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* The scheduling of computation is moved out the the assignment code and into a new TensorExecutor class * The assignment itself is now a regular node on the expression tree * The expression evaluators start by recursively evaluating all their subexpressions if needed
92 lines
3.0 KiB
C++
92 lines
3.0 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
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#define EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
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namespace Eigen {
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/** \class TensorDevice
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* \ingroup CXX11_Tensor_Module
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*
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* \brief Pseudo expression providing an operator = that will evaluate its argument
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* on the specified computing 'device' (GPU, thread pool, ...)
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*
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* Example:
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* C.device(EIGEN_GPU) = A + B;
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*
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* Todo: thread pools.
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* Todo: operator +=, -=, *= and so on.
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*/
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template <typename ExpressionType, typename DeviceType> class TensorDevice {
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public:
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TensorDevice(const DeviceType& device, ExpressionType& expression) : m_device(device), m_expression(expression) {}
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template<typename OtherDerived>
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EIGEN_STRONG_INLINE TensorDevice& operator=(const OtherDerived& other) {
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typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
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Assign assign(m_expression, other);
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static const bool Vectorize = TensorEvaluator<const Assign, DeviceType>::PacketAccess;
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internal::TensorExecutor<const Assign, DeviceType, Vectorize>::run(assign, m_device);
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return *this;
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}
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protected:
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const DeviceType& m_device;
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ExpressionType& m_expression;
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};
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#ifdef EIGEN_USE_THREADS
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template <typename ExpressionType> class TensorDevice<ExpressionType, ThreadPoolDevice> {
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public:
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TensorDevice(const ThreadPoolDevice& device, ExpressionType& expression) : m_device(device), m_expression(expression) {}
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template<typename OtherDerived>
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EIGEN_STRONG_INLINE TensorDevice& operator=(const OtherDerived& other) {
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typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
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Assign assign(m_expression, other);
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static const bool Vectorize = TensorEvaluator<const Assign, ThreadPoolDevice>::PacketAccess;
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internal::TensorExecutor<const Assign, ThreadPoolDevice, Vectorize>::run(assign, m_device);
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return *this;
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}
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protected:
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const ThreadPoolDevice& m_device;
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ExpressionType& m_expression;
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};
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#endif
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#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
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template <typename ExpressionType> class TensorDevice<ExpressionType, GpuDevice>
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{
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public:
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TensorDevice(const GpuDevice& device, ExpressionType& expression) : m_device(device), m_expression(expression) {}
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template<typename OtherDerived>
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EIGEN_STRONG_INLINE TensorDevice& operator=(const OtherDerived& other) {
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typedef TensorAssignOp<ExpressionType, const OtherDerived> Assign;
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Assign assign(m_expression, other);
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internal::TensorExecutor<const Assign, GpuDevice, false>::run(assign, m_device);
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return *this;
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}
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protected:
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const GpuDevice& m_device;
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ExpressionType m_expression;
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};
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#endif
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} // end namespace Eigen
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#endif // EIGEN_CXX11_TENSOR_TENSOR_DEVICE_H
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