/usr/include/viennacl/linalg/scalar_operations.hpp is in libviennacl-dev 1.5.2-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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#define VIENNACL_LINALG_SCALAR_OPERATIONS_HPP
/* =========================================================================
Copyright (c) 2010-2014, Institute for Microelectronics,
Institute for Analysis and Scientific Computing,
TU Wien.
Portions of this software are copyright by UChicago Argonne, LLC.
-----------------
ViennaCL - The Vienna Computing Library
-----------------
Project Head: Karl Rupp rupp@iue.tuwien.ac.at
(A list of authors and contributors can be found in the PDF manual)
License: MIT (X11), see file LICENSE in the base directory
============================================================================= */
/** @file viennacl/linalg/scalar_operations.hpp
@brief Implementations of scalar operations.
*/
#include "viennacl/forwards.h"
#include "viennacl/tools/tools.hpp"
#include "viennacl/meta/predicate.hpp"
#include "viennacl/meta/enable_if.hpp"
#include "viennacl/traits/size.hpp"
#include "viennacl/traits/start.hpp"
#include "viennacl/traits/handle.hpp"
#include "viennacl/traits/stride.hpp"
#include "viennacl/linalg/host_based/scalar_operations.hpp"
#ifdef VIENNACL_WITH_OPENCL
#include "viennacl/linalg/opencl/scalar_operations.hpp"
#endif
#ifdef VIENNACL_WITH_CUDA
#include "viennacl/linalg/cuda/scalar_operations.hpp"
#endif
namespace viennacl
{
namespace linalg
{
/** @brief Interface for the generic operation s1 = s2 @ alpha, where s1 and s2 are GPU scalars, @ denotes multiplication or division, and alpha is either a GPU or a CPU scalar
*
* @param s1 The first (GPU) scalar
* @param s2 The second (GPU) scalar
* @param alpha The scalar alpha in the operation
* @param len_alpha If alpha is obtained from summing over a small GPU vector (e.g. the final summation after a multi-group reduction), then supply the length of the array here
* @param reciprocal_alpha If true, then s2 / alpha instead of s2 * alpha is computed
* @param flip_sign_alpha If true, then (-alpha) is used instead of alpha
*/
template <typename S1,
typename S2, typename ScalarType1>
typename viennacl::enable_if< viennacl::is_scalar<S1>::value
&& viennacl::is_scalar<S2>::value
&& viennacl::is_any_scalar<ScalarType1>::value
>::type
as(S1 & s1,
S2 const & s2, ScalarType1 const & alpha, vcl_size_t len_alpha, bool reciprocal_alpha, bool flip_sign_alpha)
{
switch (viennacl::traits::handle(s1).get_active_handle_id())
{
case viennacl::MAIN_MEMORY:
viennacl::linalg::host_based::as(s1, s2, alpha, len_alpha, reciprocal_alpha, flip_sign_alpha);
break;
#ifdef VIENNACL_WITH_OPENCL
case viennacl::OPENCL_MEMORY:
viennacl::linalg::opencl::as(s1, s2, alpha, len_alpha, reciprocal_alpha, flip_sign_alpha);
break;
#endif
#ifdef VIENNACL_WITH_CUDA
case viennacl::CUDA_MEMORY:
viennacl::linalg::cuda::as(s1, s2, alpha, len_alpha, reciprocal_alpha, flip_sign_alpha);
break;
#endif
case viennacl::MEMORY_NOT_INITIALIZED:
throw memory_exception("not initialised!");
default:
throw memory_exception("not implemented");
}
}
/** @brief Interface for the generic operation s1 = s2 @ alpha + s3 @ beta, where s1, s2 and s3 are GPU scalars, @ denotes multiplication or division, and alpha, beta are either a GPU or a CPU scalar
*
* @param s1 The first (GPU) scalar
* @param s2 The second (GPU) scalar
* @param alpha The scalar alpha in the operation
* @param len_alpha If alpha is a small GPU vector, which needs to be summed in order to obtain the final scalar, then supply the length of the array here
* @param reciprocal_alpha If true, then s2 / alpha instead of s2 * alpha is computed
* @param flip_sign_alpha If true, then (-alpha) is used instead of alpha
* @param s3 The third (GPU) scalar
* @param beta The scalar beta in the operation
* @param len_beta If beta is obtained from summing over a small GPU vector (e.g. the final summation after a multi-group reduction), then supply the length of the array here
* @param reciprocal_beta If true, then s2 / beta instead of s2 * beta is computed
* @param flip_sign_beta If true, then (-beta) is used instead of beta
*/
template <typename S1,
typename S2, typename ScalarType1,
typename S3, typename ScalarType2>
typename viennacl::enable_if< viennacl::is_scalar<S1>::value
&& viennacl::is_scalar<S2>::value
&& viennacl::is_scalar<S3>::value
&& viennacl::is_any_scalar<ScalarType1>::value
&& viennacl::is_any_scalar<ScalarType2>::value
>::type
asbs(S1 & s1,
S2 const & s2, ScalarType1 const & alpha, vcl_size_t len_alpha, bool reciprocal_alpha, bool flip_sign_alpha,
S3 const & s3, ScalarType2 const & beta, vcl_size_t len_beta, bool reciprocal_beta, bool flip_sign_beta)
{
switch (viennacl::traits::handle(s1).get_active_handle_id())
{
case viennacl::MAIN_MEMORY:
viennacl::linalg::host_based::asbs(s1,
s2, alpha, len_alpha, reciprocal_alpha, flip_sign_alpha,
s3, beta, len_beta, reciprocal_beta, flip_sign_beta);
break;
#ifdef VIENNACL_WITH_OPENCL
case viennacl::OPENCL_MEMORY:
viennacl::linalg::opencl::asbs(s1,
s2, alpha, len_alpha, reciprocal_alpha, flip_sign_alpha,
s3, beta, len_beta, reciprocal_beta, flip_sign_beta);
break;
#endif
#ifdef VIENNACL_WITH_CUDA
case viennacl::CUDA_MEMORY:
viennacl::linalg::cuda::asbs(s1,
s2, alpha, len_alpha, reciprocal_alpha, flip_sign_alpha,
s3, beta, len_beta, reciprocal_beta, flip_sign_beta);
break;
#endif
case viennacl::MEMORY_NOT_INITIALIZED:
throw memory_exception("not initialised!");
default:
throw memory_exception("not implemented");
}
}
/** @brief Interface for the generic operation s1 += s2 @ alpha + s3 @ beta, where s1, s2 and s3 are GPU scalars, @ denotes multiplication or division, and alpha, beta are either a GPU or a CPU scalar
*
* @param s1 The first (GPU) scalar
* @param s2 The second (GPU) scalar
* @param alpha The scalar alpha in the operation
* @param len_alpha If alpha is a small GPU vector, which needs to be summed in order to obtain the final scalar, then supply the length of the array here
* @param reciprocal_alpha If true, then s2 / alpha instead of s2 * alpha is computed
* @param flip_sign_alpha If true, then (-alpha) is used instead of alpha
* @param s3 The third (GPU) scalar
* @param beta The scalar beta in the operation
* @param len_beta If beta is obtained from summing over a small GPU vector (e.g. the final summation after a multi-group reduction), then supply the length of the array here
* @param reciprocal_beta If true, then s2 / beta instead of s2 * beta is computed
* @param flip_sign_beta If true, then (-beta) is used instead of beta
*/
template <typename S1,
typename S2, typename ScalarType1,
typename S3, typename ScalarType2>
typename viennacl::enable_if< viennacl::is_scalar<S1>::value
&& viennacl::is_scalar<S2>::value
&& viennacl::is_scalar<S3>::value
&& viennacl::is_any_scalar<ScalarType1>::value
&& viennacl::is_any_scalar<ScalarType2>::value
>::type
asbs_s(S1 & s1,
S2 const & s2, ScalarType1 const & alpha, vcl_size_t len_alpha, bool reciprocal_alpha, bool flip_sign_alpha,
S3 const & s3, ScalarType2 const & beta, vcl_size_t len_beta, bool reciprocal_beta, bool flip_sign_beta)
{
switch (viennacl::traits::handle(s1).get_active_handle_id())
{
case viennacl::MAIN_MEMORY:
viennacl::linalg::host_based::asbs_s(s1,
s2, alpha, len_alpha, reciprocal_alpha, flip_sign_alpha,
s3, beta, len_beta, reciprocal_beta, flip_sign_beta);
break;
#ifdef VIENNACL_WITH_OPENCL
case viennacl::OPENCL_MEMORY:
viennacl::linalg::opencl::asbs_s(s1,
s2, alpha, len_alpha, reciprocal_alpha, flip_sign_alpha,
s3, beta, len_beta, reciprocal_beta, flip_sign_beta);
break;
#endif
#ifdef VIENNACL_WITH_CUDA
case viennacl::CUDA_MEMORY:
viennacl::linalg::cuda::asbs_s(s1,
s2, alpha, len_alpha, reciprocal_alpha, flip_sign_alpha,
s3, beta, len_beta, reciprocal_beta, flip_sign_beta);
break;
#endif
case viennacl::MEMORY_NOT_INITIALIZED:
throw memory_exception("not initialised!");
default:
throw memory_exception("not implemented");
}
}
/** @brief Swaps the contents of two scalars
*
* @param s1 The first scalar
* @param s2 The second scalar
*/
template <typename S1, typename S2>
typename viennacl::enable_if< viennacl::is_scalar<S1>::value
&& viennacl::is_scalar<S2>::value
>::type
swap(S1 & s1, S2 & s2)
{
switch (viennacl::traits::handle(s1).get_active_handle_id())
{
case viennacl::MAIN_MEMORY:
viennacl::linalg::host_based::swap(s1, s2);
break;
#ifdef VIENNACL_WITH_OPENCL
case viennacl::OPENCL_MEMORY:
viennacl::linalg::opencl::swap(s1, s2);
break;
#endif
#ifdef VIENNACL_WITH_CUDA
case viennacl::CUDA_MEMORY:
viennacl::linalg::cuda::swap(s1, s2);
break;
#endif
case viennacl::MEMORY_NOT_INITIALIZED:
throw memory_exception("not initialised!");
default:
throw memory_exception("not implemented");
}
}
} //namespace linalg
} //namespace viennacl
#endif
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