/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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// Class declaration for Stream type that enqueues tasks onto a host/CPU-based
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// execution context (as opposed to a GPU device), HostExecutor.
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#ifndef TENSORFLOW_STREAM_EXECUTOR_HOST_HOST_STREAM_H_
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#define TENSORFLOW_STREAM_EXECUTOR_HOST_HOST_STREAM_H_
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#include <functional>
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#include <memory>
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#include "tensorflow/stream_executor/lib/threadpool.h"
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#include "tensorflow/stream_executor/stream_executor_internal.h"
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namespace stream_executor {
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namespace host {
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class HostStream : public internal::StreamInterface {
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public:
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HostStream();
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~HostStream() override;
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bool EnqueueTask(std::function<void()> task);
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void *GpuStreamHack() override { return nullptr; }
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void **GpuStreamMemberHack() override { return nullptr; }
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void BlockUntilDone();
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private:
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// Use only one thread and own task queue to preserve FIFO ordering
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// for the operations enqueued by any given stream.
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static const int kExecutorThreads = 1;
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std::unique_ptr<port::ThreadPool> host_executor_;
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mutex mu_;
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int pending_tasks_ GUARDED_BY(mu_) = 0;
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condition_variable completion_condition_;
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};
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} // namespace host
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} // namespace stream_executor
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#endif // TENSORFLOW_STREAM_EXECUTOR_HOST_HOST_STREAM_H_
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