ccminer/compat/thrust/transform_reduce.h

/*
 *  Copyright 2008-2012 NVIDIA Corporation
 *
 *  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.
 */


/*! \file transform_reduce.h
 *  \brief Fused transform / reduction
 */

#pragma once

#include <thrust/detail/config.h>
#include <thrust/detail/execution_policy.h>

namespace thrust
{


/*! \addtogroup reductions
 *  \{
 *  \addtogroup transformed_reductions Transformed Reductions
 *  \ingroup reductions
 *  \{
 */


/*! \p transform_reduce fuses the \p transform and \p reduce operations.
 *  \p transform_reduce is equivalent to performing a transformation defined by
 *  \p unary_op into a temporary sequence and then performing \p reduce on the
 *  transformed sequence. In most cases, fusing these two operations together is
 *  more efficient, since fewer memory reads and writes are required.
 *
 *  \p transform_reduce performs a reduction on the transformation of the
 *  sequence <tt>[first, last)</tt> according to \p unary_op. Specifically,
 *  \p unary_op is applied to each element of the sequence and then the result
 *  is reduced to a single value with \p binary_op using the initial value 
 *  \p init.  Note that the transformation \p unary_op is not applied to 
 *  the initial value \p init.  The order of reduction is not specified, 
 *  so \p binary_op must be both commutative and associative. 
 *
 *  The algorithm's execution is parallelized as determined by \p exec.
 *
 *  \param exec The execution policy to use for parallelization.
 *  \param first The beginning of the sequence.
 *  \param last The end of the sequence.
 *  \param unary_op The function to apply to each element of the input sequence.
 *  \param init The result is initialized to this value.
 *  \param binary_op The reduction operation.
 *  \return The result of the transformed reduction.
 *
 *  \tparam DerivedPolicy The name of the derived execution policy.
 *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
 *          and \p InputIterator's \c value_type is convertible to \p UnaryFunction's \c argument_type.
 *  \tparam UnaryFunction is a model of <a href="http://www.sgi.com/tech/stl/UnaryFunction.html">Unary Function</a>,
 *          and \p UnaryFunction's \c result_type is convertible to \c OutputType.
 *  \tparam OutputType is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
 *          and is convertible to \p BinaryFunction's \c first_argument_type and \c second_argument_type.
 *  \tparam BinaryFunction is a model of <a href="http://www.sgi.com/tech/stl/BinaryFunction.html">Binary Function</a>,
 *          and \p BinaryFunction's \c result_type is convertible to \p OutputType.
 *
 *  The following code snippet demonstrates how to use \p transform_reduce
 *  to compute the maximum value of the absolute value of the elements
 *  of a range using the \p thrust::host execution policy for parallelization:
 *
 *  \code
 *  #include <thrust/transform_reduce.h>
 *  #include <thrust/functional.h>
 *  #include <thrust/execution_policy.h>
 *
 *  template<typename T>
 *  struct absolute_value : public unary_function<T,T>
 *  {
 *    __host__ __device__ T operator()(const T &x) const
 *    {
 *      return x < T(0) ? -x : x;
 *    }
 *  };
 *
 *  ...
 *
 *  int data[6] = {-1, 0, -2, -2, 1, -3};
 *  int result = thrust::transform_reduce(thrust::host,
 *                                        data, data + 6,
 *                                        absolute_value<int>(),
 *                                        0,
 *                                        thrust::maximum<int>());
 *  // result == 3
 *  \endcode
 *
 *  \see \c transform
 *  \see \c reduce
 */
template<typename DerivedPolicy,
         typename InputIterator, 
         typename UnaryFunction, 
         typename OutputType,
         typename BinaryFunction>
  OutputType transform_reduce(const thrust::detail::execution_policy_base<DerivedPolicy> &exec,
                              InputIterator first,
                              InputIterator last,
                              UnaryFunction unary_op,
                              OutputType init,
                              BinaryFunction binary_op);


/*! \p transform_reduce fuses the \p transform and \p reduce operations.
 *  \p transform_reduce is equivalent to performing a transformation defined by
 *  \p unary_op into a temporary sequence and then performing \p reduce on the
 *  transformed sequence. In most cases, fusing these two operations together is
 *  more efficient, since fewer memory reads and writes are required.
 *
 *  \p transform_reduce performs a reduction on the transformation of the
 *  sequence <tt>[first, last)</tt> according to \p unary_op. Specifically,
 *  \p unary_op is applied to each element of the sequence and then the result
 *  is reduced to a single value with \p binary_op using the initial value 
 *  \p init.  Note that the transformation \p unary_op is not applied to 
 *  the initial value \p init.  The order of reduction is not specified, 
 *  so \p binary_op must be both commutative and associative. 
 *
 *  \param first The beginning of the sequence.
 *  \param last The end of the sequence.
 *  \param unary_op The function to apply to each element of the input sequence.
 *  \param init The result is initialized to this value.
 *  \param binary_op The reduction operation.
 *  \return The result of the transformed reduction.
 *
 *  \tparam InputIterator is a model of <a href="http://www.sgi.com/tech/stl/InputIterator.html">Input Iterator</a>,
 *          and \p InputIterator's \c value_type is convertible to \p UnaryFunction's \c argument_type.
 *  \tparam UnaryFunction is a model of <a href="http://www.sgi.com/tech/stl/UnaryFunction.html">Unary Function</a>,
 *          and \p UnaryFunction's \c result_type is convertible to \c OutputType.
 *  \tparam OutputType is a model of <a href="http://www.sgi.com/tech/stl/Assignable.html">Assignable</a>,
 *          and is convertible to \p BinaryFunction's \c first_argument_type and \c second_argument_type.
 *  \tparam BinaryFunction is a model of <a href="http://www.sgi.com/tech/stl/BinaryFunction.html">Binary Function</a>,
 *          and \p BinaryFunction's \c result_type is convertible to \p OutputType.
 *
 *  The following code snippet demonstrates how to use \p transform_reduce
 *  to compute the maximum value of the absolute value of the elements
 *  of a range.
 *
 *  \code
 *  #include <thrust/transform_reduce.h>
 *  #include <thrust/functional.h>
 *
 *  template<typename T>
 *  struct absolute_value : public unary_function<T,T>
 *  {
 *    __host__ __device__ T operator()(const T &x) const
 *    {
 *      return x < T(0) ? -x : x;
 *    }
 *  };
 *
 *  ...
 *
 *  int data[6] = {-1, 0, -2, -2, 1, -3};
 *  int result = thrust::transform_reduce(data, data + 6,
 *                                        absolute_value<int>(),
 *                                        0,
 *                                        thrust::maximum<int>());
 *  // result == 3
 *  \endcode
 *
 *  \see \c transform
 *  \see \c reduce
 */
template<typename InputIterator, 
         typename UnaryFunction, 
         typename OutputType,
         typename BinaryFunction>
  OutputType transform_reduce(InputIterator first,
                              InputIterator last,
                              UnaryFunction unary_op,
                              OutputType init,
                              BinaryFunction binary_op);


/*! \} // end transformed_reductions
 *  \} // end reductions
 */


} // end namespace thrust

#include <thrust/detail/transform_reduce.inl>