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vector.hpp
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#ifndef VIENNACL_LINALG_OPENCL_KERNELS_VECTOR_HPP
#define VIENNACL_LINALG_OPENCL_KERNELS_VECTOR_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 rupp@iue.tuwien.ac.at
(A list of authors and contributors can be found in the manual)
License: MIT (X11), see file LICENSE in the base directory
============================================================================= */
#include "viennacl/tools/tools.hpp"
#include "viennacl/scheduler/forwards.h"
#include "viennacl/scheduler/io.hpp"
#include "viennacl/scheduler/preset.hpp"
#include "viennacl/ocl/kernel.hpp"
#include "viennacl/ocl/platform.hpp"
#include "viennacl/ocl/utils.hpp"
namespace viennacl
{
namespace linalg
{
namespace opencl
{
namespace kernels
{
enum avbv_scalar_type
{
VIENNACL_AVBV_NONE = 0, // vector does not exist/contribute
VIENNACL_AVBV_CPU,
VIENNACL_AVBV_GPU
};
struct avbv_config
{
avbv_config() : with_stride_and_range(true), a(VIENNACL_AVBV_CPU), b(VIENNACL_AVBV_NONE) {}
bool with_stride_and_range;
std::string assign_op;
avbv_scalar_type a;
avbv_scalar_type b;
};
// just returns the for-loop
template <typename StringType>
void generate_avbv_impl2(StringType & source, std::string const & /*numeric_string*/, avbv_config const & cfg, bool mult_alpha, bool mult_beta)
{
source.append(" for (unsigned int i = get_global_id(0); i < size1.z; i += get_global_size(0)) \n");
if (cfg.with_stride_and_range)
{
source.append(" vec1[i*size1.y+size1.x] "); source.append(cfg.assign_op); source.append(" vec2[i*size2.y+size2.x] ");
if (mult_alpha)
source.append("* alpha ");
else
source.append("/ alpha ");
if (cfg.b != VIENNACL_AVBV_NONE)
{
source.append("+ vec3[i*size3.y+size3.x] ");
if (mult_beta)
source.append("* beta");
else
source.append("/ beta");
}
}
else
{
source.append(" vec1[i] "); source.append(cfg.assign_op); source.append(" vec2[i] ");
if (mult_alpha)
source.append("* alpha ");
else
source.append("/ alpha ");
if (cfg.b != VIENNACL_AVBV_NONE)
{
source.append("+ vec3[i] ");
if (mult_beta)
source.append("* beta");
else
source.append("/ beta");
}
}
source.append("; \n");
}
template <typename StringType>
void generate_avbv_impl(StringType & source, std::string const & numeric_string, avbv_config const & cfg)
{
source.append("__kernel void av");
if (cfg.b != VIENNACL_AVBV_NONE)
source.append("bv");
if (cfg.assign_op != "=")
source.append("_v");
if (cfg.a == VIENNACL_AVBV_CPU)
source.append("_cpu");
else if (cfg.a == VIENNACL_AVBV_GPU)
source.append("_gpu");
if (cfg.b == VIENNACL_AVBV_CPU)
source.append("_cpu");
else if (cfg.b == VIENNACL_AVBV_GPU)
source.append("_gpu");
source.append("( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * vec1, \n");
source.append(" uint4 size1, \n");
source.append(" \n");
if (cfg.a == VIENNACL_AVBV_CPU)
{
source.append(" "); source.append(numeric_string); source.append(" fac2, \n");
}
else if (cfg.a == VIENNACL_AVBV_GPU)
{
source.append(" __global "); source.append(numeric_string); source.append(" * fac2, \n");
}
source.append(" unsigned int options2, \n"); // 0: no action, 1: flip sign, 2: take inverse, 3: flip sign and take inverse
source.append(" __global const "); source.append(numeric_string); source.append(" * vec2, \n");
source.append(" uint4 size2");
if (cfg.b != VIENNACL_AVBV_NONE)
{
source.append(", \n\n");
if (cfg.b == VIENNACL_AVBV_CPU)
{
source.append(" "); source.append(numeric_string); source.append(" fac3, \n");
}
else if (cfg.b == VIENNACL_AVBV_GPU)
{
source.append(" __global "); source.append(numeric_string); source.append(" * fac3, \n");
}
source.append(" unsigned int options3, \n"); // 0: no action, 1: flip sign, 2: take inverse, 3: flip sign and take inverse
source.append(" __global const "); source.append(numeric_string); source.append(" * vec3, \n");
source.append(" uint4 size3 \n");
}
source.append(") { \n");
if (cfg.a == VIENNACL_AVBV_CPU)
{
source.append(" "); source.append(numeric_string); source.append(" alpha = fac2; \n");
}
else if (cfg.a == VIENNACL_AVBV_GPU)
{
source.append(" "); source.append(numeric_string); source.append(" alpha = fac2[0]; \n");
}
source.append(" if (options2 & (1 << 0)) \n");
source.append(" alpha = -alpha; \n");
source.append(" \n");
if (cfg.b == VIENNACL_AVBV_CPU)
{
source.append(" "); source.append(numeric_string); source.append(" beta = fac3; \n");
}
else if (cfg.b == VIENNACL_AVBV_GPU)
{
source.append(" "); source.append(numeric_string); source.append(" beta = fac3[0]; \n");
}
if (cfg.b != VIENNACL_AVBV_NONE)
{
source.append(" if (options3 & (1 << 0)) \n");
source.append(" beta = -beta; \n");
source.append(" \n");
}
source.append(" if (options2 & (1 << 1)) { \n");
if (cfg.b != VIENNACL_AVBV_NONE)
{
source.append(" if (options3 & (1 << 1)) {\n");
generate_avbv_impl2(source, numeric_string, cfg, false, false);
source.append(" } else {\n");
generate_avbv_impl2(source, numeric_string, cfg, false, true);
source.append(" } \n");
}
else
generate_avbv_impl2(source, numeric_string, cfg, false, true);
source.append(" } else { \n");
if (cfg.b != VIENNACL_AVBV_NONE)
{
source.append(" if (options3 & (1 << 1)) {\n");
generate_avbv_impl2(source, numeric_string, cfg, true, false);
source.append(" } else {\n");
generate_avbv_impl2(source, numeric_string, cfg, true, true);
source.append(" } \n");
}
else
generate_avbv_impl2(source, numeric_string, cfg, true, true);
source.append(" } \n");
source.append("} \n");
}
template <typename StringType>
void generate_avbv(StringType & source, std::string const & numeric_string)
{
avbv_config cfg;
cfg.assign_op = "=";
cfg.with_stride_and_range = true;
// av
cfg.b = VIENNACL_AVBV_NONE; cfg.a = VIENNACL_AVBV_CPU; generate_avbv_impl(source, numeric_string, cfg);
cfg.b = VIENNACL_AVBV_NONE; cfg.a = VIENNACL_AVBV_GPU; generate_avbv_impl(source, numeric_string, cfg);
// avbv
cfg.a = VIENNACL_AVBV_CPU; cfg.b = VIENNACL_AVBV_CPU; generate_avbv_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AVBV_CPU; cfg.b = VIENNACL_AVBV_GPU; generate_avbv_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AVBV_GPU; cfg.b = VIENNACL_AVBV_CPU; generate_avbv_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AVBV_GPU; cfg.b = VIENNACL_AVBV_GPU; generate_avbv_impl(source, numeric_string, cfg);
// avbv
cfg.assign_op = "+=";
cfg.a = VIENNACL_AVBV_CPU; cfg.b = VIENNACL_AVBV_CPU; generate_avbv_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AVBV_CPU; cfg.b = VIENNACL_AVBV_GPU; generate_avbv_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AVBV_GPU; cfg.b = VIENNACL_AVBV_CPU; generate_avbv_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AVBV_GPU; cfg.b = VIENNACL_AVBV_GPU; generate_avbv_impl(source, numeric_string, cfg);
}
template <typename StringType>
void generate_plane_rotation(StringType & source, std::string const & numeric_string)
{
source.append("__kernel void plane_rotation( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * vec1, \n");
source.append(" unsigned int start1, \n");
source.append(" unsigned int inc1, \n");
source.append(" unsigned int size1, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * vec2, \n");
source.append(" unsigned int start2, \n");
source.append(" unsigned int inc2, \n");
source.append(" unsigned int size2, \n");
source.append(" "); source.append(numeric_string); source.append(" alpha, \n");
source.append(" "); source.append(numeric_string); source.append(" beta) \n");
source.append("{ \n");
source.append(" "); source.append(numeric_string); source.append(" tmp1 = 0; \n");
source.append(" "); source.append(numeric_string); source.append(" tmp2 = 0; \n");
source.append(" \n");
source.append(" for (unsigned int i = get_global_id(0); i < size1; i += get_global_size(0)) \n");
source.append(" { \n");
source.append(" tmp1 = vec1[i*inc1+start1]; \n");
source.append(" tmp2 = vec2[i*inc2+start2]; \n");
source.append(" \n");
source.append(" vec1[i*inc1+start1] = alpha * tmp1 + beta * tmp2; \n");
source.append(" vec2[i*inc2+start2] = alpha * tmp2 - beta * tmp1; \n");
source.append(" } \n");
source.append(" \n");
source.append("} \n");
}
template <typename StringType>
void generate_vector_swap(StringType & source, std::string const & numeric_string)
{
source.append("__kernel void swap( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * vec1, \n");
source.append(" unsigned int start1, \n");
source.append(" unsigned int inc1, \n");
source.append(" unsigned int size1, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * vec2, \n");
source.append(" unsigned int start2, \n");
source.append(" unsigned int inc2, \n");
source.append(" unsigned int size2 \n");
source.append(" ) \n");
source.append("{ \n");
source.append(" "); source.append(numeric_string); source.append(" tmp; \n");
source.append(" for (unsigned int i = get_global_id(0); i < size1; i += get_global_size(0)) \n");
source.append(" { \n");
source.append(" tmp = vec2[i*inc2+start2]; \n");
source.append(" vec2[i*inc2+start2] = vec1[i*inc1+start1]; \n");
source.append(" vec1[i*inc1+start1] = tmp; \n");
source.append(" } \n");
source.append("} \n");
}
template <typename StringType>
void generate_assign_cpu(StringType & source, std::string const & numeric_string)
{
source.append("__kernel void assign_cpu( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * vec1, \n");
source.append(" unsigned int start1, \n");
source.append(" unsigned int inc1, \n");
source.append(" unsigned int size1, \n");
source.append(" unsigned int internal_size1, \n");
source.append(" "); source.append(numeric_string); source.append(" alpha) \n");
source.append("{ \n");
source.append(" for (unsigned int i = get_global_id(0); i < internal_size1; i += get_global_size(0)) \n");
source.append(" vec1[i*inc1+start1] = (i < size1) ? alpha : 0; \n");
source.append("} \n");
}
template <typename StringType>
void generate_inner_prod(StringType & source, std::string const & numeric_string, vcl_size_t vector_num)
{
std::stringstream ss;
ss << vector_num;
std::string vector_num_string = ss.str();
source.append("__kernel void inner_prod"); source.append(vector_num_string); source.append("( \n");
source.append(" __global const "); source.append(numeric_string); source.append(" * x, \n");
source.append(" uint4 params_x, \n");
for (vcl_size_t i=0; i<vector_num; ++i)
{
ss.str("");
ss << i;
source.append(" __global const "); source.append(numeric_string); source.append(" * y"); source.append(ss.str()); source.append(", \n");
source.append(" uint4 params_y"); source.append(ss.str()); source.append(", \n");
}
source.append(" __local "); source.append(numeric_string); source.append(" * tmp_buffer, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * group_buffer) \n");
source.append("{ \n");
source.append(" unsigned int entries_per_thread = (params_x.z - 1) / get_global_size(0) + 1; \n");
source.append(" unsigned int vec_start_index = get_group_id(0) * get_local_size(0) * entries_per_thread; \n");
source.append(" unsigned int vec_stop_index = min((unsigned int)((get_group_id(0) + 1) * get_local_size(0) * entries_per_thread), params_x.z); \n");
// compute partial results within group:
for (vcl_size_t i=0; i<vector_num; ++i)
{
ss.str("");
ss << i;
source.append(" "); source.append(numeric_string); source.append(" tmp"); source.append(ss.str()); source.append(" = 0; \n");
}
source.append(" for (unsigned int i = vec_start_index + get_local_id(0); i < vec_stop_index; i += get_local_size(0)) { \n");
source.append(" "); source.append(numeric_string); source.append(" val_x = x[i*params_x.y + params_x.x]; \n");
for (vcl_size_t i=0; i<vector_num; ++i)
{
ss.str("");
ss << i;
source.append(" tmp"); source.append(ss.str()); source.append(" += val_x * y"); source.append(ss.str()); source.append("[i * params_y"); source.append(ss.str()); source.append(".y + params_y"); source.append(ss.str()); source.append(".x]; \n");
}
source.append(" } \n");
for (vcl_size_t i=0; i<vector_num; ++i)
{
ss.str("");
ss << i;
source.append(" tmp_buffer[get_local_id(0) + "); source.append(ss.str()); source.append(" * get_local_size(0)] = tmp"); source.append(ss.str()); source.append("; \n");
}
// now run reduction:
source.append(" for (unsigned int stride = get_local_size(0)/2; stride > 0; stride /= 2) \n");
source.append(" { \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_local_id(0) < stride) { \n");
for (vcl_size_t i=0; i<vector_num; ++i)
{
ss.str("");
ss << i;
source.append(" tmp_buffer[get_local_id(0) + "); source.append(ss.str()); source.append(" * get_local_size(0)] += tmp_buffer[get_local_id(0) + "); source.append(ss.str()); source.append(" * get_local_size(0) + stride]; \n");
}
source.append(" } \n");
source.append(" } \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_local_id(0) == 0) { \n");
for (vcl_size_t i=0; i<vector_num; ++i)
{
ss.str("");
ss << i;
source.append(" group_buffer[get_group_id(0) + "); source.append(ss.str()); source.append(" * get_num_groups(0)] = tmp_buffer["); source.append(ss.str()); source.append(" * get_local_size(0)]; \n");
}
source.append(" } \n");
source.append("} \n");
}
template <typename StringType>
void generate_norm(StringType & source, std::string const & numeric_string)
{
bool is_float_or_double = (numeric_string == "float" || numeric_string == "double");
source.append(numeric_string); source.append(" impl_norm( \n");
source.append(" __global const "); source.append(numeric_string); source.append(" * vec, \n");
source.append(" unsigned int start1, \n");
source.append(" unsigned int inc1, \n");
source.append(" unsigned int size1, \n");
source.append(" unsigned int norm_selector, \n");
source.append(" __local "); source.append(numeric_string); source.append(" * tmp_buffer) \n");
source.append("{ \n");
source.append(" "); source.append(numeric_string); source.append(" tmp = 0; \n");
source.append(" if (norm_selector == 1) \n"); //norm_1
source.append(" { \n");
source.append(" for (unsigned int i = get_local_id(0); i < size1; i += get_local_size(0)) \n");
if (is_float_or_double)
source.append(" tmp += fabs(vec[i*inc1 + start1]); \n");
else if (numeric_string[0] == 'u') // abs may not be defined for unsigned types
source.append(" tmp += vec[i*inc1 + start1]; \n");
else
source.append(" tmp += abs(vec[i*inc1 + start1]); \n");
source.append(" } \n");
source.append(" else if (norm_selector == 2) \n"); //norm_2
source.append(" { \n");
source.append(" "); source.append(numeric_string); source.append(" vec_entry = 0; \n");
source.append(" for (unsigned int i = get_local_id(0); i < size1; i += get_local_size(0)) \n");
source.append(" { \n");
source.append(" vec_entry = vec[i*inc1 + start1]; \n");
source.append(" tmp += vec_entry * vec_entry; \n");
source.append(" } \n");
source.append(" } \n");
source.append(" else if (norm_selector == 0) \n"); //norm_inf
source.append(" { \n");
source.append(" for (unsigned int i = get_local_id(0); i < size1; i += get_local_size(0)) \n");
if (is_float_or_double)
source.append(" tmp = fmax(fabs(vec[i*inc1 + start1]), tmp); \n");
else if (numeric_string[0] == 'u') // abs may not be defined for unsigned types
source.append(" tmp = max(vec[i*inc1 + start1], tmp); \n");
else
{
source.append(" tmp = max(("); source.append(numeric_string); source.append(")abs(vec[i*inc1 + start1]), tmp); \n");
}
source.append(" } \n");
source.append(" tmp_buffer[get_local_id(0)] = tmp; \n");
source.append(" if (norm_selector > 0) \n"); //norm_1 or norm_2:
source.append(" { \n");
source.append(" for (unsigned int stride = get_local_size(0)/2; stride > 0; stride /= 2) \n");
source.append(" { \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_local_id(0) < stride) \n");
source.append(" tmp_buffer[get_local_id(0)] += tmp_buffer[get_local_id(0)+stride]; \n");
source.append(" } \n");
source.append(" return tmp_buffer[0]; \n");
source.append(" } \n");
//norm_inf:
source.append(" for (unsigned int stride = get_local_size(0)/2; stride > 0; stride /= 2) \n");
source.append(" { \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_local_id(0) < stride) \n");
if (is_float_or_double)
source.append(" tmp_buffer[get_local_id(0)] = fmax(tmp_buffer[get_local_id(0)], tmp_buffer[get_local_id(0)+stride]); \n");
else
source.append(" tmp_buffer[get_local_id(0)] = max(tmp_buffer[get_local_id(0)], tmp_buffer[get_local_id(0)+stride]); \n");
source.append(" } \n");
source.append(" return tmp_buffer[0]; \n");
source.append("}; \n");
source.append("__kernel void norm( \n");
source.append(" __global const "); source.append(numeric_string); source.append(" * vec, \n");
source.append(" unsigned int start1, \n");
source.append(" unsigned int inc1, \n");
source.append(" unsigned int size1, \n");
source.append(" unsigned int norm_selector, \n");
source.append(" __local "); source.append(numeric_string); source.append(" * tmp_buffer, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * group_buffer) \n");
source.append("{ \n");
source.append(" "); source.append(numeric_string); source.append(" tmp = impl_norm(vec, \n");
source.append(" ( get_group_id(0) * size1) / get_num_groups(0) * inc1 + start1, \n");
source.append(" inc1, \n");
source.append(" ( (1 + get_group_id(0)) * size1) / get_num_groups(0) \n");
source.append(" - ( get_group_id(0) * size1) / get_num_groups(0), \n");
source.append(" norm_selector, \n");
source.append(" tmp_buffer); \n");
source.append(" if (get_local_id(0) == 0) \n");
source.append(" group_buffer[get_group_id(0)] = tmp; \n");
source.append("} \n");
}
template <typename StringType>
void generate_inner_prod_sum(StringType & source, std::string const & numeric_string)
{
// sums the array 'vec1' and writes to result. Each work group computes the inner product for a subvector of size 'size_per_workgroup'.
source.append("__kernel void sum_inner_prod( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * vec1, \n");
source.append(" unsigned int size_per_workgroup, \n");
source.append(" __local "); source.append(numeric_string); source.append(" * tmp_buffer, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * result, \n");
source.append(" unsigned int start_result, \n");
source.append(" unsigned int inc_result) \n");
source.append("{ \n");
source.append(" "); source.append(numeric_string); source.append(" thread_sum = 0; \n");
source.append(" for (unsigned int i = get_local_id(0); i<size_per_workgroup; i += get_local_size(0)) \n");
source.append(" thread_sum += vec1[size_per_workgroup * get_group_id(0) + i]; \n");
source.append(" tmp_buffer[get_local_id(0)] = thread_sum; \n");
source.append(" for (unsigned int stride = get_local_size(0)/2; stride > 0; stride /= 2) \n");
source.append(" { \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_local_id(0) < stride) \n");
source.append(" tmp_buffer[get_local_id(0)] += tmp_buffer[get_local_id(0) + stride]; \n");
source.append(" } \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_local_id(0) == 0) \n");
source.append(" result[start_result + inc_result * get_group_id(0)] = tmp_buffer[0]; \n");
source.append("} \n");
}
template <typename StringType>
void generate_sum(StringType & source, std::string const & numeric_string)
{
// sums the array 'vec1' and writes to result. Makes use of a single work-group only.
source.append("__kernel void sum( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * vec1, \n");
source.append(" unsigned int start1, \n");
source.append(" unsigned int inc1, \n");
source.append(" unsigned int size1, \n");
source.append(" unsigned int option, \n"); //0: use fmax, 1: just sum, 2: sum and return sqrt of sum
source.append(" __local "); source.append(numeric_string); source.append(" * tmp_buffer, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * result) \n");
source.append("{ \n");
source.append(" "); source.append(numeric_string); source.append(" thread_sum = 0; \n");
source.append(" "); source.append(numeric_string); source.append(" tmp = 0; \n");
source.append(" for (unsigned int i = get_local_id(0); i<size1; i += get_local_size(0)) \n");
source.append(" { \n");
source.append(" if (option > 0) \n");
source.append(" thread_sum += vec1[i*inc1+start1]; \n");
source.append(" else \n");
source.append(" { \n");
source.append(" tmp = vec1[i*inc1+start1]; \n");
source.append(" tmp = (tmp < 0) ? -tmp : tmp; \n");
source.append(" thread_sum = (thread_sum > tmp) ? thread_sum : tmp; \n");
source.append(" } \n");
source.append(" } \n");
source.append(" tmp_buffer[get_local_id(0)] = thread_sum; \n");
source.append(" for (unsigned int stride = get_local_size(0)/2; stride > 0; stride /= 2) \n");
source.append(" { \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_local_id(0) < stride) \n");
source.append(" { \n");
source.append(" if (option > 0) \n");
source.append(" tmp_buffer[get_local_id(0)] += tmp_buffer[get_local_id(0) + stride]; \n");
source.append(" else \n");
source.append(" tmp_buffer[get_local_id(0)] = (tmp_buffer[get_local_id(0)] > tmp_buffer[get_local_id(0) + stride]) ? tmp_buffer[get_local_id(0)] : tmp_buffer[get_local_id(0) + stride]; \n");
source.append(" } \n");
source.append(" } \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_global_id(0) == 0) \n");
source.append(" { \n");
if (numeric_string == "float" || numeric_string == "double")
{
source.append(" if (option == 2) \n");
source.append(" *result = sqrt(tmp_buffer[0]); \n");
source.append(" else \n");
}
source.append(" *result = tmp_buffer[0]; \n");
source.append(" } \n");
source.append("} \n");
}
template <typename StringType>
void generate_index_norm_inf(StringType & source, std::string const & numeric_string)
{
//index_norm_inf:
source.append("unsigned int index_norm_inf_impl( \n");
source.append(" __global const "); source.append(numeric_string); source.append(" * vec, \n");
source.append(" unsigned int start1, \n");
source.append(" unsigned int inc1, \n");
source.append(" unsigned int size1, \n");
source.append(" __local "); source.append(numeric_string); source.append(" * entry_buffer, \n");
source.append(" __local unsigned int * index_buffer) \n");
source.append("{ \n");
//step 1: fill buffer:
source.append(" "); source.append(numeric_string); source.append(" cur_max = 0; \n");
source.append(" "); source.append(numeric_string); source.append(" tmp; \n");
source.append(" for (unsigned int i = get_global_id(0); i < size1; i += get_global_size(0)) \n");
source.append(" { \n");
if (numeric_string == "float" || numeric_string == "double")
source.append(" tmp = fabs(vec[i*inc1+start1]); \n");
else if (numeric_string[0] == 'u') // abs may not be defined for unsigned types
source.append(" tmp = vec[i*inc1+start1]; \n");
else
source.append(" tmp = abs(vec[i*inc1+start1]); \n");
source.append(" if (cur_max < tmp) \n");
source.append(" { \n");
source.append(" entry_buffer[get_global_id(0)] = tmp; \n");
source.append(" index_buffer[get_global_id(0)] = i; \n");
source.append(" cur_max = tmp; \n");
source.append(" } \n");
source.append(" } \n");
//step 2: parallel reduction:
source.append(" for (unsigned int stride = get_global_size(0)/2; stride > 0; stride /= 2) \n");
source.append(" { \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_global_id(0) < stride) \n");
source.append(" { \n");
//find the first occurring index
source.append(" if (entry_buffer[get_global_id(0)] < entry_buffer[get_global_id(0)+stride]) \n");
source.append(" { \n");
source.append(" index_buffer[get_global_id(0)] = index_buffer[get_global_id(0)+stride]; \n");
source.append(" entry_buffer[get_global_id(0)] = entry_buffer[get_global_id(0)+stride]; \n");
source.append(" } \n");
source.append(" } \n");
source.append(" } \n");
source.append(" \n");
source.append(" return index_buffer[0]; \n");
source.append("} \n");
source.append("__kernel void index_norm_inf( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * vec, \n");
source.append(" unsigned int start1, \n");
source.append(" unsigned int inc1, \n");
source.append(" unsigned int size1, \n");
source.append(" __local "); source.append(numeric_string); source.append(" * entry_buffer, \n");
source.append(" __local unsigned int * index_buffer, \n");
source.append(" __global unsigned int * result) \n");
source.append("{ \n");
source.append(" entry_buffer[get_global_id(0)] = 0; \n");
source.append(" index_buffer[get_global_id(0)] = 0; \n");
source.append(" unsigned int tmp = index_norm_inf_impl(vec, start1, inc1, size1, entry_buffer, index_buffer); \n");
source.append(" if (get_global_id(0) == 0) *result = tmp; \n");
source.append("} \n");
}
template <typename StringType>
void generate_maxmin(StringType & source, std::string const & numeric_string, bool is_max)
{
// sums the array 'vec1' and writes to result. Makes use of a single work-group only.
if (is_max)
source.append("__kernel void max_kernel( \n");
else
source.append("__kernel void min_kernel( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * vec1, \n");
source.append(" unsigned int start1, \n");
source.append(" unsigned int inc1, \n");
source.append(" unsigned int size1, \n");
source.append(" __local "); source.append(numeric_string); source.append(" * tmp_buffer, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * result) \n");
source.append("{ \n");
source.append(" "); source.append(numeric_string); source.append(" thread_result = vec1[start1]; \n");
source.append(" for (unsigned int i = get_global_id(0); i<size1; i += get_global_size(0)) \n");
source.append(" { \n");
source.append(" "); source.append(numeric_string); source.append(" tmp = vec1[i*inc1+start1]; \n");
if (is_max)
source.append(" thread_result = thread_result > tmp ? thread_result : tmp; \n");
else
source.append(" thread_result = thread_result < tmp ? thread_result : tmp; \n");
source.append(" } \n");
source.append(" tmp_buffer[get_local_id(0)] = thread_result; \n");
source.append(" for (unsigned int stride = get_local_size(0)/2; stride > 0; stride /= 2) \n");
source.append(" { \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_local_id(0) < stride) \n");
source.append(" { \n");
if (is_max)
source.append(" tmp_buffer[get_local_id(0)] = tmp_buffer[get_local_id(0)] > tmp_buffer[get_local_id(0) + stride] ? tmp_buffer[get_local_id(0)] : tmp_buffer[get_local_id(0) + stride]; \n");
else
source.append(" tmp_buffer[get_local_id(0)] = tmp_buffer[get_local_id(0)] < tmp_buffer[get_local_id(0) + stride] ? tmp_buffer[get_local_id(0)] : tmp_buffer[get_local_id(0) + stride]; \n");
source.append(" } \n");
source.append(" } \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if (get_local_id(0) == 0) \n");
source.append(" result[get_group_id(0)] = tmp_buffer[0]; \n");
source.append("} \n");
}
// main kernel class
template<typename NumericT>
struct vector
{
static std::string program_name()
{
return viennacl::ocl::type_to_string<NumericT>::apply() + "_vector";
}
static void init(viennacl::ocl::context & ctx)
{
viennacl::ocl::DOUBLE_PRECISION_CHECKER<NumericT>::apply(ctx);
std::string numeric_string = viennacl::ocl::type_to_string<NumericT>::apply();
static std::map<cl_context, bool> init_done;
if (!init_done[ctx.handle().get()])
{
std::string source;
source.reserve(8192);
viennacl::ocl::append_double_precision_pragma<NumericT>(ctx, source);
// fully parametrized kernels:
generate_avbv(source, numeric_string);
// kernels with mostly predetermined skeleton:
generate_plane_rotation(source, numeric_string);
generate_vector_swap(source, numeric_string);
generate_assign_cpu(source, numeric_string);
generate_inner_prod(source, numeric_string, 1);
generate_norm(source, numeric_string);
generate_sum(source, numeric_string);
generate_index_norm_inf(source, numeric_string);
generate_maxmin(source, numeric_string, true);
generate_maxmin(source, numeric_string, false);
std::string prog_name = program_name();
#ifdef VIENNACL_BUILD_INFO
std::cout << "Creating program " << prog_name << std::endl;
#endif
ctx.add_program(source, prog_name);
init_done[ctx.handle().get()] = true;
} //if
} //init
};
// class with kernels for multiple inner products.
template<typename NumericT>
struct vector_multi_inner_prod
{
static std::string program_name()
{
return viennacl::ocl::type_to_string<NumericT>::apply() + "_vector_multi";
}
static void init(viennacl::ocl::context & ctx)
{
viennacl::ocl::DOUBLE_PRECISION_CHECKER<NumericT>::apply(ctx);
std::string numeric_string = viennacl::ocl::type_to_string<NumericT>::apply();
static std::map<cl_context, bool> init_done;
if (!init_done[ctx.handle().get()])
{
std::string source;
source.reserve(8192);
viennacl::ocl::append_double_precision_pragma<NumericT>(ctx, source);
generate_inner_prod(source, numeric_string, 2);
generate_inner_prod(source, numeric_string, 3);
generate_inner_prod(source, numeric_string, 4);
generate_inner_prod(source, numeric_string, 8);
generate_inner_prod_sum(source, numeric_string);
std::string prog_name = program_name();
#ifdef VIENNACL_BUILD_INFO
std::cout << "Creating program " << prog_name << std::endl;
#endif
ctx.add_program(source, prog_name);
init_done[ctx.handle().get()] = true;
} //if
} //init
};
template<typename StringT>
void generate_vector_convert(StringT & source, std::string const & dest_type, std::string const & src_type)
{
source.append(" __kernel void convert_" + dest_type + "_" + src_type + "( \n");
source.append(" __global " + dest_type + " * dest, \n");
source.append(" unsigned int start_dest, unsigned int inc_dest, unsigned int size_dest, \n");
source.append(" __global const " + src_type + " * src, \n");
source.append(" unsigned int start_src, unsigned int inc_src) \n");
source.append(" { \n");
source.append(" for (unsigned int i = get_global_id(0); i < size_dest; i += get_global_size(0)) \n");
source.append(" dest[start_dest + i * inc_dest] = src[start_src + i * inc_src]; \n");
source.append(" } \n");
}
struct vector_convert
{
public:
static std::string program_name()
{
return "vector_convert";
}
static void init(viennacl::ocl::context & ctx)
{
static std::map<cl_context, bool> init_done;
if (!init_done[ctx.handle().get()])
{
std::string source;
source.reserve(4096);
// int
generate_vector_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<float>::apply());
// unsigned int
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<float>::apply());
// long
generate_vector_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<float>::apply());
// unsigned long
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<float>::apply());
// float
generate_vector_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<float>::apply());
if (ctx.current_device().double_support())
{
viennacl::ocl::append_double_precision_pragma<double>(ctx, source);
generate_vector_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<double>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<double>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<double>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<double>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<double>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<float>::apply());
generate_vector_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<double>::apply());
}
std::string prog_name = program_name();
#ifdef VIENNACL_BUILD_INFO
std::cout << "Creating program " << prog_name << std::endl;
#endif
ctx.add_program(source, prog_name);
init_done[ctx.handle().get()] = true;
} //if
} //init
};
} // namespace kernels
} // namespace opencl
} // namespace linalg
} // namespace viennacl
#endif
viennacl::ocl::context::current_device
viennacl::ocl::device const & current_device() const
Returns the current device.
Definition: context.hpp:112
platform.hpp
Implements a OpenCL platform within ViennaCL.
viennacl::linalg::opencl::kernels::generate_inner_prod
void generate_inner_prod(StringType &source, std::string const &numeric_string, vcl_size_t vector_num)
Definition: vector.hpp:306
viennacl::linalg::opencl::kernels::generate_index_norm_inf
void generate_index_norm_inf(StringType &source, std::string const &numeric_string)
Definition: vector.hpp:564
viennacl::linalg::opencl::kernels::vector_convert::program_name
static std::string program_name()
Definition: vector.hpp:784
viennacl::ocl::append_double_precision_pragma< double >
void append_double_precision_pragma< double >(viennacl::ocl::context const &ctx, std::string &source)
Definition: utils.hpp:78
tools.hpp
Various little tools used here and there in ViennaCL.
viennacl::linalg::opencl::kernels::generate_vector_swap
void generate_vector_swap(StringType &source, std::string const &numeric_string)
Definition: vector.hpp:265
io.hpp
Some helper routines for reading/writing/printing scheduler expressions.
viennacl::ocl::context
Manages an OpenCL context and provides the respective convenience functions for creating buffers...
Definition: context.hpp:55
viennacl::linalg::opencl::kernels::vector_multi_inner_prod
Main kernel class for generating OpenCL kernels for multiple inner products on/with viennacl::vector<...
Definition: vector.hpp:727
viennacl::linalg::opencl::kernels::generate_assign_cpu
void generate_assign_cpu(StringType &source, std::string const &numeric_string, bool is_row_major)
Definition: matrix.hpp:279
utils.hpp
Provides OpenCL-related utilities.
viennacl::linalg::opencl::kernels::generate_maxmin
void generate_maxmin(StringType &source, std::string const &numeric_string, bool is_max)
Definition: vector.hpp:630
viennacl::linalg::opencl::kernels::vector_convert
Main kernel class for vector conversion routines (e.g. convert vector<int> to vector<float>).
Definition: vector.hpp:780
viennacl::linalg::opencl::kernels::generate_plane_rotation
void generate_plane_rotation(StringType &source, std::string const &numeric_string)
Definition: vector.hpp:235
viennacl::linalg::opencl::kernels::vector_multi_inner_prod::program_name
static std::string program_name()
Definition: vector.hpp:729
viennacl::linalg::opencl::kernels::VIENNACL_AVBV_CPU
Definition: vector.hpp:50
viennacl::linalg::opencl::kernels::avbv_config::with_stride_and_range
bool with_stride_and_range
Definition: vector.hpp:59
viennacl::ocl::context::handle
const viennacl::ocl::handle< cl_context > & handle() const
Returns the context handle.
Definition: context.hpp:611
viennacl::ocl::DOUBLE_PRECISION_CHECKER::apply
static void apply(viennacl::ocl::context const &)
Definition: utils.hpp:40
viennacl::linalg::opencl::kernels::avbv_config::b
avbv_scalar_type b
Definition: vector.hpp:62
viennacl::linalg::opencl::kernels::generate_avbv
void generate_avbv(StringType &source, std::string const &numeric_string)
Definition: vector.hpp:209
viennacl::ocl::context::add_program
viennacl::ocl::program & add_program(cl_program p, std::string const &prog_name)
Adds a program to the context.
Definition: context.hpp:368
viennacl::ocl::handle::get
const OCL_TYPE & get() const
Definition: handle.hpp:191
preset.hpp
viennacl::linalg::opencl::kernels::vector::program_name
static std::string program_name()
Definition: vector.hpp:681
viennacl::linalg::opencl::kernels::generate_sum
void generate_sum(StringType &source, std::string const &numeric_string)
Definition: vector.hpp:508
viennacl::linalg::opencl::kernels::vector::init
static void init(viennacl::ocl::context &ctx)
Definition: vector.hpp:686
viennacl::linalg::opencl::kernels::avbv_config
Configuration struct for generating OpenCL kernels for linear combinations of vectors.
Definition: vector.hpp:55
viennacl::ocl::device::double_support
bool double_support() const
ViennaCL convenience function: Returns true if the device supports double precision.
Definition: device.hpp:956
viennacl::vcl_size_t
std::size_t vcl_size_t
Definition: forwards.h:75
viennacl::linalg::opencl::kernels::avbv_config::avbv_config
avbv_config()
Definition: vector.hpp:57
forwards.h
Provides the datastructures for dealing with a single statement such as 'x = y + z;'.
viennacl::linalg::opencl::kernels::vector_multi_inner_prod::init
static void init(viennacl::ocl::context &ctx)
Definition: vector.hpp:734
viennacl::linalg::opencl::kernels::generate_avbv_impl
void generate_avbv_impl(StringType &source, std::string const &numeric_string, avbv_config const &cfg)
Definition: vector.hpp:106
viennacl::linalg::opencl::kernels::generate_avbv_impl2
void generate_avbv_impl2(StringType &source, std::string const &, avbv_config const &cfg, bool mult_alpha, bool mult_beta)
Definition: vector.hpp:67
viennacl::linalg::opencl::kernels::generate_inner_prod_sum
void generate_inner_prod_sum(StringType &source, std::string const &numeric_string)
Definition: vector.hpp:476
kernel.hpp
Representation of an OpenCL kernel in ViennaCL.
viennacl::linalg::opencl::kernels::VIENNACL_AVBV_GPU
Definition: vector.hpp:51
viennacl::linalg::opencl::kernels::avbv_config::assign_op
std::string assign_op
Definition: vector.hpp:60
viennacl::linalg::opencl::kernels::generate_norm
void generate_norm(StringType &source, std::string const &numeric_string)
Definition: vector.hpp:380
viennacl::ocl::type_to_string
Helper class for converting a type to its string representation.
Definition: utils.hpp:57
viennacl::linalg::opencl::kernels::generate_vector_convert
void generate_vector_convert(StringT &source, std::string const &dest_type, std::string const &src_type)
Definition: vector.hpp:766
viennacl::linalg::opencl::kernels::VIENNACL_AVBV_NONE
Definition: vector.hpp:49
viennacl::linalg::opencl::kernels::avbv_config::a
avbv_scalar_type a
Definition: vector.hpp:61
viennacl::linalg::opencl::kernels::vector_convert::init
static void init(viennacl::ocl::context &ctx)
Definition: vector.hpp:789
viennacl::linalg::opencl::kernels::avbv_scalar_type
avbv_scalar_type
Enumeration for the scalar type in avbv-like operations.
Definition: vector.hpp:47
viennacl::linalg::opencl::kernels::vector
Main kernel class for generating OpenCL kernels for operations on/with viennacl::vector<> without inv...
Definition: vector.hpp:679