compute/detail/meta_kernel.hpp>
+#include <boost/compute/detail/iterator_range_size.hpp>
+#include <boost/compute/detail/read_write_single_value.hpp>
+#include <boost/compute/type_traits.hpp>
+#include <boost/compute/utility/program_cache.hpp>
+
+namespace boost {
+namespace compute {
+namespace detail {
+
+/// \internal_
+///
+/// Fills \p new_keys_first with unsigned integer keys generated from vector
+/// of original keys \p keys_first. New keys can be distinguish by simple equality
+/// predicate.
+///
+/// \param keys_first iterator pointing to the first key
+/// \param number_of_keys number of keys
+/// \param predicate binary predicate for key comparison
+/// \param new_keys_first iterator pointing to the new keys vector
+/// \param preferred_work_group_size preferred work group size
+/// \param queue command queue to perform the operation
+///
+/// Binary function \p predicate must take two keys as arguments and
+/// return true only if they are considered the same.
+///
+/// The first new key equals zero and the last equals number of unique keys
+/// minus one.
+///
+/// No local memory usage.
+template<class InputKeyIterator, class BinaryPredicate>
+inline void generate_uint_keys(InputKeyIterator keys_first,
+ size_t number_of_keys,
+ BinaryPredicate predicate,
+ vector<uint_>::iterator new_keys_first,
+ size_t preferred_work_group_size,
+ command_queue &queue)
+{
+ typedef typename
+ std::iterator_traits<InputKeyIterator>::value_type key_type;
+
+ detail::meta_kernel k("reduce_by_key_new_key_flags");
+ k.add_set_arg<const uint_>("count", uint_(number_of_keys));
+
+ k <<
+ k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
+ k.decl<uint_>("value") << " = 0;\n" <<
+ "if(gid >= count){\n return;\n}\n" <<
+ "if(gid > 0){ \n" <<
+ k.decl<key_type>("key") << " = " <<
+ keys_first[k.var<const uint_>("gid")] << ";\n" <<
+ k.decl<key_type>("previous_key") << " = " <<
+ keys_first[k.var<const uint_>("gid - 1")] << ";\n" <<
+ " value = " << predicate(k.var<key_type>("previous_key"),
+ k.var<key_type>("key")) <<
+ " ? 0 : 1;\n" <<
+ "}\n else {\n" <<
+ " value = 0;\n" <<
+ "}\n" <<
+ new_keys_first[k.var<const uint_>("gid")] << " = value;\n";
+
+ const context &context = queue.get_context();
+ kernel kernel = k.compile(context);
+
+ size_t work_group_size = preferred_work_group_size;
+ size_t work_groups_no = static_cast<size_t>(
+ std::ceil(float(number_of_keys) / work_group_size)
+ );
+
+ queue.enqueue_1d_range_kernel(kernel,
+ 0,
+ work_groups_no * work_group_size,
+ work_group_size);
+
+ inclusive_scan(new_keys_first, new_keys_first + number_of_keys,
+ new_keys_first, queue);
+}
+
+/// \internal_
+/// Calculate carry-out for each work group.
+/// Carry-out is a pair of the last key processed by a work group and sum of all
+/// values under this key in this work group.
+template<class InputValueIterator, class OutputValueIterator, class BinaryFunction>
+inline void carry_outs(vector<uint_>::iterator keys_first,
+ InputValueIterator values_first,
+ size_t count,
+ vector<uint_>::iterator carry_out_keys_first,
+ OutputValueIterator carry_out_values_first,
+ BinaryFunction function,
+ size_t work_group_size,
+ command_queue &queue)
+{
+ typedef typename
+ std::iterator_traits<OutputValueIterator>::value_type value_out_type;
+
+ detail::meta_kernel k("reduce_by_key_with_scan_carry_outs");
+ k.add_set_arg<const uint_>("count", uint_(count));
+ size_t local_keys_arg = k.add_arg<uint_ *>(memory_object::local_memory, "lkeys");
+ size_t local_vals_arg = k.add_arg<value_out_type *>(memory_object::local_memory, "lvals");
+
+ k <<
+ k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
+ k.decl<const uint_>("wg_size") << " = get_local_size(0);\n" <<
+ k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
+ k.decl<const uint_>("group_id") << " = get_group_id(0);\n" <<
+
+ "if(gid >= count){\n return;\n}\n" <<
+
+ k.decl<uint_>("key") << " = " << keys_first[k.var<const uint_>("gid")] << ";\n" <<
+ k.decl<value_out_type>("value") << " = " << values_first[k.var<const uint_>("gid")] << ";\n" <<
+ "lkeys[lid] = key;\n" <<
+ "lvals[lid] = value;\n" <<
+
+ // Calculate carry out for each work group by performing Hillis/Steele scan
+ // where only last element (key-value pair) is saved
+ k.decl<value_out_type>("result") << " = value;\n" <<
+ k.decl<uint_>("other_key") << ";\n" <<
+ k.decl<value_out_type>("other_value") << ";\n" <<
+
+ "for(" << k.decl<uint_>("offset") << " = 1; " <<
+ "offset < wg_size && lid >= offset; offset *= 2){\n"
+ " barrier(CLK_LOCAL_MEM_FENCE);\n" <<
+ " other_key = lkeys[lid - offset];\n" <<
+ " if(other_key == key){\n" <<
+ " other_value = lvals[lid - offset];\n" <<
+ " result = " << function(k.var<value_out_type>("result"),
+ k.var<value_out_type>("other_value")) << ";\n" <<
+ " }\n" <<
+ " barrier(CLK_LOCAL_MEM_FENCE);\n" <<
+ " lvals[lid] = result;\n" <<
+ "}\n" <<
+
+ // save carry out
+ "if(lid == (wg_size - 1)){\n" <<
+ carry_out_keys_first[k.var<const uint_>("group_id")] << " = key;\n" <<
+ carry_out_values_first[k.var<const uint_>("group_id")] << " = result;\n" <<
+ "}\n";
+
+ size_t work_groups_no = static_cast<size_t>(
+ std::ceil(float(count) / work_group_size)
+ );
+
+ const context &context = queue.get_context();
+ kernel kernel = k.compile(context);
+ kernel.set_arg(local_keys_arg, local_buffer<uint_>(work_group_size));
+ kernel.set_arg(local_vals_arg, local_buffer<value_out_type>(work_group_size));
+
+ queue.enqueue_1d_range_kernel(kernel,
+ 0,
+ work_groups_no * work_group_size,
+ work_group_size);
+}
+
+/// \internal_
+/// Calculate carry-in by performing inclusive scan by key on carry-outs vector.
+template<class OutputValueIterator, class BinaryFunction>
+inline void carry_ins(vector<uint_>::iterator carry_out_keys_first,
+ OutputValueIterator carry_out_values_first,
+ OutputValueIterator carry_in_values_first,
+ size_t carry_out_size,
+ BinaryFunction function,
+ size_t work_group_size,
+ command_queue &queue)
+{
+ typedef typename
+ std::iterator_traits<OutputValueIterator>::value_type value_out_type;
+
+ uint_ values_pre_work_item = static_cast<uint_>(
+ std::ceil(float(carry_out_size) / work_group_size)
+ );
+
+ detail::meta_kernel k("reduce_by_key_with_scan_carry_ins");
+ k.add_set_arg<const uint_>("carry_out_size", uint_(carry_out_size));
+ k.add_set_arg<const uint_>("values_per_work_item", values_pre_work_item);
+ size_t local_keys_arg = k.add_arg<uint_ *>(memory_object::local_memory, "lkeys");
+ size_t local_vals_arg = k.add_arg<value_out_type *>(memory_object::local_memory, "lvals");
+
+ k <<
+ k.decl<const uint_>("id") << " = get_global_id(0) * values_per_work_item;\n" <<
+ k.decl<uint_>("idx") << " = id;\n" <<
+ k.decl<const uint_>("wg_size") << " = get_local_size(0);\n" <<
+ k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
+ k.decl<const uint_>("group_id") << " = get_group_id(0);\n" <<
+
+ "if(id >= carry_out_size){\n return;\n}\n" <<
+
+ k.decl<uint_>("key") << ";\n" <<
+ k.decl<value_out_type>("value") << ";\n" <<
+ k.decl<uint_>("previous_key") << " = " << carry_out_keys_first[k.var<const uint_>("idx")] << ";\n" <<
+ k.decl<value_out_type>("result") << " = " << carry_out_values_first[k.var<const uint_>("idx")] << ";\n" <<
+ carry_in_values_first[k.var<const uint_>("idx")] << " = result;\n" <<
+
+ k.decl<const uint_>("end") << " = (id + values_per_work_item) <= carry_out_size" <<
+ " ? (values_per_work_item + id) : carry_out_size;\n" <<
+
+ "for(idx = idx + 1; idx < end; idx += 1){\n" <<
+ " key = " << carry_out_keys_first[k.var<const uint_>("idx")] << ";\n" <<
+ " value = " << carry_out_values_first[k.var<const uint_>("idx")] << ";\n" <<
+ " if(previous_key == key){\n" <<
+ " result = " << function(k.var<value_out_type>("result"),
+ k.var<value_out_type>("value")) << ";\n" <<
+ " }\n else { \n" <<
+ " result = value;\n"
+ " }\n" <<
+ " " << carry_in_values_first[k.var<const uint_>("idx")] << " = result;\n" <<
+ " previous_key = key;\n"
+ "}\n" <<
+
+ // save the last key and result to local memory
+ "lkeys[lid] = previous_key;\n" <<
+ "lvals[lid] = result;\n" <<
+
+ // Hillis/Steele scan
+ "for(" << k.decl<uint_>("offset") << " = 1; " <<
+ "offset < wg_size && lid >= offset; offset *= 2){\n"
+ " barrier(CLK_LOCAL_MEM_FENCE);\n" <<
+ " key = lkeys[lid - offset];\n" <<
+ " if(previous_key == key){\n" <<
+ " value = lvals[lid - offset];\n" <<
+ " result = " << function(k.var<value_out_type>("result"),
+ k.var<value_out_type>("value")) << ";\n" <<
+ " }\n" <<
+ " barrier(CLK_LOCAL_MEM_FENCE);\n" <<
+ " lvals[lid] = result;\n" <<
+ "}\n" <<
+ "barrier(CLK_LOCAL_MEM_FENCE);\n" <<
+
+ // first in the group has nothing to do
+ "if(lid == 0){\n return;\n}\n" <<
+
+ // load key-value reduced by previous work item
+ "previous_key = lkeys[lid - 1];\n" <<
+ "result = lvals[lid - 1];\n" <<
+
+ // make sure all carry-ins are saved in global memory
+ "barrier( CLK_GLOBAL_MEM_FENCE );\n" <<
+
+ // add key-value reduced by previous work item
+ "for(idx = id; idx < end; idx += 1){\n" <<
+ " key = " << carry_out_keys_first[k.var<const uint_>("idx")] << ";\n" <<
+ " value = " << carry_in_values_first[k.var<const uint_>("idx")] << ";\n" <<
+ " if(previous_key == key){\n" <<
+ " value = " << function(k.var<value_out_type>("result"),
+ k.var<value_out_type>("value")) << ";\n" <<
+ " }\n" <<
+ " " << carry_in_values_first[k.var<const uint_>("idx")] << " = value;\n" <<
+ "}\n";
+
+
+ const context &context = queue.get_context();
+ kernel kernel = k.compile(context);
+ kernel.set_arg(local_keys_arg, local_buffer<uint_>(work_group_size));
+ kernel.set_arg(local_vals_arg, local_buffer<value_out_type>(work_group_size));
+
+ queue.enqueue_1d_range_kernel(kernel,
+ 0,
+ work_group_size,
+ work_group_size);
+}
+
+/// \internal_
+///
+/// Perform final reduction by key. Each work item:
+/// 1. Perform local work-group reduction (Hillis/Steele scan)
+/// 2. Add carry-in (if keys are right)
+/// 3. Save reduced value if next key is different than processed one
+template<class InputKeyIterator, class InputValueIterator,
+ class OutputKeyIterator, class OutputValueIterator,
+ class BinaryFunction>
+inline void final_reduction(InputKeyIterator keys_first,
+ InputValueIterator values_first,
+ OutputKeyIterator keys_result,
+ OutputValueIterator values_result,
+ size_t count,
+ BinaryFunction function,
+ vector<uint_>::iterator new_keys_first,
+ vector<uint_>::iterator carry_in_keys_first,
+ OutputValueIterator carry_in_values_first,
+ size_t carry_in_size,
+ size_t work_group_size,
+ command_queue &queue)
+{
+ typedef typename
+ std::iterator_traits<OutputValueIterator>::value_type value_out_type;
+
+ detail::meta_kernel k("reduce_by_key_with_scan_final_reduction");
+ k.add_set_arg<const uint_>("count", uint_(count));
+ size_t local_keys_arg = k.add_arg<uint_ *>(memory_object::local_memory, "lkeys");
+ size_t local_vals_arg = k.add_arg<value_out_type *>(memory_object::local_memory, "lvals");
+
+ k <<
+ k.decl<const uint_>("gid") << " = get_global_id(0);\n" <<
+ k.decl<const uint_>("wg_size") << " = get_local_size(0);\n" <<
+ k.decl<const uint_>("lid") << " = get_local_id(0);\n" <<
+ k.decl<const uint_>("group_id") << " = get_group_id(0);\n" <<
+
+ "if(gid >= count){\n return;\n}\n" <<
+
+ k.decl<const uint_>("key") << " = " << new_keys_first[k.var<const uint_>("gid")] << ";\n" <<
+ k.decl<value_out_type>("value") << " = " << values_first[k.var<const uint_>("gid")] << ";\n" <<
+ "lkeys[lid] = key;\n" <<
+ "lvals[lid] = value;\n" <<
+
+ // Hillis/Steele scan
+ k.decl<value_out_type>("result") << " = value;\n" <<
+ k.decl<uint_>("other_key") << ";\n" <<
+ k.decl<value_out_type>("other_value") << ";\n" <<
+
+ "for(" << k.decl<uint_>("offset") << " = 1; " <<
+ "offset < wg_size && lid >= offset; offset *= 2){\n"
+ " barrier(CLK_LOCAL_MEM_FENCE);\n" <<
+ " other_key = lkeys[lid - offset];\n" <<
+ " if(other_key == key){\n" <<
+ " other_value = lvals[lid - offset];\n" <<
+ " result = " << function(k.var<value_out_type>("result"),
+ k.var<value_out_type>("other_value")) << ";\n" <<
+ " }\n" <<
+ " barrier(CLK_LOCAL_MEM_FENCE);\n" <<
+ " lvals[lid] = result;\n" <<
+ "}\n" <<
+
+ k.decl<const bool>("save") << " = (gid < (count - 1)) ?"
+ << new_keys_first[k.var<const uint_>("gid + 1")] << " != key" <<
+ ": true;\n" <<
+
+ // Add carry in
+ k.decl<uint_>("carry_in_key") << ";\n" <<
+ "if(group_id > 0 && save) {\n" <<
+ " carry_in_key = " << carry_in_keys_first[k.var<const uint_>("group_id - 1")] << ";\n" <<
+ " if(key == carry_in_key){\n" <<
+ " other_value = " << carry_in_values_first[k.var<const uint_>("group_id - 1")] << ";\n" <<
+ " result = " << function(k.var<value_out_type>("result"),
+ k.var<value_out_type>("other_value")) << ";\n" <<
+ " }\n" <<
+ "}\n" <<
+
+ // Save result only if the next key is different or it's the last element.
+ "if(save){\n" <<
+ keys_result[k.var<uint_>("key")] << " = " << keys_first[k.var<const uint_>("gid")] << ";\n" <<
+ values_result[k.var<uint_>("key")] << " = result;\n" <<
+ "}\n"
+ ;
+
+ size_t work_groups_no = static_cast<size_t>(
+ std::ceil(float(count) / work_group_size)
+ );
+
+ const context &context = queue.get_context();
+ kernel kernel = k.compile(context);
+ kernel.set_arg(local_keys_arg, local_buffer<uint_>(work_group_size));
+ kernel.set_arg(local_vals_arg, local_buffer<value_out_type>(work_group_size));
+
+ queue.enqueue_1d_range_kernel(kernel,
+ 0,
+ work_groups_no * work_group_size,
+ work_group_size);
+}
+
+/// \internal_
+/// Returns preferred work group size for reduce by key with scan algorithm.
+template<class KeyType, class ValueType>
+inline size_t get_work_group_size(const device& device)
+{
+ std::string cache_key = std::string("__boost_reduce_by_key_with_scan")
+ + "k_" + type_name<KeyType>() + "_v_" + type_name<ValueType>();
+
+ // load parameters
+ boost::shared_ptr<parameter_cache> parameters =
+ detail::parameter_cache::get_global_cache(device);
+
+ return (std::max)(
+ static_cast<size_t>(parameters->get(cache_key, "wgsize", 256)),
+ static_cast<size_t>(device.get_info<CL_DEVICE_MAX_WORK_GROUP_SIZE>())
+ );
+}
+
+/// \internal_
+///
+/// 1. For each work group carry-out value is calculated (it's done by key-oriented
+/// Hillis/Steele scan). Carry-out is a pair of the last key processed by work
+/// group and sum of all values under this key in work group.
+/// 2. From every carry-out carry-in is calculated by performing inclusive scan
+/// by key.
+/// 3. Final reduction by key is performed (key-oriented Hillis/Steele scan),
+/// carry-in values are added where needed.
+template<class InputKeyIterator, class InputValueIterator,
+ class OutputKeyIterator, class OutputValueIterator,
+ class BinaryFunction, class BinaryPredicate>
+inline size_t reduce_by_key_with_scan(InputKeyIterator keys_first,
+ InputKeyIterator keys_last,
+ InputValueIterator values_first,
+ OutputKeyIterator keys_result,
+ OutputValueIterator values_result,
+ BinaryFunction function,
+ BinaryPredicate predicate,
+ command_queue &queue)
+{
+ typedef typename
+ std::iterator_traits<InputValueIterator>::value_type value_type;
+ typedef typename
+ std::iterator_traits<InputKeyIterator>::value_type key_type;
+ typedef typename
+ std::iterator_traits<OutputValueIterator>::value_type value_out_type;
+
+ const context &context = queue.get_context();
+ size_t count = detail::iterator_range_size(keys_first, keys_last);
+
+ if(count == 0){
+ return size_t(0);
+ }
+
+ const device &device = queue.get_device();
+ size_t work_group_size = get_work_group_size<value_type, key_type>(device);
+
+ // Replace original key with unsigned integer keys generated based on given
+ // predicate. New key is also an index for keys_result and values_result vectors,
+ // which points to place where reduced value should be saved.
+ vector<uint_> new_keys(count, context);
+ vector<uint_>::iterator new_keys_first = new_keys.begin();
+ generate_uint_keys(keys_first, count, predicate, new_keys_first,
+ work_group_size, queue);
+
+ // Calculate carry-out and carry-in vectors size
+ const size_t carry_out_size = static_cast<size_t>(
+ std::ceil(float(count) / work_group_size)
+ );
+ vector<uint_> carry_out_keys(carry_out_size, context);
+ vector<value_out_type> carry_out_values(carry_out_size, context);
+ carry_outs(new_keys_first, values_first, count, carry_out_keys.begin(),
+ carry_out_values.begin(), function, work_group_size, queue);
+
+ vector<value_out_type> carry_in_values(carry_out_size, context);
+ carry_ins(carry_out_keys.begin(), carry_out_values.begin(),
+ carry_in_values.begin(), carry_out_size, function, work_group_size,
+ queue);
+
+ final_reduction(keys_first, values_first, keys_result, values_result,
+ count, function, new_keys_first, carry_out_keys.begin(),
+ carry_in_values.begin(), carry_out_size, work_group_size,
+ queue);
+
+ const size_t result = read_single_value<uint_>(new_keys.get_buffer(),
+ count - 1, queue);
+ return result + 1;
+}
+
+/// \internal_
+/// Return true if requirements for running reduce by key with scan on given
+/// device are met (at least one work group of preferred size can be run).
+template<class InputKeyIterator, class InputValueIterator,
+ class OutputKeyIterator, class OutputValueIterator>
+bool reduce_by_key_with_scan_requirements_met(InputKeyIterator keys_first,
+ InputValueIterator values_first,
+ OutputKeyIterator keys_result,
+ OutputValueIterator values_result,
+ const size_t count,
+ command_queue &queue)
+{
+ typedef typename
+ std::iterator_traits<InputValueIterator>::value_type value_type;
+ typedef typename
+ std::iterator_traits<InputKeyIterator>::value_type key_type;
+ typedef typename
+ std::iterator_traits<OutputValueIterator>::value_type value_out_type;
+
+ (void) keys_first;
+ (void) values_first;
+ (void) keys_result;
+ (void) values_result;
+
+ const device &device = queue.get_device();
+ // device must have dedicated local memory storage
+ if(device.get_info<CL_DEVICE_LOCAL_MEM_TYPE>() != CL_LOCAL)
+ {
+ return false;
+ }
+
+ // local memory size in bytes (per compute unit)
+ const size_t local_mem_size = device.get_info<CL_DEVICE_LOCAL_MEM_SIZE>();
+
+ // preferred work group size
+ size_t work_group_size = get_work_group_size<key_type, value_type>(device);
+
+ // local memory size needed to perform parallel reduction
+ size_t required_local_mem_size = 0;
+ // keys size
+ required_local_mem_size += sizeof(uint_) * work_group_size;
+ // reduced values size
+ required_local_mem_size += sizeof(value_out_type) * work_group_size;
+
+ return (required_local_mem_size <= local_mem_size);
+}
+
+} // end detail namespace
+} // end compute namespace
+} // end boost namespace
+
+#endif // BOOST_COMPUTE_ALGORITHM_DETAIL_REDUCE_BY_KEY_WITH_SCAN_HPP
diff --git a/perf/perf.py b/perf/perf.py
index f3ff1fe..984535e 100755
--- a/perf/perf.py
+++ b/perf/perf.py
@@ -123,6 +123,7 @@ def run_benchmark(name, sizes, vs=[]):
"merge",
"partial_sum",
"partition",
+ "reduce_by_key",
"reverse",
"reverse_copy",
"rotate",
@@ -139,6 +140,7 @@ def run_benchmark(name, sizes, vs=[]):
"max_element",
"merge",
"partial_sum",
+ "reduce_by_key",
"saxpy",
"sort"
],
--
Alioth's /usr/local/bin/git-commit-notice on /srv/git.debian.org/git/debian-science/packages/compute.git
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