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// Copyright 2016 The Gemmlowp Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef GEMMLOWP_META_MULTI_THREAD_TRANSFORM_H_
#define GEMMLOWP_META_MULTI_THREAD_TRANSFORM_H_
#include "multi_thread_common.h"
#include "single_thread_transform.h"
namespace gemmlowp {
namespace meta {
namespace internal {
const int kTransformTaskOverhead = 128000;
const int kMinTransformTaskSize = 32000;
template <typename MultiThreadingContext, typename Params>
inline bool PrepareTransform1DTasks(MultiThreadingContext* context,
const Params& params, int kernel_size,
std::vector<Params>* task_params) {
typedef Transform1DUtil<typename Params::InType, typename Params::OutType,
typename Params::Kernel>
Util;
const int max_threads = ResolveMaxThreads(context->max_num_threads());
const int task_size = Util::EstimateComputeCost(params.kernel);
const int max_tasks_by_size =
(task_size - kTransformTaskOverhead) / kMinTransformTaskSize;
const int real_tasks = std::max(1, std::min(max_threads, max_tasks_by_size));
if (real_tasks == 1) {
return false;
}
const int chunk = params.kernel.count / real_tasks;
for (int i = 0; i < real_tasks - 1; ++i) {
task_params->push_back(params);
Params& task = task_params->back();
task.kernel.count = chunk;
task.input = Util::OffsetInput(params.kernel, params.input, i * chunk);
task.output = Util::OffsetOutput(params.kernel, params.output, i * chunk);
}
task_params->push_back(params);
Params& task = task_params->back();
const int sum_chunk = (real_tasks - 1) * chunk;
task.kernel.count = params.kernel.count - sum_chunk;
task.input = Util::OffsetInput(params.kernel, params.input, sum_chunk);
task.output = Util::OffsetOutput(params.kernel, params.output, sum_chunk);
return true;
}
template <typename Params, int kernel_size>
struct Transform1DTaskRunner : gemmlowp::Task {
Transform1DTaskRunner(const Params& params) : params(params) {}
void Run() override { Transform1D<Params, kernel_size>(params); }
Params params;
};
} // namespace internal
template <typename MultiThreadingContext, typename Params, int kernel_size>
inline void MultiThreadTransform1D(MultiThreadingContext* context,
const Params& params) {
typedef internal::Transform1DTaskRunner<Params, kernel_size> TaskRunnerType;
std::vector<Params> task_params;
if (!internal::PrepareTransform1DTasks<MultiThreadingContext, Params>(
context, params, kernel_size, &task_params)) {
Transform1D<Params, kernel_size>(params);
return;
}
auto workers_pool = context->workers_pool();
std::vector<Task*> tasks;
for (auto& task_param : task_params) {
tasks.push_back(new TaskRunnerType(task_param));
}
workers_pool->Execute(tasks);
}
} // namespace meta
} // namespace gemmlowp
#endif // GEMMLOWP_META_MULTI_THREAD_TRANSFORM_H_