http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/opencl/vector_operations.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/opencl/vector_operations.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/opencl/vector_operations.hpp deleted file mode 100644 index cd04482..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/opencl/vector_operations.hpp +++ /dev/null @@ -1,1263 +0,0 @@ -#ifndef VIENNACL_LINALG_OPENCL_VECTOR_OPERATIONS_HPP_ -#define VIENNACL_LINALG_OPENCL_VECTOR_OPERATIONS_HPP_ - -/* ========================================================================= - Copyright (c) 2010-2016, 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 [email protected] - - (A list of authors and contributors can be found in the manual) - - License: MIT (X11), see file LICENSE in the base directory -============================================================================= */ - -/** @file viennacl/linalg/opencl/vector_operations.hpp - @brief Implementations of vector operations using OpenCL -*/ - -#include <cmath> - -#include "viennacl/forwards.h" -#include "viennacl/detail/vector_def.hpp" -#include "viennacl/ocl/device.hpp" -#include "viennacl/ocl/handle.hpp" -#include "viennacl/ocl/kernel.hpp" -#include "viennacl/scalar.hpp" -#include "viennacl/tools/tools.hpp" -#include "viennacl/linalg/opencl/common.hpp" -#include "viennacl/linalg/opencl/kernels/vector.hpp" -#include "viennacl/linalg/opencl/kernels/vector_element.hpp" -#include "viennacl/linalg/opencl/kernels/scan.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" - -namespace viennacl -{ -namespace linalg -{ -namespace opencl -{ - -// -// Introductory note: By convention, all dimensions are already checked in the dispatcher frontend. No need to double-check again in here! -// -template<typename DestNumericT, typename SrcNumericT> -void convert(vector_base<DestNumericT> & dest, vector_base<SrcNumericT> const & src) -{ - assert(viennacl::traits::opencl_handle(dest).context() == viennacl::traits::opencl_handle(src).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - std::string kernel_name("convert_"); - kernel_name += viennacl::ocl::type_to_string<DestNumericT>::apply(); - kernel_name += "_"; - kernel_name += viennacl::ocl::type_to_string<SrcNumericT>::apply(); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(dest).context()); - viennacl::linalg::opencl::kernels::vector_convert::init(ctx); - viennacl::ocl::kernel& k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector_convert::program_name(), kernel_name); - - viennacl::ocl::enqueue(k( dest, cl_uint(dest.start()), cl_uint(dest.stride()), cl_uint(dest.size()), - src, cl_uint( src.start()), cl_uint( src.stride()) - ) ); - -} - -template <typename T, typename ScalarType1> -void av(vector_base<T> & vec1, - vector_base<T> const & vec2, ScalarType1 const & alpha, vcl_size_t len_alpha, bool reciprocal_alpha, bool flip_sign_alpha) -{ - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(vec2).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - viennacl::linalg::opencl::kernels::vector<T>::init(ctx); - - cl_uint options_alpha = detail::make_options(len_alpha, reciprocal_alpha, flip_sign_alpha); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), - (viennacl::is_cpu_scalar<ScalarType1>::value ? "av_cpu" : "av_gpu")); - k.global_work_size(0, std::min<vcl_size_t>(128 * k.local_work_size(), - viennacl::tools::align_to_multiple<vcl_size_t>(viennacl::traits::size(vec1), k.local_work_size()) ) ); - - viennacl::ocl::packed_cl_uint size_vec1; - size_vec1.start = cl_uint(viennacl::traits::start(vec1)); - size_vec1.stride = cl_uint(viennacl::traits::stride(vec1)); - size_vec1.size = cl_uint(viennacl::traits::size(vec1)); - size_vec1.internal_size = cl_uint(viennacl::traits::internal_size(vec1)); - - viennacl::ocl::packed_cl_uint size_vec2; - size_vec2.start = cl_uint(viennacl::traits::start(vec2)); - size_vec2.stride = cl_uint(viennacl::traits::stride(vec2)); - size_vec2.size = cl_uint(viennacl::traits::size(vec2)); - size_vec2.internal_size = cl_uint(viennacl::traits::internal_size(vec2)); - - - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec1), - size_vec1, - - viennacl::traits::opencl_handle(viennacl::tools::promote_if_host_scalar<T>(alpha)), - options_alpha, - viennacl::traits::opencl_handle(vec2), - size_vec2 ) - ); -} - - -template <typename T, typename ScalarType1, typename ScalarType2> -void avbv(vector_base<T> & vec1, - vector_base<T> const & vec2, ScalarType1 const & alpha, vcl_size_t len_alpha, bool reciprocal_alpha, bool flip_sign_alpha, - vector_base<T> const & vec3, ScalarType2 const & beta, vcl_size_t len_beta, bool reciprocal_beta, bool flip_sign_beta) -{ - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(vec2).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - assert(viennacl::traits::opencl_handle(vec2).context() == viennacl::traits::opencl_handle(vec3).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - viennacl::linalg::opencl::kernels::vector<T>::init(ctx); - - std::string kernel_name; - if (viennacl::is_cpu_scalar<ScalarType1>::value && viennacl::is_cpu_scalar<ScalarType2>::value) - kernel_name = "avbv_cpu_cpu"; - else if (viennacl::is_cpu_scalar<ScalarType1>::value && !viennacl::is_cpu_scalar<ScalarType2>::value) - kernel_name = "avbv_cpu_gpu"; - else if (!viennacl::is_cpu_scalar<ScalarType1>::value && viennacl::is_cpu_scalar<ScalarType2>::value) - kernel_name = "avbv_gpu_cpu"; - else - kernel_name = "avbv_gpu_gpu"; - - cl_uint options_alpha = detail::make_options(len_alpha, reciprocal_alpha, flip_sign_alpha); - cl_uint options_beta = detail::make_options(len_beta, reciprocal_beta, flip_sign_beta); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), kernel_name); - k.global_work_size(0, std::min<vcl_size_t>(128 * k.local_work_size(), - viennacl::tools::align_to_multiple<vcl_size_t>(viennacl::traits::size(vec1), k.local_work_size()) ) ); - - viennacl::ocl::packed_cl_uint size_vec1; - size_vec1.start = cl_uint(viennacl::traits::start(vec1)); - size_vec1.stride = cl_uint(viennacl::traits::stride(vec1)); - size_vec1.size = cl_uint(viennacl::traits::size(vec1)); - size_vec1.internal_size = cl_uint(viennacl::traits::internal_size(vec1)); - - viennacl::ocl::packed_cl_uint size_vec2; - size_vec2.start = cl_uint(viennacl::traits::start(vec2)); - size_vec2.stride = cl_uint(viennacl::traits::stride(vec2)); - size_vec2.size = cl_uint(viennacl::traits::size(vec2)); - size_vec2.internal_size = cl_uint(viennacl::traits::internal_size(vec2)); - - viennacl::ocl::packed_cl_uint size_vec3; - size_vec3.start = cl_uint(viennacl::traits::start(vec3)); - size_vec3.stride = cl_uint(viennacl::traits::stride(vec3)); - size_vec3.size = cl_uint(viennacl::traits::size(vec3)); - size_vec3.internal_size = cl_uint(viennacl::traits::internal_size(vec3)); - - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec1), - size_vec1, - - viennacl::traits::opencl_handle(viennacl::tools::promote_if_host_scalar<T>(alpha)), - options_alpha, - viennacl::traits::opencl_handle(vec2), - size_vec2, - - viennacl::traits::opencl_handle(viennacl::tools::promote_if_host_scalar<T>(beta)), - options_beta, - viennacl::traits::opencl_handle(vec3), - size_vec3 ) - ); -} - - -template <typename T, typename ScalarType1, typename ScalarType2> -void avbv_v(vector_base<T> & vec1, - vector_base<T> const & vec2, ScalarType1 const & alpha, vcl_size_t len_alpha, bool reciprocal_alpha, bool flip_sign_alpha, - vector_base<T> const & vec3, ScalarType2 const & beta, vcl_size_t len_beta, bool reciprocal_beta, bool flip_sign_beta) -{ - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(vec2).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - assert(viennacl::traits::opencl_handle(vec2).context() == viennacl::traits::opencl_handle(vec3).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - viennacl::linalg::opencl::kernels::vector<T>::init(ctx); - - std::string kernel_name; - if (viennacl::is_cpu_scalar<ScalarType1>::value && viennacl::is_cpu_scalar<ScalarType2>::value) - kernel_name = "avbv_v_cpu_cpu"; - else if (viennacl::is_cpu_scalar<ScalarType1>::value && !viennacl::is_cpu_scalar<ScalarType2>::value) - kernel_name = "avbv_v_cpu_gpu"; - else if (!viennacl::is_cpu_scalar<ScalarType1>::value && viennacl::is_cpu_scalar<ScalarType2>::value) - kernel_name = "avbv_v_gpu_cpu"; - else - kernel_name = "avbv_v_gpu_gpu"; - - cl_uint options_alpha = detail::make_options(len_alpha, reciprocal_alpha, flip_sign_alpha); - cl_uint options_beta = detail::make_options(len_beta, reciprocal_beta, flip_sign_beta); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), kernel_name); - k.global_work_size(0, std::min<vcl_size_t>(128 * k.local_work_size(), - viennacl::tools::align_to_multiple<vcl_size_t>(viennacl::traits::size(vec1), k.local_work_size()) ) ); - - viennacl::ocl::packed_cl_uint size_vec1; - size_vec1.start = cl_uint(viennacl::traits::start(vec1)); - size_vec1.stride = cl_uint(viennacl::traits::stride(vec1)); - size_vec1.size = cl_uint(viennacl::traits::size(vec1)); - size_vec1.internal_size = cl_uint(viennacl::traits::internal_size(vec1)); - - viennacl::ocl::packed_cl_uint size_vec2; - size_vec2.start = cl_uint(viennacl::traits::start(vec2)); - size_vec2.stride = cl_uint(viennacl::traits::stride(vec2)); - size_vec2.size = cl_uint(viennacl::traits::size(vec2)); - size_vec2.internal_size = cl_uint(viennacl::traits::internal_size(vec2)); - - viennacl::ocl::packed_cl_uint size_vec3; - size_vec3.start = cl_uint(viennacl::traits::start(vec3)); - size_vec3.stride = cl_uint(viennacl::traits::stride(vec3)); - size_vec3.size = cl_uint(viennacl::traits::size(vec3)); - size_vec3.internal_size = cl_uint(viennacl::traits::internal_size(vec3)); - - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec1), - size_vec1, - - viennacl::traits::opencl_handle(viennacl::tools::promote_if_host_scalar<T>(alpha)), - options_alpha, - viennacl::traits::opencl_handle(vec2), - size_vec2, - - viennacl::traits::opencl_handle(viennacl::tools::promote_if_host_scalar<T>(beta)), - options_beta, - viennacl::traits::opencl_handle(vec3), - size_vec3 ) - ); -} - - -/** @brief Assign a constant value to a vector (-range/-slice) -* -* @param vec1 The vector to which the value should be assigned -* @param alpha The value to be assigned -* @param up_to_internal_size Specifies whether alpha should also be written to padded memory (mostly used for clearing the whole buffer). -*/ -template <typename T> -void vector_assign(vector_base<T> & vec1, const T & alpha, bool up_to_internal_size = false) -{ - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - viennacl::linalg::opencl::kernels::vector<T>::init(ctx); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), "assign_cpu"); - k.global_work_size(0, std::min<vcl_size_t>(128 * k.local_work_size(), - viennacl::tools::align_to_multiple<vcl_size_t>(viennacl::traits::size(vec1), k.local_work_size()) ) ); - - cl_uint size = up_to_internal_size ? cl_uint(vec1.internal_size()) : cl_uint(viennacl::traits::size(vec1)); - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec1), - cl_uint(viennacl::traits::start(vec1)), - cl_uint(viennacl::traits::stride(vec1)), - size, - cl_uint(vec1.internal_size()), //Note: Do NOT use traits::internal_size() here, because vector proxies don't require padding. - viennacl::traits::opencl_handle(T(alpha)) ) - ); -} - - -/** @brief Swaps the contents of two vectors, data is copied -* -* @param vec1 The first vector (or -range, or -slice) -* @param vec2 The second vector (or -range, or -slice) -*/ -template <typename T> -void vector_swap(vector_base<T> & vec1, vector_base<T> & vec2) -{ - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(vec2).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - viennacl::linalg::opencl::kernels::vector<T>::init(ctx); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), "swap"); - - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec1), - cl_uint(viennacl::traits::start(vec1)), - cl_uint(viennacl::traits::stride(vec1)), - cl_uint(viennacl::traits::size(vec1)), - viennacl::traits::opencl_handle(vec2), - cl_uint(viennacl::traits::start(vec2)), - cl_uint(viennacl::traits::stride(vec2)), - cl_uint(viennacl::traits::size(vec2))) - ); -} - -///////////////////////// Binary Elementwise operations ///////////// - -/** @brief Implementation of the element-wise operation v1 = v2 .* v3 and v1 = v2 ./ v3 (using MATLAB syntax) -* -* @param vec1 The result vector (or -range, or -slice) -* @param proxy The proxy object holding v2, v3 and the operation -*/ -template <typename T, typename OP> -void element_op(vector_base<T> & vec1, - vector_expression<const vector_base<T>, const vector_base<T>, op_element_binary<OP> > const & proxy) -{ - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(proxy.lhs()).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(proxy.rhs()).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - viennacl::linalg::opencl::kernels::vector_element<T>::init(ctx); - - std::string kernel_name = "element_pow"; - cl_uint op_type = 2; //0: product, 1: division, 2: power - if (viennacl::is_division<OP>::value) - { - op_type = 1; - kernel_name = "element_div"; - } - else if (viennacl::is_product<OP>::value) - { - op_type = 0; - kernel_name = "element_prod"; - } - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector_element<T>::program_name(), kernel_name); - - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec1), - cl_uint(viennacl::traits::start(vec1)), - cl_uint(viennacl::traits::stride(vec1)), - cl_uint(viennacl::traits::size(vec1)), - - viennacl::traits::opencl_handle(proxy.lhs()), - cl_uint(viennacl::traits::start(proxy.lhs())), - cl_uint(viennacl::traits::stride(proxy.lhs())), - - viennacl::traits::opencl_handle(proxy.rhs()), - cl_uint(viennacl::traits::start(proxy.rhs())), - cl_uint(viennacl::traits::stride(proxy.rhs())), - - op_type) - ); -} - -///////////////////////// Unary Elementwise operations ///////////// - -/** @brief Implementation of unary element-wise operations v1 = OP(v2) -* -* @param vec1 The result vector (or -range, or -slice) -* @param proxy The proxy object holding v2 and the operation -*/ -template <typename T, typename OP> -void element_op(vector_base<T> & vec1, - vector_expression<const vector_base<T>, const vector_base<T>, op_element_unary<OP> > const & proxy) -{ - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(proxy.lhs()).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(proxy.rhs()).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - viennacl::linalg::opencl::kernels::vector_element<T>::init(ctx); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector_element<T>::program_name(), detail::op_to_string(OP()) + "_assign"); - - viennacl::ocl::packed_cl_uint size_vec1; - size_vec1.start = cl_uint(viennacl::traits::start(vec1)); - size_vec1.stride = cl_uint(viennacl::traits::stride(vec1)); - size_vec1.size = cl_uint(viennacl::traits::size(vec1)); - size_vec1.internal_size = cl_uint(viennacl::traits::internal_size(vec1)); - - viennacl::ocl::packed_cl_uint size_vec2; - size_vec2.start = cl_uint(viennacl::traits::start(proxy.lhs())); - size_vec2.stride = cl_uint(viennacl::traits::stride(proxy.lhs())); - size_vec2.size = cl_uint(viennacl::traits::size(proxy.lhs())); - size_vec2.internal_size = cl_uint(viennacl::traits::internal_size(proxy.lhs())); - - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec1), - size_vec1, - viennacl::traits::opencl_handle(proxy.lhs()), - size_vec2) - ); -} - -///////////////////////// Norms and inner product /////////////////// - -/** @brief Computes the partial inner product of two vectors - implementation. Library users should call inner_prod(vec1, vec2). -* -* @param vec1 The first vector -* @param vec2 The second vector -* @param partial_result The results of each group -*/ -template <typename T> -void inner_prod_impl(vector_base<T> const & vec1, - vector_base<T> const & vec2, - vector_base<T> & partial_result) -{ - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(vec2).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - assert(viennacl::traits::opencl_handle(vec2).context() == viennacl::traits::opencl_handle(partial_result).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - viennacl::linalg::opencl::kernels::vector<T>::init(ctx); - - assert( (viennacl::traits::size(vec1) == viennacl::traits::size(vec2)) - && bool("Incompatible vector sizes in inner_prod_impl()!")); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), "inner_prod1"); - - assert( (k.global_work_size() / k.local_work_size() <= partial_result.size()) && bool("Size mismatch for partial reduction in inner_prod_impl()") ); - - viennacl::ocl::packed_cl_uint size_vec1; - size_vec1.start = cl_uint(viennacl::traits::start(vec1)); - size_vec1.stride = cl_uint(viennacl::traits::stride(vec1)); - size_vec1.size = cl_uint(viennacl::traits::size(vec1)); - size_vec1.internal_size = cl_uint(viennacl::traits::internal_size(vec1)); - - viennacl::ocl::packed_cl_uint size_vec2; - size_vec2.start = cl_uint(viennacl::traits::start(vec2)); - size_vec2.stride = cl_uint(viennacl::traits::stride(vec2)); - size_vec2.size = cl_uint(viennacl::traits::size(vec2)); - size_vec2.internal_size = cl_uint(viennacl::traits::internal_size(vec2)); - - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec1), - size_vec1, - viennacl::traits::opencl_handle(vec2), - size_vec2, - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<T>::type) * k.local_work_size()), - viennacl::traits::opencl_handle(partial_result) - ) - ); -} - - -//implementation of inner product: -//namespace { -/** @brief Computes the inner product of two vectors - implementation. Library users should call inner_prod(vec1, vec2). -* -* @param vec1 The first vector -* @param vec2 The second vector -* @param result The result scalar (on the gpu) -*/ -template <typename T> -void inner_prod_impl(vector_base<T> const & vec1, - vector_base<T> const & vec2, - scalar<T> & result) -{ - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(vec2).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(result).context() && bool("Operands do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - - vcl_size_t work_groups = 128; - viennacl::vector<T> temp(work_groups, viennacl::traits::context(vec1)); - temp.resize(work_groups, ctx); // bring default-constructed vectors to the correct size: - - // Step 1: Compute partial inner products for each work group: - inner_prod_impl(vec1, vec2, temp); - - // Step 2: Sum partial results: - viennacl::ocl::kernel & ksum = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), "sum"); - - ksum.global_work_size(0, ksum.local_work_size(0)); - viennacl::ocl::enqueue(ksum(viennacl::traits::opencl_handle(temp), - cl_uint(viennacl::traits::start(temp)), - cl_uint(viennacl::traits::stride(temp)), - cl_uint(viennacl::traits::size(temp)), - cl_uint(1), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<T>::type) * ksum.local_work_size()), - viennacl::traits::opencl_handle(result) ) - ); -} - -namespace detail -{ - template<typename NumericT> - viennacl::ocl::packed_cl_uint make_layout(vector_base<NumericT> const & vec) - { - viennacl::ocl::packed_cl_uint ret; - ret.start = cl_uint(viennacl::traits::start(vec)); - ret.stride = cl_uint(viennacl::traits::stride(vec)); - ret.size = cl_uint(viennacl::traits::size(vec)); - ret.internal_size = cl_uint(viennacl::traits::internal_size(vec)); - return ret; - } -} - -/** @brief Computes multiple inner products where one argument is common to all inner products. <x, y1>, <x, y2>, ..., <x, yN> -* -* @param x The common vector -* @param vec_tuple The tuple of vectors y1, y2, ..., yN -* @param result The result vector -*/ -template <typename NumericT> -void inner_prod_impl(vector_base<NumericT> const & x, - vector_tuple<NumericT> const & vec_tuple, - vector_base<NumericT> & result) -{ - assert(viennacl::traits::opencl_handle(x).context() == viennacl::traits::opencl_handle(result).context() && bool("Operands do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(x).context()); - viennacl::linalg::opencl::kernels::vector<NumericT>::init(ctx); - viennacl::linalg::opencl::kernels::vector_multi_inner_prod<NumericT>::init(ctx); - - viennacl::ocl::packed_cl_uint layout_x = detail::make_layout(x); - - viennacl::ocl::kernel & ksum = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector_multi_inner_prod<NumericT>::program_name(), "sum_inner_prod"); - viennacl::ocl::kernel & inner_prod_kernel_1 = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<NumericT>::program_name(), "inner_prod1"); - viennacl::ocl::kernel & inner_prod_kernel_2 = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector_multi_inner_prod<NumericT>::program_name(), "inner_prod2"); - viennacl::ocl::kernel & inner_prod_kernel_3 = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector_multi_inner_prod<NumericT>::program_name(), "inner_prod3"); - viennacl::ocl::kernel & inner_prod_kernel_4 = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector_multi_inner_prod<NumericT>::program_name(), "inner_prod4"); - viennacl::ocl::kernel & inner_prod_kernel_8 = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector_multi_inner_prod<NumericT>::program_name(), "inner_prod8"); - - vcl_size_t work_groups = inner_prod_kernel_8.global_work_size(0) / inner_prod_kernel_8.local_work_size(0); - viennacl::vector<NumericT> temp(8 * work_groups, viennacl::traits::context(x)); - - vcl_size_t current_index = 0; - while (current_index < vec_tuple.const_size()) - { - switch (vec_tuple.const_size() - current_index) - { - case 7: - case 6: - case 5: - case 4: - { - vector_base<NumericT> const & y0 = vec_tuple.const_at(current_index ); - vector_base<NumericT> const & y1 = vec_tuple.const_at(current_index + 1); - vector_base<NumericT> const & y2 = vec_tuple.const_at(current_index + 2); - vector_base<NumericT> const & y3 = vec_tuple.const_at(current_index + 3); - viennacl::ocl::enqueue(inner_prod_kernel_4( viennacl::traits::opencl_handle(x), layout_x, - viennacl::traits::opencl_handle(y0), detail::make_layout(y0), - viennacl::traits::opencl_handle(y1), detail::make_layout(y1), - viennacl::traits::opencl_handle(y2), detail::make_layout(y2), - viennacl::traits::opencl_handle(y3), detail::make_layout(y3), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * 4 * inner_prod_kernel_4.local_work_size()), - viennacl::traits::opencl_handle(temp) - ) ); - - ksum.global_work_size(0, 4 * ksum.local_work_size(0)); - viennacl::ocl::enqueue(ksum(viennacl::traits::opencl_handle(temp), - cl_uint(work_groups), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * 4 * ksum.local_work_size()), - viennacl::traits::opencl_handle(result), - cl_uint(viennacl::traits::start(result) + current_index * viennacl::traits::stride(result)), - cl_uint(viennacl::traits::stride(result)) - ) - ); - } - current_index += 4; - break; - - case 3: - { - vector_base<NumericT> const & y0 = vec_tuple.const_at(current_index ); - vector_base<NumericT> const & y1 = vec_tuple.const_at(current_index + 1); - vector_base<NumericT> const & y2 = vec_tuple.const_at(current_index + 2); - viennacl::ocl::enqueue(inner_prod_kernel_3( viennacl::traits::opencl_handle(x), layout_x, - viennacl::traits::opencl_handle(y0), detail::make_layout(y0), - viennacl::traits::opencl_handle(y1), detail::make_layout(y1), - viennacl::traits::opencl_handle(y2), detail::make_layout(y2), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * 3 * inner_prod_kernel_3.local_work_size()), - viennacl::traits::opencl_handle(temp) - ) ); - - ksum.global_work_size(0, 3 * ksum.local_work_size(0)); - viennacl::ocl::enqueue(ksum(viennacl::traits::opencl_handle(temp), - cl_uint(work_groups), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * 3 * ksum.local_work_size()), - viennacl::traits::opencl_handle(result), - cl_uint(viennacl::traits::start(result) + current_index * viennacl::traits::stride(result)), - cl_uint(viennacl::traits::stride(result)) - ) - ); - } - current_index += 3; - break; - - case 2: - { - vector_base<NumericT> const & y0 = vec_tuple.const_at(current_index ); - vector_base<NumericT> const & y1 = vec_tuple.const_at(current_index + 1); - viennacl::ocl::enqueue(inner_prod_kernel_2( viennacl::traits::opencl_handle(x), layout_x, - viennacl::traits::opencl_handle(y0), detail::make_layout(y0), - viennacl::traits::opencl_handle(y1), detail::make_layout(y1), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * 2 * inner_prod_kernel_2.local_work_size()), - viennacl::traits::opencl_handle(temp) - ) ); - - ksum.global_work_size(0, 2 * ksum.local_work_size(0)); - viennacl::ocl::enqueue(ksum(viennacl::traits::opencl_handle(temp), - cl_uint(work_groups), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * 2 * ksum.local_work_size()), - viennacl::traits::opencl_handle(result), - cl_uint(viennacl::traits::start(result) + current_index * viennacl::traits::stride(result)), - cl_uint(viennacl::traits::stride(result)) - ) - ); - } - current_index += 2; - break; - - case 1: - { - vector_base<NumericT> const & y0 = vec_tuple.const_at(current_index ); - viennacl::ocl::enqueue(inner_prod_kernel_1( viennacl::traits::opencl_handle(x), layout_x, - viennacl::traits::opencl_handle(y0), detail::make_layout(y0), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * 1 * inner_prod_kernel_1.local_work_size()), - viennacl::traits::opencl_handle(temp) - ) ); - - ksum.global_work_size(0, 1 * ksum.local_work_size(0)); - viennacl::ocl::enqueue(ksum(viennacl::traits::opencl_handle(temp), - cl_uint(work_groups), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * 1 * ksum.local_work_size()), - viennacl::traits::opencl_handle(result), - cl_uint(viennacl::traits::start(result) + current_index * viennacl::traits::stride(result)), - cl_uint(viennacl::traits::stride(result)) - ) - ); - } - current_index += 1; - break; - - default: //8 or more vectors - { - vector_base<NumericT> const & y0 = vec_tuple.const_at(current_index ); - vector_base<NumericT> const & y1 = vec_tuple.const_at(current_index + 1); - vector_base<NumericT> const & y2 = vec_tuple.const_at(current_index + 2); - vector_base<NumericT> const & y3 = vec_tuple.const_at(current_index + 3); - vector_base<NumericT> const & y4 = vec_tuple.const_at(current_index + 4); - vector_base<NumericT> const & y5 = vec_tuple.const_at(current_index + 5); - vector_base<NumericT> const & y6 = vec_tuple.const_at(current_index + 6); - vector_base<NumericT> const & y7 = vec_tuple.const_at(current_index + 7); - viennacl::ocl::enqueue(inner_prod_kernel_8( viennacl::traits::opencl_handle(x), layout_x, - viennacl::traits::opencl_handle(y0), detail::make_layout(y0), - viennacl::traits::opencl_handle(y1), detail::make_layout(y1), - viennacl::traits::opencl_handle(y2), detail::make_layout(y2), - viennacl::traits::opencl_handle(y3), detail::make_layout(y3), - viennacl::traits::opencl_handle(y4), detail::make_layout(y4), - viennacl::traits::opencl_handle(y5), detail::make_layout(y5), - viennacl::traits::opencl_handle(y6), detail::make_layout(y6), - viennacl::traits::opencl_handle(y7), detail::make_layout(y7), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * 8 * inner_prod_kernel_8.local_work_size()), - viennacl::traits::opencl_handle(temp) - ) ); - - ksum.global_work_size(0, 8 * ksum.local_work_size(0)); - viennacl::ocl::enqueue(ksum(viennacl::traits::opencl_handle(temp), - cl_uint(work_groups), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * 8 * ksum.local_work_size()), - viennacl::traits::opencl_handle(result), - cl_uint(viennacl::traits::start(result) + current_index * viennacl::traits::stride(result)), - cl_uint(viennacl::traits::stride(result)) - ) - ); - } - current_index += 8; - break; - } - } - -} - - - -//implementation of inner product: -//namespace { -/** @brief Computes the inner product of two vectors - implementation. Library users should call inner_prod(vec1, vec2). -* -* @param vec1 The first vector -* @param vec2 The second vector -* @param result The result scalar (on the gpu) -*/ -template <typename T> -void inner_prod_cpu(vector_base<T> const & vec1, - vector_base<T> const & vec2, - T & result) -{ - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(vec2).context() && bool("Vectors do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - - vcl_size_t work_groups = 128; - viennacl::vector<T> temp(work_groups, viennacl::traits::context(vec1)); - temp.resize(work_groups, ctx); // bring default-constructed vectors to the correct size: - - // Step 1: Compute partial inner products for each work group: - inner_prod_impl(vec1, vec2, temp); - - // Step 2: Sum partial results: - - // Now copy partial results from GPU back to CPU and run reduction there: - std::vector<T> temp_cpu(work_groups); - viennacl::fast_copy(temp.begin(), temp.end(), temp_cpu.begin()); - - result = 0; - for (typename std::vector<T>::const_iterator it = temp_cpu.begin(); it != temp_cpu.end(); ++it) - result += *it; -} - - -//////////// Helper for norms - -/** @brief Computes the partial work group results for vector norms -* -* @param vec The vector -* @param partial_result The result scalar -* @param norm_id Norm selector. 0: norm_inf, 1: norm_1, 2: norm_2 -*/ -template <typename T> -void norm_reduction_impl(vector_base<T> const & vec, - vector_base<T> & partial_result, - cl_uint norm_id) -{ - assert(viennacl::traits::opencl_handle(vec).context() == viennacl::traits::opencl_handle(partial_result).context() && bool("Operands do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec).context()); - viennacl::linalg::opencl::kernels::vector<T>::init(ctx); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), "norm"); - - assert( (k.global_work_size() / k.local_work_size() <= partial_result.size()) && bool("Size mismatch for partial reduction in norm_reduction_impl()") ); - - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec), - cl_uint(viennacl::traits::start(vec)), - cl_uint(viennacl::traits::stride(vec)), - cl_uint(viennacl::traits::size(vec)), - cl_uint(norm_id), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<T>::type) * k.local_work_size()), - viennacl::traits::opencl_handle(partial_result) ) - ); -} - - -//////////// Norm 1 - -/** @brief Computes the l^1-norm of a vector -* -* @param vec The vector -* @param result The result scalar -*/ -template <typename T> -void norm_1_impl(vector_base<T> const & vec, - scalar<T> & result) -{ - assert(viennacl::traits::opencl_handle(vec).context() == viennacl::traits::opencl_handle(result).context() && bool("Operands do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec).context()); - - vcl_size_t work_groups = 128; - viennacl::vector<T> temp(work_groups, viennacl::traits::context(vec)); - - // Step 1: Compute the partial work group results - norm_reduction_impl(vec, temp, 1); - - // Step 2: Compute the partial reduction using OpenCL - viennacl::ocl::kernel & ksum = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), "sum"); - - ksum.global_work_size(0, ksum.local_work_size(0)); - viennacl::ocl::enqueue(ksum(viennacl::traits::opencl_handle(temp), - cl_uint(viennacl::traits::start(temp)), - cl_uint(viennacl::traits::stride(temp)), - cl_uint(viennacl::traits::size(temp)), - cl_uint(1), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<T>::type) * ksum.local_work_size()), - result) - ); -} - -/** @brief Computes the l^1-norm of a vector with final reduction on CPU -* -* @param vec The vector -* @param result The result scalar -*/ -template <typename T> -void norm_1_cpu(vector_base<T> const & vec, - T & result) -{ - vcl_size_t work_groups = 128; - viennacl::vector<T> temp(work_groups, viennacl::traits::context(vec)); - - // Step 1: Compute the partial work group results - norm_reduction_impl(vec, temp, 1); - - // Step 2: Now copy partial results from GPU back to CPU and run reduction there: - typedef std::vector<typename viennacl::result_of::cl_type<T>::type> CPUVectorType; - - CPUVectorType temp_cpu(work_groups); - viennacl::fast_copy(temp.begin(), temp.end(), temp_cpu.begin()); - - result = 0; - for (typename CPUVectorType::const_iterator it = temp_cpu.begin(); it != temp_cpu.end(); ++it) - result += static_cast<T>(*it); -} - - - -//////// Norm 2 - - -/** @brief Computes the l^2-norm of a vector - implementation using OpenCL summation at second step -* -* @param vec The vector -* @param result The result scalar -*/ -template <typename T> -void norm_2_impl(vector_base<T> const & vec, - scalar<T> & result) -{ - assert(viennacl::traits::opencl_handle(vec).context() == viennacl::traits::opencl_handle(result).context() && bool("Operands do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec).context()); - - vcl_size_t work_groups = 128; - viennacl::vector<T> temp(work_groups, viennacl::traits::context(vec)); - - // Step 1: Compute the partial work group results - norm_reduction_impl(vec, temp, 2); - - // Step 2: Reduction via OpenCL - viennacl::ocl::kernel & ksum = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), "sum"); - - ksum.global_work_size(0, ksum.local_work_size(0)); - viennacl::ocl::enqueue( ksum(viennacl::traits::opencl_handle(temp), - cl_uint(viennacl::traits::start(temp)), - cl_uint(viennacl::traits::stride(temp)), - cl_uint(viennacl::traits::size(temp)), - cl_uint(2), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<T>::type) * ksum.local_work_size()), - result) - ); -} - -/** @brief Computes the l^1-norm of a vector with final reduction on CPU -* -* @param vec The vector -* @param result The result scalar -*/ -template <typename T> -void norm_2_cpu(vector_base<T> const & vec, - T & result) -{ - vcl_size_t work_groups = 128; - viennacl::vector<T> temp(work_groups, viennacl::traits::context(vec)); - - // Step 1: Compute the partial work group results - norm_reduction_impl(vec, temp, 2); - - // Step 2: Now copy partial results from GPU back to CPU and run reduction there: - typedef std::vector<typename viennacl::result_of::cl_type<T>::type> CPUVectorType; - - CPUVectorType temp_cpu(work_groups); - viennacl::fast_copy(temp.begin(), temp.end(), temp_cpu.begin()); - - result = 0; - for (typename CPUVectorType::const_iterator it = temp_cpu.begin(); it != temp_cpu.end(); ++it) - result += static_cast<T>(*it); - result = std::sqrt(result); -} - - - -////////// Norm inf - -/** @brief Computes the supremum-norm of a vector -* -* @param vec The vector -* @param result The result scalar -*/ -template <typename T> -void norm_inf_impl(vector_base<T> const & vec, - scalar<T> & result) -{ - assert(viennacl::traits::opencl_handle(vec).context() == viennacl::traits::opencl_handle(result).context() && bool("Operands do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec).context()); - - vcl_size_t work_groups = 128; - viennacl::vector<T> temp(work_groups, viennacl::traits::context(vec)); - - // Step 1: Compute the partial work group results - norm_reduction_impl(vec, temp, 0); - - //part 2: parallel reduction of reduced kernel: - viennacl::ocl::kernel & ksum = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), "sum"); - - ksum.global_work_size(0, ksum.local_work_size(0)); - viennacl::ocl::enqueue( ksum(viennacl::traits::opencl_handle(temp), - cl_uint(viennacl::traits::start(temp)), - cl_uint(viennacl::traits::stride(temp)), - cl_uint(viennacl::traits::size(temp)), - cl_uint(0), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<T>::type) * ksum.local_work_size()), - result) - ); -} - -/** @brief Computes the supremum-norm of a vector -* -* @param vec The vector -* @param result The result scalar -*/ -template <typename T> -void norm_inf_cpu(vector_base<T> const & vec, - T & result) -{ - vcl_size_t work_groups = 128; - viennacl::vector<T> temp(work_groups, viennacl::traits::context(vec)); - - // Step 1: Compute the partial work group results - norm_reduction_impl(vec, temp, 0); - - // Step 2: Now copy partial results from GPU back to CPU and run reduction there: - typedef std::vector<typename viennacl::result_of::cl_type<T>::type> CPUVectorType; - - CPUVectorType temp_cpu(work_groups); - viennacl::fast_copy(temp.begin(), temp.end(), temp_cpu.begin()); - - result = 0; - for (typename CPUVectorType::const_iterator it = temp_cpu.begin(); it != temp_cpu.end(); ++it) - result = std::max(result, static_cast<T>(*it)); -} - - -/////////// index norm_inf - -//This function should return a CPU scalar, otherwise statements like -// vcl_rhs[index_norm_inf(vcl_rhs)] -// are ambiguous -/** @brief Computes the index of the first entry that is equal to the supremum-norm in modulus. -* -* @param vec The vector -* @return The result. Note that the result must be a CPU scalar (unsigned int), since gpu scalars are floating point types. -*/ -template <typename T> -cl_uint index_norm_inf(vector_base<T> const & vec) -{ - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec).context()); - viennacl::linalg::opencl::kernels::vector<T>::init(ctx); - - viennacl::ocl::handle<cl_mem> h = ctx.create_memory(CL_MEM_READ_WRITE, sizeof(cl_uint)); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), "index_norm_inf"); - //cl_uint size = static_cast<cl_uint>(vcl_vec.internal_size()); - - //TODO: Use multi-group kernel for large vector sizes - - k.global_work_size(0, k.local_work_size()); - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec), - cl_uint(viennacl::traits::start(vec)), - cl_uint(viennacl::traits::stride(vec)), - cl_uint(viennacl::traits::size(vec)), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<T>::type) * k.local_work_size()), - viennacl::ocl::local_mem(sizeof(cl_uint) * k.local_work_size()), h)); - - //read value: - cl_uint result; - cl_int err = clEnqueueReadBuffer(ctx.get_queue().handle().get(), h.get(), CL_TRUE, 0, sizeof(cl_uint), &result, 0, NULL, NULL); - VIENNACL_ERR_CHECK(err); - return result; -} - - -////////// max - -/** @brief Computes the maximum value of a vector, where the result is stored in an OpenCL buffer. -* -* @param x The vector -* @param result The result scalar -*/ -template<typename NumericT> -void max_impl(vector_base<NumericT> const & x, - scalar<NumericT> & result) -{ - assert(viennacl::traits::opencl_handle(x).context() == viennacl::traits::opencl_handle(result).context() && bool("Operands do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(x).context()); - viennacl::linalg::opencl::kernels::vector<NumericT>::init(ctx); - - vcl_size_t work_groups = 128; - viennacl::vector<NumericT> temp(work_groups, viennacl::traits::context(x)); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<NumericT>::program_name(), "max_kernel"); - - k.global_work_size(0, work_groups * k.local_work_size(0)); - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(x), - cl_uint(viennacl::traits::start(x)), - cl_uint(viennacl::traits::stride(x)), - cl_uint(viennacl::traits::size(x)), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * k.local_work_size()), - viennacl::traits::opencl_handle(temp) - )); - - k.global_work_size(0, k.local_work_size()); - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(temp), - cl_uint(viennacl::traits::start(temp)), - cl_uint(viennacl::traits::stride(temp)), - cl_uint(viennacl::traits::size(temp)), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * k.local_work_size()), - viennacl::traits::opencl_handle(result) - )); -} - -/** @brief Computes the maximum value of a vector, where the value is stored in a host value. -* -* @param x The vector -* @param result The result scalar -*/ -template<typename NumericT> -void max_cpu(vector_base<NumericT> const & x, - NumericT & result) -{ - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(x).context()); - viennacl::linalg::opencl::kernels::vector<NumericT>::init(ctx); - - vcl_size_t work_groups = 128; - viennacl::vector<NumericT> temp(work_groups, viennacl::traits::context(x)); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<NumericT>::program_name(), "max_kernel"); - - k.global_work_size(0, work_groups * k.local_work_size(0)); - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(x), - cl_uint(viennacl::traits::start(x)), - cl_uint(viennacl::traits::stride(x)), - cl_uint(viennacl::traits::size(x)), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * k.local_work_size()), - viennacl::traits::opencl_handle(temp) - )); - - // Step 2: Now copy partial results from GPU back to CPU and run reduction there: - typedef std::vector<typename viennacl::result_of::cl_type<NumericT>::type> CPUVectorType; - - CPUVectorType temp_cpu(work_groups); - viennacl::fast_copy(temp.begin(), temp.end(), temp_cpu.begin()); - - result = static_cast<NumericT>(temp_cpu[0]); - for (typename CPUVectorType::const_iterator it = temp_cpu.begin(); it != temp_cpu.end(); ++it) - result = std::max(result, static_cast<NumericT>(*it)); - -} - - -////////// min - -/** @brief Computes the minimum of a vector, where the result is stored in an OpenCL buffer. -* -* @param x The vector -* @param result The result scalar -*/ -template<typename NumericT> -void min_impl(vector_base<NumericT> const & x, - scalar<NumericT> & result) -{ - assert(viennacl::traits::opencl_handle(x).context() == viennacl::traits::opencl_handle(result).context() && bool("Operands do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(x).context()); - viennacl::linalg::opencl::kernels::vector<NumericT>::init(ctx); - - vcl_size_t work_groups = 128; - viennacl::vector<NumericT> temp(work_groups, viennacl::traits::context(x)); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<NumericT>::program_name(), "min_kernel"); - - k.global_work_size(0, work_groups * k.local_work_size(0)); - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(x), - cl_uint(viennacl::traits::start(x)), - cl_uint(viennacl::traits::stride(x)), - cl_uint(viennacl::traits::size(x)), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * k.local_work_size()), - viennacl::traits::opencl_handle(temp) - )); - - k.global_work_size(0, k.local_work_size()); - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(temp), - cl_uint(viennacl::traits::start(temp)), - cl_uint(viennacl::traits::stride(temp)), - cl_uint(viennacl::traits::size(temp)), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * k.local_work_size()), - viennacl::traits::opencl_handle(result) - )); -} - -/** @brief Computes the minimum of a vector, where the result is stored on a CPU scalar. -* -* @param x The vector -* @param result The result scalar -*/ -template<typename NumericT> -void min_cpu(vector_base<NumericT> const & x, - NumericT & result) -{ - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(x).context()); - viennacl::linalg::opencl::kernels::vector<NumericT>::init(ctx); - - vcl_size_t work_groups = 128; - viennacl::vector<NumericT> temp(work_groups, viennacl::traits::context(x)); - - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<NumericT>::program_name(), "min_kernel"); - - k.global_work_size(0, work_groups * k.local_work_size(0)); - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(x), - cl_uint(viennacl::traits::start(x)), - cl_uint(viennacl::traits::stride(x)), - cl_uint(viennacl::traits::size(x)), - viennacl::ocl::local_mem(sizeof(typename viennacl::result_of::cl_type<NumericT>::type) * k.local_work_size()), - viennacl::traits::opencl_handle(temp) - )); - - // Step 2: Now copy partial results from GPU back to CPU and run reduction there: - typedef std::vector<typename viennacl::result_of::cl_type<NumericT>::type> CPUVectorType; - - CPUVectorType temp_cpu(work_groups); - viennacl::fast_copy(temp.begin(), temp.end(), temp_cpu.begin()); - - result = static_cast<NumericT>(temp_cpu[0]); - for (typename CPUVectorType::const_iterator it = temp_cpu.begin(); it != temp_cpu.end(); ++it) - result = std::min(result, static_cast<NumericT>(*it)); -} - -////////// sum - -/** @brief Computes the sum over all entries of a vector -* -* @param x The vector -* @param result The result scalar -*/ -template<typename NumericT> -void sum_impl(vector_base<NumericT> const & x, - scalar<NumericT> & result) -{ - assert(viennacl::traits::opencl_handle(x).context() == viennacl::traits::opencl_handle(result).context() && bool("Operands do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::vector<NumericT> all_ones = viennacl::scalar_vector<NumericT>(x.size(), NumericT(1), viennacl::traits::context(x)); - viennacl::linalg::opencl::inner_prod_impl(x, all_ones, result); -} - -/** @brief Computes the sum over all entries of a vector. -* -* @param x The vector -* @param result The result scalar -*/ -template<typename NumericT> -void sum_cpu(vector_base<NumericT> const & x, NumericT & result) -{ - scalar<NumericT> tmp(0, viennacl::traits::context(x)); - sum_impl(x, tmp); - result = tmp; -} - - -//TODO: Special case vec1 == vec2 allows improvement!! -/** @brief Computes a plane rotation of two vectors. -* -* Computes (x,y) <- (alpha * x + beta * y, -beta * x + alpha * y) -* -* @param vec1 The first vector -* @param vec2 The second vector -* @param alpha The first transformation coefficient -* @param beta The second transformation coefficient -*/ -template <typename T> -void plane_rotation(vector_base<T> & vec1, - vector_base<T> & vec2, - T alpha, T beta) -{ - assert(viennacl::traits::opencl_handle(vec1).context() == viennacl::traits::opencl_handle(vec2).context() && bool("Operands do not reside in the same OpenCL context. Automatic migration not yet supported!")); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(vec1).context()); - viennacl::linalg::opencl::kernels::vector<T>::init(ctx); - - assert(viennacl::traits::size(vec1) == viennacl::traits::size(vec2)); - viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::vector<T>::program_name(), "plane_rotation"); - - viennacl::ocl::enqueue(k(viennacl::traits::opencl_handle(vec1), - cl_uint(viennacl::traits::start(vec1)), - cl_uint(viennacl::traits::stride(vec1)), - cl_uint(viennacl::traits::size(vec1)), - viennacl::traits::opencl_handle(vec2), - cl_uint(viennacl::traits::start(vec2)), - cl_uint(viennacl::traits::stride(vec2)), - cl_uint(viennacl::traits::size(vec2)), - viennacl::traits::opencl_handle(alpha), - viennacl::traits::opencl_handle(beta)) - ); -} - - -////////////////////////// - - -namespace detail -{ - /** @brief Worker routine for scan routines using OpenCL - * - * Note on performance: For non-in-place scans one could optimize away the temporary 'opencl_carries'-array. - * This, however, only provides small savings in the latency-dominated regime, yet would effectively double the amount of code to maintain. - */ - template<typename NumericT> - void scan_impl(vector_base<NumericT> const & input, - vector_base<NumericT> & output, - bool is_inclusive) - { - vcl_size_t local_worksize = 128; - vcl_size_t workgroups = 128; - - viennacl::backend::mem_handle opencl_carries; - viennacl::backend::memory_create(opencl_carries, sizeof(NumericT)*workgroups, viennacl::traits::context(input)); - - viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(input).context()); - viennacl::linalg::opencl::kernels::scan<NumericT>::init(ctx); - viennacl::ocl::kernel& k1 = ctx.get_kernel(viennacl::linalg::opencl::kernels::scan<NumericT>::program_name(), "scan_1"); - viennacl::ocl::kernel& k2 = ctx.get_kernel(viennacl::linalg::opencl::kernels::scan<NumericT>::program_name(), "scan_2"); - viennacl::ocl::kernel& k3 = ctx.get_kernel(viennacl::linalg::opencl::kernels::scan<NumericT>::program_name(), "scan_3"); - - // First step: Scan within each thread group and write carries - k1.local_work_size(0, local_worksize); - k1.global_work_size(0, workgroups * local_worksize); - viennacl::ocl::enqueue(k1( input, cl_uint( input.start()), cl_uint( input.stride()), cl_uint(input.size()), - output, cl_uint(output.start()), cl_uint(output.stride()), - cl_uint(is_inclusive ? 0 : 1), opencl_carries.opencl_handle()) - ); - - // Second step: Compute offset for each thread group (exclusive scan for each thread group) - k2.local_work_size(0, workgroups); - k2.global_work_size(0, workgroups); - viennacl::ocl::enqueue(k2(opencl_carries.opencl_handle())); - - // Third step: Offset each thread group accordingly - k3.local_work_size(0, local_worksize); - k3.global_work_size(0, workgroups * local_worksize); - viennacl::ocl::enqueue(k3(output, cl_uint(output.start()), cl_uint(output.stride()), cl_uint(output.size()), - opencl_carries.opencl_handle()) - ); - } -} - - -/** @brief This function implements an inclusive scan using CUDA. -* -* @param input Input vector. -* @param output The output vector. Either idential to input or non-overlapping. -*/ -template<typename NumericT> -void inclusive_scan(vector_base<NumericT> const & input, - vector_base<NumericT> & output) -{ - detail::scan_impl(input, output, true); -} - - -/** @brief This function implements an exclusive scan using CUDA. -* -* @param input Input vector -* @param output The output vector. Either idential to input or non-overlapping. -*/ -template<typename NumericT> -void exclusive_scan(vector_base<NumericT> const & input, - vector_base<NumericT> & output) -{ - detail::scan_impl(input, output, false); -} - - -} //namespace opencl -} //namespace linalg -} //namespace viennacl - - -#endif
http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/power_iter.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/power_iter.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/power_iter.hpp deleted file mode 100644 index 9721517..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/power_iter.hpp +++ /dev/null @@ -1,129 +0,0 @@ -#ifndef VIENNACL_LINALG_POWER_ITER_HPP_ -#define VIENNACL_LINALG_POWER_ITER_HPP_ - -/* ========================================================================= - Copyright (c) 2010-2016, 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 [email protected] - - (A list of authors and contributors can be found in the manual) - - License: MIT (X11), see file LICENSE in the base directory -============================================================================= */ - -/** @file viennacl/linalg/power_iter.hpp - @brief Defines a tag for the configuration of the power iteration method. - - Contributed by Astrid Rupp. -*/ - -#include <cmath> -#include <vector> -#include "viennacl/linalg/bisect.hpp" -#include "viennacl/linalg/prod.hpp" -#include "viennacl/linalg/norm_2.hpp" - -namespace viennacl -{ - namespace linalg - { - /** @brief A tag for the power iteration algorithm. */ - class power_iter_tag - { - public: - - /** @brief The constructor - * - * @param tfac If the eigenvalue does not change more than this termination factor, the algorithm stops - * @param max_iters Maximum number of iterations for the power iteration - */ - power_iter_tag(double tfac = 1e-8, vcl_size_t max_iters = 50000) : termination_factor_(tfac), max_iterations_(max_iters) {} - - /** @brief Sets the factor for termination */ - void factor(double fct){ termination_factor_ = fct; } - - /** @brief Returns the factor for termination */ - double factor() const { return termination_factor_; } - - vcl_size_t max_iterations() const { return max_iterations_; } - void max_iterations(vcl_size_t new_max) { max_iterations_ = new_max; } - - private: - double termination_factor_; - vcl_size_t max_iterations_; - - }; - - /** - * @brief Implementation of the calculation of the largest eigenvalue (in modulus) and the associated eigenvector using power iteration - * - * @param A The system matrix - * @param tag Tag with termination factor - * @param eigenvec Vector which holds the associated eigenvector once the routine completes - * @return Returns the largest eigenvalue computed by the power iteration method - */ - template<typename MatrixT, typename VectorT > - typename viennacl::result_of::cpu_value_type<typename MatrixT::value_type>::type - eig(MatrixT const& A, power_iter_tag const & tag, VectorT & eigenvec) - { - - typedef typename viennacl::result_of::value_type<MatrixT>::type ScalarType; - typedef typename viennacl::result_of::cpu_value_type<ScalarType>::type CPU_ScalarType; - - vcl_size_t matrix_size = A.size1(); - VectorT r(eigenvec); - std::vector<CPU_ScalarType> s(matrix_size); - - for (vcl_size_t i=0; i<s.size(); ++i) - s[i] = CPU_ScalarType(i % 3) * CPU_ScalarType(0.1234) - CPU_ScalarType(0.5); //'random' starting vector - - detail::copy_vec_to_vec(s, eigenvec); - - double epsilon = tag.factor(); - CPU_ScalarType norm = norm_2(eigenvec); - CPU_ScalarType norm_prev = 0; - long numiter = 0; - - for (vcl_size_t i=0; i<tag.max_iterations(); ++i) - { - if (std::fabs(norm - norm_prev) / std::fabs(norm) < epsilon) - break; - - eigenvec /= norm; - r = viennacl::linalg::prod(A, eigenvec); //using helper vector r for the computation of x <- A * x in order to avoid the repeated creation of temporaries - eigenvec = r; - norm_prev = norm; - norm = norm_2(eigenvec); - numiter++; - } - - return norm; - } - - /** - * @brief Implementation of the calculation of eigenvalues using power iteration. Does not return the eigenvector. - * - * @param A The system matrix - * @param tag Tag with termination factor - * @return Returns the largest eigenvalue computed by the power iteration method - */ - template< typename MatrixT > - typename viennacl::result_of::cpu_value_type<typename MatrixT::value_type>::type - eig(MatrixT const& A, power_iter_tag const & tag) - { - typedef typename viennacl::result_of::vector_for_matrix<MatrixT>::type VectorT; - - VectorT eigenvec(A.size1()); - return eig(A, tag, eigenvec); - } - - } // end namespace linalg -} // end namespace viennacl -#endif http://git-wip-us.apache.org/repos/asf/mahout/blob/7ae549fa/native-viennaCL/src/main/cpp/viennacl/linalg/prod.hpp ---------------------------------------------------------------------- diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/prod.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/prod.hpp deleted file mode 100644 index af041dc..0000000 --- a/native-viennaCL/src/main/cpp/viennacl/linalg/prod.hpp +++ /dev/null @@ -1,370 +0,0 @@ -#ifndef VIENNACL_LINALG_PROD_HPP_ -#define VIENNACL_LINALG_PROD_HPP_ - -/* ========================================================================= - Copyright (c) 2010-2016, 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 [email protected] - - (A list of authors and contributors can be found in the manual) - - License: MIT (X11), see file LICENSE in the base directory -============================================================================= */ - -/** @file viennacl/linalg/prod.hpp - @brief Generic interface for matrix-vector and matrix-matrix products. - See viennacl/linalg/vector_operations.hpp, viennacl/linalg/matrix_operations.hpp, and - viennacl/linalg/sparse_matrix_operations.hpp for implementations. -*/ - -#include "viennacl/forwards.h" -#include "viennacl/tools/tools.hpp" -#include "viennacl/meta/enable_if.hpp" -#include "viennacl/meta/tag_of.hpp" -#include <vector> -#include <map> - -namespace viennacl -{ - // - // generic prod function - // uses tag dispatch to identify which algorithm - // should be called - // - namespace linalg - { - #ifdef VIENNACL_WITH_MTL4 - // ---------------------------------------------------- - // mtl4 - // - template< typename MatrixT, typename VectorT > - typename viennacl::enable_if< viennacl::is_mtl4< typename viennacl::traits::tag_of< MatrixT >::type >::value, - VectorT>::type - prod(MatrixT const& matrix, VectorT const& vector) - { - return VectorT(matrix * vector); - } - #endif - - #ifdef VIENNACL_WITH_ARMADILLO - // ---------------------------------------------------- - // Armadillo - // - template<typename NumericT, typename VectorT> - VectorT prod(arma::SpMat<NumericT> const& A, VectorT const& vector) - { - return A * vector; - } - #endif - - #ifdef VIENNACL_WITH_EIGEN - // ---------------------------------------------------- - // Eigen - // - template< typename MatrixT, typename VectorT > - typename viennacl::enable_if< viennacl::is_eigen< typename viennacl::traits::tag_of< MatrixT >::type >::value, - VectorT>::type - prod(MatrixT const& matrix, VectorT const& vector) - { - return matrix * vector; - } - #endif - - #ifdef VIENNACL_WITH_UBLAS - // ---------------------------------------------------- - // UBLAS - // - template< typename MatrixT, typename VectorT > - typename viennacl::enable_if< viennacl::is_ublas< typename viennacl::traits::tag_of< MatrixT >::type >::value, - VectorT>::type - prod(MatrixT const& matrix, VectorT const& vector) - { - // std::cout << "ublas .. " << std::endl; - return boost::numeric::ublas::prod(matrix, vector); - } - #endif - - - // ---------------------------------------------------- - // STL type - // - - // dense matrix-vector product: - template< typename T, typename A1, typename A2, typename VectorT > - VectorT - prod(std::vector< std::vector<T, A1>, A2 > const & matrix, VectorT const& vector) - { - VectorT result(matrix.size()); - for (typename std::vector<T, A1>::size_type i=0; i<matrix.size(); ++i) - { - result[i] = 0; //we will not assume that VectorT is initialized to zero - for (typename std::vector<T, A1>::size_type j=0; j<matrix[i].size(); ++j) - result[i] += matrix[i][j] * vector[j]; - } - return result; - } - - // sparse matrix-vector product: - template< typename KEY, typename DATA, typename COMPARE, typename AMAP, typename AVEC, typename VectorT > - VectorT - prod(std::vector< std::map<KEY, DATA, COMPARE, AMAP>, AVEC > const& matrix, VectorT const& vector) - { - typedef std::vector< std::map<KEY, DATA, COMPARE, AMAP>, AVEC > MatrixType; - - VectorT result(matrix.size()); - for (typename MatrixType::size_type i=0; i<matrix.size(); ++i) - { - result[i] = 0; //we will not assume that VectorT is initialized to zero - for (typename std::map<KEY, DATA, COMPARE, AMAP>::const_iterator row_entries = matrix[i].begin(); - row_entries != matrix[i].end(); - ++row_entries) - result[i] += row_entries->second * vector[row_entries->first]; - } - return result; - } - - - /*template< typename MatrixT, typename VectorT > - VectorT - prod(MatrixT const& matrix, VectorT const& vector, - typename viennacl::enable_if< viennacl::is_stl< typename viennacl::traits::tag_of< MatrixT >::type >::value - >::type* dummy = 0) - { - // std::cout << "std .. " << std::endl; - return prod_impl(matrix, vector); - }*/ - - // ---------------------------------------------------- - // VIENNACL - // - - // standard product: - template<typename NumericT> - viennacl::matrix_expression< const viennacl::matrix_base<NumericT>, - const viennacl::matrix_base<NumericT>, - viennacl::op_mat_mat_prod > - prod(viennacl::matrix_base<NumericT> const & A, - viennacl::matrix_base<NumericT> const & B) - { - return viennacl::matrix_expression< const viennacl::matrix_base<NumericT>, - const viennacl::matrix_base<NumericT>, - viennacl::op_mat_mat_prod >(A, B); - } - - // right factor is a matrix expression: - template<typename NumericT, typename LhsT, typename RhsT, typename OpT> - viennacl::matrix_expression< const viennacl::matrix_base<NumericT>, - const viennacl::matrix_expression<const LhsT, const RhsT, OpT>, - viennacl::op_mat_mat_prod > - prod(viennacl::matrix_base<NumericT> const & A, - viennacl::matrix_expression<const LhsT, const RhsT, OpT> const & B) - { - return viennacl::matrix_expression< const viennacl::matrix_base<NumericT>, - const viennacl::matrix_expression<const LhsT, const RhsT, OpT>, - viennacl::op_mat_mat_prod >(A, B); - } - - // left factor is a matrix expression: - template<typename LhsT, typename RhsT, typename OpT, typename NumericT> - viennacl::matrix_expression< const viennacl::matrix_expression<const LhsT, const RhsT, OpT>, - const viennacl::matrix_base<NumericT>, - viennacl::op_mat_mat_prod > - prod(viennacl::matrix_expression<const LhsT, const RhsT, OpT> const & A, - viennacl::matrix_base<NumericT> const & B) - { - return viennacl::matrix_expression< const viennacl::matrix_expression<const LhsT, const RhsT, OpT>, - const viennacl::matrix_base<NumericT>, - viennacl::op_mat_mat_prod >(A, B); - } - - - // both factors transposed: - template<typename LhsT1, typename RhsT1, typename OpT1, - typename LhsT2, typename RhsT2, typename OpT2> - viennacl::matrix_expression< const viennacl::matrix_expression<const LhsT1, const RhsT1, OpT1>, - const viennacl::matrix_expression<const LhsT2, const RhsT2, OpT2>, - viennacl::op_mat_mat_prod > - prod(viennacl::matrix_expression<const LhsT1, const RhsT1, OpT1> const & A, - viennacl::matrix_expression<const LhsT2, const RhsT2, OpT2> const & B) - { - return viennacl::matrix_expression< const viennacl::matrix_expression<const LhsT1, const RhsT1, OpT1>, - const viennacl::matrix_expression<const LhsT2, const RhsT2, OpT2>, - viennacl::op_mat_mat_prod >(A, B); - } - - - - // matrix-vector product - template< typename NumericT> - viennacl::vector_expression< const viennacl::matrix_base<NumericT>, - const viennacl::vector_base<NumericT>, - viennacl::op_prod > - prod(viennacl::matrix_base<NumericT> const & A, - viennacl::vector_base<NumericT> const & x) - { - return viennacl::vector_expression< const viennacl::matrix_base<NumericT>, - const viennacl::vector_base<NumericT>, - viennacl::op_prod >(A, x); - } - - // matrix-vector product (resolve ambiguity) - template<typename NumericT, typename F> - viennacl::vector_expression< const viennacl::matrix_base<NumericT>, - const viennacl::vector_base<NumericT>, - viennacl::op_prod > - prod(viennacl::matrix<NumericT, F> const & A, - viennacl::vector_base<NumericT> const & x) - { - return viennacl::vector_expression< const viennacl::matrix_base<NumericT>, - const viennacl::vector_base<NumericT>, - viennacl::op_prod >(A, x); - } - - // matrix-vector product (resolve ambiguity) - template<typename MatrixT, typename NumericT> - viennacl::vector_expression< const viennacl::matrix_base<NumericT>, - const viennacl::vector_base<NumericT>, - viennacl::op_prod > - prod(viennacl::matrix_range<MatrixT> const & A, - viennacl::vector_base<NumericT> const & x) - { - return viennacl::vector_expression< const viennacl::matrix_base<NumericT>, - const viennacl::vector_base<NumericT>, - viennacl::op_prod >(A, x); - } - - // matrix-vector product (resolve ambiguity) - template<typename MatrixT, typename NumericT> - viennacl::vector_expression< const viennacl::matrix_base<NumericT>, - const viennacl::vector_base<NumericT>, - viennacl::op_prod > - prod(viennacl::matrix_slice<MatrixT> const & A, - viennacl::vector_base<NumericT> const & x) - { - return viennacl::vector_expression< const viennacl::matrix_base<NumericT>, - const viennacl::vector_base<NumericT>, - viennacl::op_prod >(A, x); - } - - // matrix-vector product with matrix expression (including transpose) - template< typename NumericT, typename LhsT, typename RhsT, typename OpT> - viennacl::vector_expression< const viennacl::matrix_expression<const LhsT, const RhsT, OpT>, - const viennacl::vector_base<NumericT>, - viennacl::op_prod > - prod(viennacl::matrix_expression<const LhsT, const RhsT, OpT> const & A, - viennacl::vector_base<NumericT> const & x) - { - return viennacl::vector_expression< const viennacl::matrix_expression<const LhsT, const RhsT, OpT>, - const viennacl::vector_base<NumericT>, - viennacl::op_prod >(A, x); - } - - - // matrix-vector product with vector expression - template< typename NumericT, typename LhsT, typename RhsT, typename OpT> - viennacl::vector_expression< const viennacl::matrix_base<NumericT>, - const viennacl::vector_expression<const LhsT, const RhsT, OpT>, - viennacl::op_prod > - prod(viennacl::matrix_base<NumericT> const & A, - viennacl::vector_expression<const LhsT, const RhsT, OpT> const & x) - { - return viennacl::vector_expression< const viennacl::matrix_base<NumericT>, - const viennacl::vector_expression<const LhsT, const RhsT, OpT>, - viennacl::op_prod >(A, x); - } - - - // matrix-vector product with matrix expression (including transpose) and vector expression - template<typename LhsT1, typename RhsT1, typename OpT1, - typename LhsT2, typename RhsT2, typename OpT2> - viennacl::vector_expression< const viennacl::matrix_expression<const LhsT1, const RhsT1, OpT1>, - const viennacl::vector_expression<const LhsT2, const RhsT2, OpT2>, - viennacl::op_prod > - prod(viennacl::matrix_expression<const LhsT1, const RhsT1, OpT1> const & A, - viennacl::vector_expression<const LhsT2, const RhsT2, OpT2> const & x) - { - return viennacl::vector_expression< const viennacl::matrix_expression<const LhsT1, const RhsT1, OpT1>, - const viennacl::vector_expression<const LhsT2, const RhsT2, OpT2>, - viennacl::op_prod >(A, x); - } - - - - - template< typename SparseMatrixType, typename SCALARTYPE> - typename viennacl::enable_if< viennacl::is_any_sparse_matrix<SparseMatrixType>::value, - viennacl::matrix_expression<const SparseMatrixType, - const matrix_base <SCALARTYPE>, - op_prod > - >::type - prod(const SparseMatrixType & sp_mat, - const viennacl::matrix_base<SCALARTYPE> & d_mat) - { - return viennacl::matrix_expression<const SparseMatrixType, - const viennacl::matrix_base<SCALARTYPE>, - op_prod >(sp_mat, d_mat); - } - - // right factor is transposed - template< typename SparseMatrixType, typename SCALARTYPE> - typename viennacl::enable_if< viennacl::is_any_sparse_matrix<SparseMatrixType>::value, - viennacl::matrix_expression< const SparseMatrixType, - const viennacl::matrix_expression<const viennacl::matrix_base<SCALARTYPE>, - const viennacl::matrix_base<SCALARTYPE>, - op_trans>, - viennacl::op_prod > - >::type - prod(const SparseMatrixType & A, - viennacl::matrix_expression<const viennacl::matrix_base<SCALARTYPE>, - const viennacl::matrix_base<SCALARTYPE>, - op_trans> const & B) - { - return viennacl::matrix_expression< const SparseMatrixType, - const viennacl::matrix_expression<const viennacl::matrix_base<SCALARTYPE>, - const viennacl::matrix_base<SCALARTYPE>, - op_trans>, - viennacl::op_prod >(A, B); - } - - - /** @brief Sparse matrix-matrix product with compressed_matrix objects */ - template<typename NumericT> - viennacl::matrix_expression<const compressed_matrix<NumericT>, - const compressed_matrix<NumericT>, - op_prod > - prod(compressed_matrix<NumericT> const & A, - compressed_matrix<NumericT> const & B) - { - return viennacl::matrix_expression<const compressed_matrix<NumericT>, - const compressed_matrix<NumericT>, - op_prod >(A, B); - } - - /** @brief Generic matrix-vector product with user-provided sparse matrix type */ - template<typename SparseMatrixType, typename NumericT> - vector_expression<const SparseMatrixType, - const vector_base<NumericT>, - op_prod > - prod(const SparseMatrixType & A, - const vector_base<NumericT> & x) - { - return vector_expression<const SparseMatrixType, - const vector_base<NumericT>, - op_prod >(A, x); - } - - } // end namespace linalg -} // end namespace viennacl -#endif - - - - -
