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matrix.hpp
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#ifndef VIENNACL_LINALG_OPENCL_KERNELS_MATRIX_HPP
#define VIENNACL_LINALG_OPENCL_KERNELS_MATRIX_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/scheduler/preset.hpp"
#include "viennacl/tools/tools.hpp"
#include "viennacl/ocl/kernel.hpp"
#include "viennacl/ocl/platform.hpp"
#include "viennacl/ocl/utils.hpp"
#include "viennacl/device_specific/execution_handler.hpp"
#include "viennacl/device_specific/builtin_database/matrix_product.hpp"
namespace viennacl
{
namespace linalg
{
namespace opencl
{
namespace kernels
{
enum ambm_scalar_type
{
VIENNACL_AMBM_NONE = 0, // matrix does not exist/contribute
VIENNACL_AMBM_CPU,
VIENNACL_AMBM_GPU
};
struct ambm_config
{
ambm_config() : with_stride_and_range(true), is_row_major(true), a(VIENNACL_AMBM_CPU), b(VIENNACL_AMBM_NONE) {}
bool with_stride_and_range;
bool is_row_major;
std::string assign_op;
ambm_scalar_type a;
ambm_scalar_type b;
};
// just returns the for-loop
template <typename StringType>
void generate_ambm_impl2(StringType & source, ambm_config const & cfg, bool mult_alpha, bool mult_beta)
{
if (cfg.is_row_major)
{
source.append(" unsigned int row_gid = get_global_id(0) / get_local_size(0);\n");
source.append(" unsigned int col_gid = get_global_id(0) % get_local_size(0);\n");
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_num_groups(0))\n");
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_local_size(0))\n");
}
else
{
source.append(" unsigned int col_gid = get_global_id(0) / get_local_size(0);\n");
source.append(" unsigned int row_gid = get_global_id(0) % get_local_size(0);\n");
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_num_groups(0))\n");
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_local_size(0))\n");
}
if (cfg.with_stride_and_range)
{
if (cfg.is_row_major)
source.append(" A[(row * A_inc1 + A_start1) * A_internal_size2 + (col * A_inc2 + A_start2)] ");
else
source.append(" A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] ");
source.append(cfg.assign_op);
if (cfg.is_row_major)
source.append(" B[(row * B_inc1 + B_start1) * B_internal_size2 + (col * B_inc2 + B_start2)] ");
else
source.append(" B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] ");
if (mult_alpha)
source.append("* alpha ");
else
source.append("/ alpha ");
if (cfg.b != VIENNACL_AMBM_NONE)
{
if (cfg.is_row_major)
source.append("+ C[(row * C_inc1 + C_start1) * C_internal_size2 + (col * C_inc2 + C_start2)] ");
else
source.append("+ C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] ");
if (mult_beta)
source.append("* beta");
else
source.append("/ beta");
}
}
else
{
if (cfg.is_row_major)
source.append(" A[row * A_internal_size2 + col] ");
else
source.append(" A[row + col * A_internal_size1] ");
source.append(cfg.assign_op);
if (cfg.is_row_major)
source.append(" B[row * B_internal_size2 + col] ");
else
source.append(" B[row + col * B_internal_size1] ");
if (mult_alpha)
source.append("* alpha ");
else
source.append("/ alpha ");
if (cfg.b != VIENNACL_AMBM_NONE)
{
if (cfg.is_row_major)
source.append("+ C[row * C_internal_size2 + col] ");
else
source.append("+ C[row + col * C_internal_size2] ");
if (mult_beta)
source.append("* beta");
else
source.append("/ beta");
}
}
source.append("; \n");
}
template <typename StringType>
void generate_ambm_impl(StringType & source, std::string const & numeric_string, ambm_config const & cfg)
{
source.append("__kernel void am");
if (cfg.b != VIENNACL_AMBM_NONE)
source.append("bm");
if (cfg.assign_op != "=")
source.append("_m");
if (cfg.a == VIENNACL_AMBM_CPU)
source.append("_cpu");
else if (cfg.a == VIENNACL_AMBM_GPU)
source.append("_gpu");
if (cfg.b == VIENNACL_AMBM_CPU)
source.append("_cpu");
else if (cfg.b == VIENNACL_AMBM_GPU)
source.append("_gpu");
source.append("( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * A, \n");
source.append(" unsigned int A_start1, unsigned int A_start2, \n");
source.append(" unsigned int A_inc1, unsigned int A_inc2, \n");
source.append(" unsigned int A_size1, unsigned int A_size2, \n");
source.append(" unsigned int A_internal_size1, unsigned int A_internal_size2, \n");
if (cfg.a == VIENNACL_AMBM_CPU)
{
source.append(" "); source.append(numeric_string); source.append(" fac2, \n");
}
else if (cfg.a == VIENNACL_AMBM_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(" * B, \n");
source.append(" unsigned int B_start1, unsigned int B_start2, \n");
source.append(" unsigned int B_inc1, unsigned int B_inc2, \n");
source.append(" unsigned int B_internal_size1, unsigned int B_internal_size2");
if (cfg.b != VIENNACL_AMBM_NONE)
{
source.append(", \n\n");
if (cfg.b == VIENNACL_AMBM_CPU)
{
source.append(" "); source.append(numeric_string); source.append(" fac3, \n");
}
else if (cfg.b == VIENNACL_AMBM_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(" * C, \n");
source.append(" unsigned int C_start1, unsigned int C_start2, \n");
source.append(" unsigned int C_inc1, unsigned int C_inc2, \n");
source.append(" unsigned int C_internal_size1, unsigned int C_internal_size2 \n");
}
source.append(") { \n");
if (cfg.a == VIENNACL_AMBM_CPU)
{
source.append(" "); source.append(numeric_string); source.append(" alpha = fac2; \n");
}
else if (cfg.a == VIENNACL_AMBM_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_AMBM_CPU)
{
source.append(" "); source.append(numeric_string); source.append(" beta = fac3; \n");
}
else if (cfg.b == VIENNACL_AMBM_GPU)
{
source.append(" "); source.append(numeric_string); source.append(" beta = fac3[0]; \n");
}
if (cfg.b != VIENNACL_AMBM_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_AMBM_NONE)
{
source.append(" if (options3 & (1 << 1)) {\n");
generate_ambm_impl2(source, cfg, false, false);
source.append(" } else {\n");
generate_ambm_impl2(source, cfg, false, true);
source.append(" } \n");
}
else
generate_ambm_impl2(source, cfg, false, true);
source.append(" } else { \n");
if (cfg.b != VIENNACL_AMBM_NONE)
{
source.append(" if (options3 & (1 << 1)) {\n");
generate_ambm_impl2(source, cfg, true, false);
source.append(" } else {\n");
generate_ambm_impl2(source, cfg, true, true);
source.append(" } \n");
}
else
generate_ambm_impl2(source, cfg, true, true);
source.append(" } \n");
source.append("} \n");
}
template <typename StringType>
void generate_ambm(StringType & source, std::string const & numeric_string, bool is_row_major)
{
ambm_config cfg;
cfg.assign_op = "=";
cfg.with_stride_and_range = true;
cfg.is_row_major = is_row_major;
// am
cfg.b = VIENNACL_AMBM_NONE; cfg.a = VIENNACL_AMBM_CPU; generate_ambm_impl(source, numeric_string, cfg);
cfg.b = VIENNACL_AMBM_NONE; cfg.a = VIENNACL_AMBM_GPU; generate_ambm_impl(source, numeric_string, cfg);
// ambm
cfg.a = VIENNACL_AMBM_CPU; cfg.b = VIENNACL_AMBM_CPU; generate_ambm_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AMBM_CPU; cfg.b = VIENNACL_AMBM_GPU; generate_ambm_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AMBM_GPU; cfg.b = VIENNACL_AMBM_CPU; generate_ambm_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AMBM_GPU; cfg.b = VIENNACL_AMBM_GPU; generate_ambm_impl(source, numeric_string, cfg);
// ambm_m
cfg.assign_op = "+=";
cfg.a = VIENNACL_AMBM_CPU; cfg.b = VIENNACL_AMBM_CPU; generate_ambm_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AMBM_CPU; cfg.b = VIENNACL_AMBM_GPU; generate_ambm_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AMBM_GPU; cfg.b = VIENNACL_AMBM_CPU; generate_ambm_impl(source, numeric_string, cfg);
cfg.a = VIENNACL_AMBM_GPU; cfg.b = VIENNACL_AMBM_GPU; generate_ambm_impl(source, numeric_string, cfg);
}
template <typename StringType>
void generate_assign_cpu(StringType & source, std::string const & numeric_string, bool is_row_major)
{
source.append("__kernel void assign_cpu( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * A, \n");
source.append(" unsigned int A_start1, unsigned int A_start2, \n");
source.append(" unsigned int A_inc1, unsigned int A_inc2, \n");
source.append(" unsigned int A_size1, unsigned int A_size2, \n");
source.append(" unsigned int A_internal_size1, unsigned int A_internal_size2, \n");
source.append(" "); source.append(numeric_string); source.append(" alpha) \n");
source.append("{ \n");
if (is_row_major)
{
source.append(" unsigned int row_gid = get_global_id(0) / get_local_size(0);\n");
source.append(" unsigned int col_gid = get_global_id(0) % get_local_size(0);\n");
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_num_groups(0))\n");
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_local_size(0))\n");
source.append(" A[(row * A_inc1 + A_start1) * A_internal_size2 + (col * A_inc2 + A_start2)] = alpha; \n");
}
else
{
source.append(" unsigned int row_gid = get_global_id(0) % get_local_size(0);\n");
source.append(" unsigned int col_gid = get_global_id(0) / get_local_size(0);\n");
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_num_groups(0))\n");
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_local_size(0))\n");
source.append(" A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = alpha; \n");
}
source.append("} \n");
}
template <typename StringType>
void generate_diagonal_assign_cpu(StringType & source, std::string const & numeric_string, bool is_row_major)
{
source.append("__kernel void diagonal_assign_cpu( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * A, \n");
source.append(" unsigned int A_start1, unsigned int A_start2, \n");
source.append(" unsigned int A_inc1, unsigned int A_inc2, \n");
source.append(" unsigned int A_size1, unsigned int A_size2, \n");
source.append(" unsigned int A_internal_size1, unsigned int A_internal_size2, \n");
source.append(" "); source.append(numeric_string); source.append(" alpha) \n");
source.append("{ \n");
source.append(" for (unsigned int idx = get_global_id(0); idx < min(A_size1, A_size2); idx += get_global_size(0))\n");
if (is_row_major)
source.append(" A[(idx * A_inc1 + A_start1) * A_internal_size2 + (idx * A_inc2 + A_start2)] = alpha; \n");
else
source.append(" A[(idx * A_inc1 + A_start1) + (idx * A_inc2 + A_start2) * A_internal_size1] = alpha; \n");
source.append("} \n");
}
template <typename StringType>
void generate_element_op(StringType & source, std::string const & numeric_string, bool is_row_major)
{
source.append("__kernel void element_op( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * A, \n");
source.append(" unsigned int A_start1, unsigned int A_start2, \n");
source.append(" unsigned int A_inc1, unsigned int A_inc2, \n");
source.append(" unsigned int A_size1, unsigned int A_size2, \n");
source.append(" unsigned int A_internal_size1, unsigned int A_internal_size2, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * B, \n");
source.append(" unsigned int B_start1, unsigned int B_start2, \n");
source.append(" unsigned int B_inc1, unsigned int B_inc2, \n");
source.append(" unsigned int B_internal_size1, unsigned int B_internal_size2, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * C, \n");
source.append(" unsigned int C_start1, unsigned int C_start2, \n");
source.append(" unsigned int C_inc1, unsigned int C_inc2, \n");
source.append(" unsigned int C_internal_size1, unsigned int C_internal_size2, \n");
source.append(" unsigned int op_type) \n"); //0: product, 1: division, 2: pow
source.append("{ \n");
if (is_row_major)
{
source.append(" unsigned int row_gid = get_global_id(0) / get_local_size(0);\n");
source.append(" unsigned int col_gid = get_global_id(0) % get_local_size(0);\n");
source.append(" if (op_type == 2) {");
if (numeric_string == "float" || numeric_string == "double")
{
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_num_groups(0))\n");
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_local_size(0))\n");
source.append(" A[(row * A_inc1 + A_start1) * A_internal_size2 + (col * A_inc2 + A_start2)] = \n");
source.append(" pow(B[(row * B_inc1 + B_start1) * B_internal_size2 + (col * B_inc2 + B_start2)], \n");
source.append(" C[(row * C_inc1 + C_start1) * C_internal_size2 + (col * C_inc2 + C_start2)]); \n");
}
source.append(" } else if (op_type == 1) {");
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_num_groups(0))\n");
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_local_size(0))\n");
source.append(" A[(row * A_inc1 + A_start1) * A_internal_size2 + (col * A_inc2 + A_start2)] = \n");
source.append(" B[(row * B_inc1 + B_start1) * B_internal_size2 + (col * B_inc2 + B_start2)] / \n");
source.append(" C[(row * C_inc1 + C_start1) * C_internal_size2 + (col * C_inc2 + C_start2)]; \n");
source.append(" } else if (op_type == 0) {");
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_num_groups(0))\n");
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_local_size(0))\n");
source.append(" A[(row * A_inc1 + A_start1) * A_internal_size2 + (col * A_inc2 + A_start2)] = \n");
source.append(" B[(row * B_inc1 + B_start1) * B_internal_size2 + (col * B_inc2 + B_start2)] * \n");
source.append(" C[(row * C_inc1 + C_start1) * C_internal_size2 + (col * C_inc2 + C_start2)]; \n");
source.append(" }");
}
else
{
source.append(" unsigned int row_gid = get_global_id(0) % get_local_size(0);\n");
source.append(" unsigned int col_gid = get_global_id(0) / get_local_size(0);\n");
source.append(" if (op_type == 2) {");
if (numeric_string == "float" || numeric_string == "double")
{
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_num_groups(0))\n");
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_local_size(0))\n");
source.append(" A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = \n");
source.append(" pow(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1], \n");
source.append(" C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1]); \n");
}
source.append(" } else if (op_type == 1) {");
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_num_groups(0))\n");
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_local_size(0))\n");
source.append(" A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = \n");
source.append(" B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / \n");
source.append(" C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1]; \n");
source.append(" } else if (op_type == 0) {");
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_num_groups(0))\n");
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_local_size(0))\n");
source.append(" A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = \n");
source.append(" B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * \n");
source.append(" C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1]; \n");
source.append(" }");
}
source.append("} \n");
}
template<typename StringT>
void generate_fft(StringT & source, std::string const & numeric_string, bool is_row_major)
{
// naive fourier transform (quadratic complexity, use for reference only)
source.append("__kernel void fft_direct(__global "); source.append(numeric_string); source.append("2 *input, \n");
source.append(" __global "); source.append(numeric_string); source.append("2 *output, \n");
source.append(" unsigned int size, \n");
source.append(" unsigned int stride, \n");
source.append(" unsigned int batch_num, \n");
source.append(" "); source.append(numeric_string); source.append(" sign) { \n");
source.append(" const "); source.append(numeric_string); source.append(" NUM_PI = 3.14159265358979323846; \n");
source.append(" \n");
source.append(" for (unsigned int batch_id = 0; batch_id < batch_num; batch_id++) { \n");
source.append(" for (unsigned int k = get_global_id(0); k < size; k += get_global_size(0)) { \n");
source.append(" "); source.append(numeric_string); source.append("2 f = 0.0f; \n");
source.append(" \n");
source.append(" for (unsigned int n = 0; n < size; n++) { \n");
source.append(" "); source.append(numeric_string); source.append("2 in = ");
if (is_row_major)
source.append("input[batch_id * stride + n]; \n"); //input index here
else
source.append("input[n * stride + batch_id]; \n"); //input index here
source.append(" \n");
source.append(" "); source.append(numeric_string); source.append(" sn, cs; \n");
source.append(" "); source.append(numeric_string); source.append(" arg = sign * 2 * NUM_PI * k / size * n; \n");
source.append(" sn = sincos(arg, &cs); \n");
source.append(" \n");
source.append(" "); source.append(numeric_string); source.append("2 ex = ("); source.append(numeric_string); source.append("2)(cs, sn); \n");
source.append(" f = f + ("); source.append(numeric_string); source.append("2)(in.x * ex.x - in.y * ex.y, in.x * ex.y + in.y * ex.x); \n");
source.append(" } \n");
source.append(" \n");
if (is_row_major)
source.append(" output[batch_id * stride + k] = f; \n"); // output index here
else
source.append(" output[k * stride + batch_id] = f; \n"); // output index here
source.append(" } \n");
source.append(" } \n");
source.append("} \n");
source.append(" \n");
source.append("__kernel void fft_radix2(__global "); source.append(numeric_string); source.append("2* input, \n");
source.append(" unsigned int s, \n");
source.append(" unsigned int bit_size, \n");
source.append(" unsigned int size, \n");
source.append(" unsigned int stride, \n");
source.append(" unsigned int batch_num, \n");
source.append(" "); source.append(numeric_string); source.append(" sign) { \n");
source.append(" \n");
source.append(" unsigned int ss = 1 << s; \n");
source.append(" unsigned int half_size = size >> 1; \n");
source.append(" \n");
source.append(" "); source.append(numeric_string); source.append(" cs, sn; \n");
source.append(" const "); source.append(numeric_string); source.append(" NUM_PI = 3.14159265358979323846; \n");
source.append(" \n");
source.append(" unsigned int glb_id = get_global_id(0); \n");
source.append(" unsigned int glb_sz = get_global_size(0); \n");
source.append(" for (unsigned int batch_id = 0; batch_id < batch_num; batch_id++) { \n");
source.append(" for (unsigned int tid = glb_id; tid < half_size; tid += glb_sz) { \n");
source.append(" unsigned int group = (tid & (ss - 1)); \n");
source.append(" unsigned int pos = ((tid >> s) << (s + 1)) + group; \n");
if (is_row_major)
{
source.append(" unsigned int offset = batch_id * stride + pos; \n");
source.append(" "); source.append(numeric_string); source.append("2 in1 = input[offset]; \n"); //index
source.append(" "); source.append(numeric_string); source.append("2 in2 = input[offset + ss]; \n");//index
}
else
{
source.append(" unsigned int offset = pos * stride + batch_id; \n");
source.append(" "); source.append(numeric_string); source.append("2 in1 = input[offset]; \n"); //index
source.append(" "); source.append(numeric_string); source.append("2 in2 = input[offset + ss * stride]; \n");//index
}
source.append(" "); source.append(numeric_string); source.append(" arg = group * sign * NUM_PI / ss; \n");
source.append(" sn = sincos(arg, &cs); \n");
source.append(" "); source.append(numeric_string); source.append("2 ex = ("); source.append(numeric_string); source.append("2)(cs, sn); \n");
source.append(" "); source.append(numeric_string); source.append("2 tmp = ("); source.append(numeric_string); source.append("2)(in2.x * ex.x - in2.y * ex.y, in2.x * ex.y + in2.y * ex.x); \n");
if (is_row_major)
source.append(" input[offset + ss] = in1 - tmp; \n");//index
else
source.append(" input[offset + ss * stride] = in1 - tmp; \n");//index
source.append(" input[offset] = in1 + tmp; \n");//index
source.append(" } \n");
source.append(" } \n");
source.append("} \n");
source.append(" \n");
source.append(" unsigned int get_reorder_num(unsigned int v, unsigned int bit_size) { \n");
source.append(" v = ((v >> 1) & 0x55555555) | ((v & 0x55555555) << 1); \n");
source.append(" v = ((v >> 2) & 0x33333333) | ((v & 0x33333333) << 2); \n");
source.append(" v = ((v >> 4) & 0x0F0F0F0F) | ((v & 0x0F0F0F0F) << 4); \n");
source.append(" v = ((v >> 8) & 0x00FF00FF) | ((v & 0x00FF00FF) << 8); \n");
source.append(" v = (v >> 16) | (v << 16); \n");
source.append(" \n");
source.append(" v = v >> (32 - bit_size); \n");
source.append(" \n");
source.append(" return v; \n");
source.append(" } \n");
source.append(" __kernel void fft_radix2_local(__global "); source.append(numeric_string); source.append("2* input, \n");
source.append(" __local "); source.append(numeric_string); source.append("2* lcl_input, \n");
source.append(" unsigned int bit_size, \n");
source.append(" unsigned int size, \n");
source.append(" unsigned int stride, \n");
source.append(" unsigned int batch_num, \n");
source.append(" "); source.append(numeric_string); source.append(" sign) { \n");
source.append(" unsigned int grp_id = get_group_id(0); \n");
source.append(" unsigned int grp_num = get_num_groups(0); \n");
source.append(" unsigned int lcl_sz = get_local_size(0); \n");
source.append(" unsigned int lcl_id = get_local_id(0); \n");
source.append(" const "); source.append(numeric_string); source.append(" NUM_PI = 3.14159265358979323846; \n");
source.append(" for (unsigned int batch_id = grp_id; batch_id < batch_num; batch_id += grp_num) { \n");
//unsigned int base_offset = stride * batch_id; \n");
//copy chunk of global memory to local \n");
source.append(" for (unsigned int p = lcl_id; p < size; p += lcl_sz) { \n");
source.append(" unsigned int v = get_reorder_num(p, bit_size); \n");
if (is_row_major)
source.append(" lcl_input[v] = input[batch_id * stride + p]; \n"); //index
else
source.append(" lcl_input[v] = input[p * stride + batch_id]; \n"); //index
source.append(" } \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
//performs Cooley-Tukey FFT on local array
source.append(" for (unsigned int s = 0; s < bit_size; s++) { \n");
source.append(" unsigned int ss = 1 << s; \n");
source.append(" "); source.append(numeric_string); source.append(" cs, sn; \n");
source.append(" for (unsigned int tid = lcl_id; tid < size; tid += lcl_sz) { \n");
source.append(" unsigned int group = (tid & (ss - 1)); \n");
source.append(" unsigned int pos = ((tid >> s) << (s + 1)) + group; \n");
source.append(" "); source.append(numeric_string); source.append("2 in1 = lcl_input[pos]; \n");
source.append(" "); source.append(numeric_string); source.append("2 in2 = lcl_input[pos + ss]; \n");
source.append(" "); source.append(numeric_string); source.append(" arg = group * sign * NUM_PI / ss; \n");
source.append(" sn = sincos(arg, &cs); \n");
source.append(" "); source.append(numeric_string); source.append("2 ex = ("); source.append(numeric_string); source.append("2)(cs, sn); \n");
source.append(" "); source.append(numeric_string); source.append("2 tmp = ("); source.append(numeric_string); source.append("2)(in2.x * ex.x - in2.y * ex.y, in2.x * ex.y + in2.y * ex.x); \n");
source.append(" lcl_input[pos + ss] = in1 - tmp; \n");
source.append(" lcl_input[pos] = in1 + tmp; \n");
source.append(" } \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" } \n");
//copy local array back to global memory
source.append(" for (unsigned int p = lcl_id; p < size; p += lcl_sz) { \n");
if (is_row_major)
source.append(" input[batch_id * stride + p] = lcl_input[p]; \n");//index
else
source.append(" input[p * stride + batch_id] = lcl_input[p]; \n");//index
source.append(" } \n");
source.append(" } \n");
source.append(" } \n");
source.append(" \n");
//
// Performs reordering of input data in bit-reversal order
// Probably it's better to do in host side,
//
source.append("unsigned int get_reorder_num_2(unsigned int v, unsigned int bit_size) { \n");
source.append(" v = ((v >> 1) & 0x55555555) | ((v & 0x55555555) << 1); \n");
source.append(" v = ((v >> 2) & 0x33333333) | ((v & 0x33333333) << 2); \n");
source.append(" v = ((v >> 4) & 0x0F0F0F0F) | ((v & 0x0F0F0F0F) << 4); \n");
source.append(" v = ((v >> 8) & 0x00FF00FF) | ((v & 0x00FF00FF) << 8); \n");
source.append(" v = (v >> 16) | (v << 16); \n");
source.append(" v = v >> (32 - bit_size); \n");
source.append(" return v; \n");
source.append("} \n");
source.append("__kernel void fft_reorder(__global "); source.append(numeric_string); source.append("2* input, \n");
source.append(" unsigned int bit_size, \n");
source.append(" unsigned int size, \n");
source.append(" unsigned int stride, \n");
source.append(" int batch_num) { \n");
source.append(" unsigned int glb_id = get_global_id(0); \n");
source.append(" unsigned int glb_sz = get_global_size(0); \n");
source.append(" for (unsigned int batch_id = 0; batch_id < batch_num; batch_id++) { \n");
source.append(" for (unsigned int i = glb_id; i < size; i += glb_sz) { \n");
source.append(" unsigned int v = get_reorder_num_2(i, bit_size); \n");
source.append(" if (i < v) {\n");
if (is_row_major)
{
source.append(" "); source.append(numeric_string); source.append("2 tmp = input[batch_id * stride + i]; \n"); // index
source.append(" input[batch_id * stride + i] = input[batch_id * stride + v]; \n"); //index
source.append(" input[batch_id * stride + v] = tmp; \n"); //index
}
else
{
source.append(" "); source.append(numeric_string); source.append("2 tmp = input[i * stride + batch_id]; \n"); // index
source.append(" input[i * stride + batch_id] = input[v * stride + batch_id]; \n"); //index
source.append(" input[v * stride + batch_id] = tmp; \n"); //index
}
source.append(" } \n");
source.append(" } \n");
source.append(" } \n");
source.append("} \n");
}
template<typename StringT>
void generate_lu(StringT & source, std::string const & numeric_string, bool is_row_major)
{
source.append("__kernel void lu_factorize( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * matrix, \n");
source.append(" unsigned int matrix_rows, \n");
source.append(" unsigned int matrix_cols, \n");
source.append(" unsigned int matrix_internal_rows, \n");
source.append(" unsigned int matrix_internal_cols) \n");
source.append("{ \n");
source.append(" "); source.append(numeric_string); source.append(" temp; \n");
if (is_row_major)
{
source.append(" unsigned rowi; \n");
source.append(" unsigned rowk; \n");
source.append(" for (unsigned int i=1; i<matrix_rows; ++i) \n");
source.append(" { \n");
source.append(" rowi = i * matrix_internal_cols; \n");
source.append(" for (unsigned int k=0; k<i; ++k) \n");
source.append(" { \n");
source.append(" rowk = k * matrix_internal_cols; \n");
source.append(" if (get_global_id(0) == 0) \n");
source.append(" matrix[rowi + k] /= matrix[rowk + k]; \n");
source.append(" barrier(CLK_GLOBAL_MEM_FENCE); \n");
source.append(" temp = matrix[rowi + k]; \n");
//parallel subtraction:
source.append(" for (unsigned int j=k+1 + get_global_id(0); j<matrix_rows; j += get_global_size(0)) \n");
source.append(" matrix[rowi + j] -= temp * matrix[rowk + j]; \n");
}
else
{
source.append(" for (unsigned int i=1; i<matrix_rows; ++i) \n");
source.append(" { \n");
source.append(" for (unsigned int k=0; k<i; ++k) \n");
source.append(" { \n");
source.append(" if (get_global_id(0) == 0) \n");
source.append(" matrix[i + k*matrix_internal_rows] /= matrix[k + k*matrix_internal_rows]; \n");
source.append(" barrier(CLK_GLOBAL_MEM_FENCE); \n");
source.append(" temp = matrix[i + k*matrix_internal_rows]; \n");
//parallel subtraction:
source.append(" for (unsigned int j=k+1 + get_global_id(0); j<matrix_cols; j += get_global_size(0)) \n");
source.append(" matrix[i + j*matrix_internal_rows] -= temp * matrix[k + j*matrix_internal_rows]; \n");
}
source.append(" }");
source.append(" }");
source.append("}");
}
template<typename StringT>
void generate_scaled_rank1_update(StringT & source, std::string const & numeric_string, bool is_row_major, bool alpha_on_cpu)
{
source.append("__kernel void scaled_rank1_update_"); alpha_on_cpu ? source.append("cpu") : source.append("gpu"); source.append("( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * A, \n");
source.append(" unsigned int A_start1, unsigned int A_start2, \n");
source.append(" unsigned int A_inc1, unsigned int A_inc2, \n");
source.append(" unsigned int A_size1, unsigned int A_size2, \n");
source.append(" unsigned int A_internal_size1, unsigned int A_internal_size2, \n");
if (alpha_on_cpu) {
source.append(" "); source.append(numeric_string); source.append(" val, \n");
} else {
source.append(" __global const "); source.append(numeric_string); source.append(" *val, \n");
}
source.append(" unsigned int options2, \n");
source.append(" __global const "); 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 const "); 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");
if (alpha_on_cpu) {
source.append(" "); source.append(numeric_string); source.append(" alpha = val; \n");
} else {
source.append(" "); source.append(numeric_string); source.append(" alpha = val[0]; \n");
}
source.append(" if (options2 & (1 << 0)) \n");
source.append(" alpha = -alpha; \n");
source.append(" unsigned int row_gid = get_global_id(0) / get_local_size(0); \n");
source.append(" unsigned int col_gid = get_global_id(0) % get_local_size(0); \n");
source.append(" for (unsigned int row = row_gid; row < A_size1; row += get_num_groups(0)) \n");
source.append(" { \n");
source.append(" "); source.append(numeric_string); source.append(" tmp = vec1[row * inc1 + start1];");
source.append(" tmp = (options2 & (1 << 1)) ? tmp / alpha : tmp * alpha;");
source.append(" for (unsigned int col = col_gid; col < A_size2; col += get_local_size(0)) \n");
if (is_row_major)
source.append(" A[(row * A_inc1 + A_start1) * A_internal_size2 + col * A_inc2 + A_start2] += tmp * vec2[col * inc2 + start2]; \n");
else
source.append(" A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] += tmp * vec2[col * inc2 + start2]; \n");
source.append(" } \n");
source.append("} \n");
}
template <typename StringType>
void generate_trans_vec_mul(StringType & source, std::string const & numeric_string, bool is_row_major)
{
source.append("__kernel void trans_vec_mul( \n");
source.append(" __global const "); source.append(numeric_string); source.append(" * A, \n");
source.append(" unsigned int A_row_start, unsigned int A_col_start, \n");
source.append(" unsigned int A_row_inc, unsigned int A_col_inc, \n");
source.append(" unsigned int A_row_size, unsigned int A_col_size, \n");
source.append(" unsigned int A_internal_rows, unsigned int A_internal_cols, \n");
source.append(" __global const "); source.append(numeric_string); source.append(" * v, \n");
source.append(" unsigned int v_start, unsigned int v_inc, unsigned int v_size, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * result, \n");
source.append(" unsigned int result_start, unsigned int result_inc, unsigned int result_size, \n");
source.append(" __local "); source.append(numeric_string); source.append(" * work) \n");
source.append("{ \n");
if (is_row_major)
{
source.append(" for (unsigned int row = get_global_id(0); row < A_col_size; row += get_global_size(0)) \n");
source.append(" { \n");
source.append(" "); source.append(numeric_string); source.append(" dot_prod = 0; \n");
source.append(" for (unsigned int col = 0; col < A_row_size; ++col) \n");
source.append(" dot_prod += A[(row * A_col_inc + A_col_start) + (col * A_row_inc + A_row_start) * A_internal_cols] * v[v_start + v_inc * col]; \n");
source.append(" result[row * result_inc + result_start] = dot_prod; \n");
}
else
{
source.append(" unsigned int row_gid = get_global_id(0) / get_local_size(0); \n");
source.append(" unsigned int col_gid = get_global_id(0) % get_local_size(0); \n");
source.append(" unsigned int lid = get_local_id(0); \n");
source.append(" for (unsigned int row = row_gid; row < A_col_size; row += get_num_groups(0)) \n");
source.append(" { \n");
source.append(" "); source.append(numeric_string); source.append(" dot_prod = 0; \n");
source.append(" for (unsigned int col = col_gid; col < A_row_size; col+=get_local_size(0)) \n");
source.append(" dot_prod += A[(row * A_col_inc + A_col_start) * A_internal_rows + col * A_row_inc + A_row_start] * v[v_start + v_inc * col]; \n");
source.append(" work[lid] = dot_prod; \n");
source.append(" for(unsigned int stride=get_local_size(0)/2 ; stride>0 ; stride>>=1){ \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if(lid < stride) \n");
source.append(" work[lid] += work[lid+stride]; \n");
source.append(" } \n");
source.append(" if(lid == 0) \n");
source.append(" result[row * result_inc + result_start] = work[0]; \n");
}
source.append(" } \n");
source.append("} \n");
}
template<typename StringT>
void generate_triangular_substitute_inplace(StringT & source, std::string const & numeric_string, bool is_row_major)
{
source.append("__kernel void triangular_substitute_inplace( \n");
source.append(" __global "); source.append(numeric_string); source.append(" * A, \n");
source.append(" unsigned int A_start1, unsigned int A_start2, \n");
source.append(" unsigned int A_inc1, unsigned int A_inc2, \n");
source.append(" unsigned int A_size1, unsigned int A_size2, \n");
source.append(" unsigned int A_internal_size1, unsigned int A_internal_size2, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * v, \n");
source.append(" unsigned int v_start, \n");
source.append(" unsigned int v_inc, \n");
source.append(" unsigned int v_size, \n");
source.append(" unsigned int options) \n");
source.append("{ \n");
source.append(" "); source.append(numeric_string); source.append(" temp; \n");
source.append(" unsigned int unit_diagonal_flag = (options & (1 << 0)); \n");
source.append(" unsigned int transposed_access_A = (options & (1 << 1)); \n");
source.append(" unsigned int is_lower_solve = (options & (1 << 2)); \n");
source.append(" unsigned int row; \n");
source.append(" for (unsigned int rows_processed = 0; rows_processed < A_size1; ++rows_processed) \n"); //Note: A required to be square
source.append(" { \n");
source.append(" row = is_lower_solve ? rows_processed : ((A_size1 - rows_processed) - 1); \n");
source.append(" barrier(CLK_GLOBAL_MEM_FENCE); \n");
source.append(" if (!unit_diagonal_flag) \n");
source.append(" { \n");
source.append(" if (get_global_id(0) == 0) \n");
if (is_row_major)
source.append(" v[row * v_inc + v_start] /= A[(row * A_inc1 + A_start1) * A_internal_size2 + (row * A_inc2 + A_start2)]; \n");
else
source.append(" v[row * v_inc + v_start] /= A[(row * A_inc1 + A_start1) + (row * A_inc2 + A_start2) * A_internal_size1]; \n");
source.append(" } \n");
source.append(" barrier(CLK_GLOBAL_MEM_FENCE); \n");
source.append(" temp = v[row * v_inc + v_start]; \n");
source.append(" for (int elim = (is_lower_solve ? (row + get_global_id(0) + 1) : get_global_id(0)); \n");
source.append(" elim < (is_lower_solve ? A_size1 : row); \n");
source.append(" elim += get_global_size(0)) \n");
if (is_row_major)
{
source.append(" v[elim * v_inc + v_start] -= temp * A[transposed_access_A ? ((row * A_inc1 + A_start1) * A_internal_size2 + (elim * A_inc2 + A_start2)) \n");
source.append(" : ((elim * A_inc1 + A_start1) * A_internal_size2 + (row * A_inc2 + A_start2))]; \n");
}
else
{
source.append(" v[elim * v_inc + v_start] -= temp * A[transposed_access_A ? ((row * A_inc1 + A_start1) + (elim * A_inc2 + A_start2) * A_internal_size1) \n");
source.append(" : ((elim * A_inc1 + A_start1) + (row * A_inc2 + A_start2) * A_internal_size1)]; \n");
}
source.append(" } \n");
source.append("} \n");
}
template <typename StringT>
void generate_trans_kernel(StringT & source, std::string const & numeric_string, bool is_row_major)
{
source.append("__kernel void trans_kernel(\n");
source.append(" __global const ");source.append(numeric_string);source.append(" * A, \n");
source.append(" unsigned int A_start1, unsigned int A_start2, \n");
source.append(" unsigned int A_internal_size1, unsigned int A_internal_size2, \n");
source.append(" unsigned int A_size1, unsigned int A_size2, \n");
source.append(" unsigned int A_stride1, unsigned int A_stride2, \n");
source.append(" __global ");source.append(numeric_string);source.append(" * B, \n");
source.append(" unsigned int B_start1, unsigned int B_start2, \n");
source.append(" unsigned int B_internal_size1, unsigned int B_internal_size2, \n");
source.append(" unsigned int B_stride1, unsigned int B_stride2) \n");
source.append("{ \n");
source.append(" for(unsigned int row = get_group_id(0); row < A_size1; row += get_num_groups(0))\n");
source.append(" { \n");
source.append(" for(unsigned int col = get_local_id(0); col < A_size2; col += get_local_size(0))\n");
source.append(" { \n");
if(is_row_major)
source.append(" B[(B_start1 + B_stride1 * col) * B_internal_size2 + (B_start2 + B_stride2 * row)] = A[(A_start1 + A_stride1 * row) * A_internal_size2 + (A_start2 + A_stride2 * col)]; \n");
else
source.append(" B[(B_start1 + B_stride1 * col) + (B_start2 + B_stride2 * row) * B_internal_size1] = A[(A_start1 + A_stride1 * row) + (A_start2 + A_stride2 * col) * A_internal_size1]; \n");
source.append(" } \n");
source.append(" } \n");
source.append("} \n");
}
template <typename StringType>
void generate_vec_mul(StringType & source, std::string const & numeric_string, bool is_row_major)
{
source.append("__kernel void vec_mul( \n");
source.append(" __global const "); source.append(numeric_string); source.append(" * A, \n");
source.append(" unsigned int A_row_start, unsigned int A_col_start, \n");
source.append(" unsigned int A_row_inc, unsigned int A_col_inc, \n");
source.append(" unsigned int A_row_size, unsigned int A_col_size, \n");
source.append(" unsigned int A_internal_rows, unsigned int A_internal_cols, \n");
source.append(" __global const "); source.append(numeric_string); source.append(" * v, \n");
source.append(" unsigned int v_start, unsigned int v_inc, unsigned int v_size, \n");
source.append(" __global "); source.append(numeric_string); source.append(" * result, \n");
source.append(" unsigned int result_start, unsigned int result_inc, unsigned int result_size, \n");
source.append(" __local "); source.append(numeric_string); source.append(" * work) \n");
source.append("{ \n");
if (is_row_major)
{
source.append(" unsigned int row_gid = get_global_id(0) / get_local_size(0); \n");
source.append(" unsigned int col_gid = get_global_id(0) % get_local_size(0); \n");
source.append(" unsigned int lid = get_local_id(0); \n");
source.append(" for (unsigned int row = row_gid; row < A_row_size; row += get_num_groups(0)) \n");
source.append(" { \n");
source.append(" "); source.append(numeric_string); source.append(" dot_prod = 0; \n");
source.append(" for (unsigned int col = col_gid; col < A_col_size; col+=get_local_size(0)) \n");
source.append(" dot_prod += A[(row * A_row_inc + A_row_start) * A_internal_cols + col * A_col_inc + A_col_start] * v[v_start + v_inc * col]; \n");
source.append(" work[lid] = dot_prod; \n");
source.append(" for(unsigned int stride=get_local_size(0)/2 ; stride>0 ; stride>>=1){ \n");
source.append(" barrier(CLK_LOCAL_MEM_FENCE); \n");
source.append(" if(lid < stride) \n");
source.append(" work[lid] += work[lid+stride]; \n");
source.append(" } \n");
source.append(" if(lid == 0) \n");
source.append(" result[row * result_inc + result_start] = work[0]; \n");
}
else
{
source.append(" for (unsigned int row = get_global_id(0); row < A_row_size; row += get_global_size(0)) \n");
source.append(" { \n");
source.append(" "); source.append(numeric_string); source.append(" dot_prod = 0; \n");
source.append(" for (unsigned int col = 0; col < A_col_size; ++col) \n");
source.append(" dot_prod += A[(row * A_row_inc + A_row_start) + (col * A_col_inc + A_col_start) * A_internal_rows] * v[v_start + v_inc * col]; \n");
source.append(" result[row * result_inc + result_start] = dot_prod; \n");
}
source.append(" } \n");
source.append("} \n");
}
namespace detail
{
inline std::string type_to_string(viennacl::row_major) { return "row"; }
inline std::string type_to_string(viennacl::column_major) { return "col"; }
}
// main kernel class
template <typename NumericT, typename F>
struct matrix
{
static std::string program_name()
{
return viennacl::ocl::type_to_string<NumericT>::apply() + "_matrix_" + detail::type_to_string(F());
}
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();
bool is_row_major = viennacl::is_row_major<F>::value;
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_ambm(source, numeric_string, is_row_major);
// kernels with mostly predetermined skeleton:
generate_assign_cpu(source, numeric_string, is_row_major);
generate_diagonal_assign_cpu(source, numeric_string, is_row_major);
generate_element_op(source, numeric_string, is_row_major);
generate_trans_vec_mul(source, numeric_string, is_row_major);
generate_vec_mul(source, numeric_string, is_row_major);
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 NumericT>
class matrix_prod
{
public:
static device_specific::execution_handler & execution_handler(bool is_row_major, viennacl::ocl::context & ctx)
{
static std::map<std::pair<bool, cl_context>, device_specific::execution_handler> handlers_map;
cl_context h = ctx.handle().get();
std::pair<bool, cl_context> key(is_row_major, h);
if (handlers_map.find(key) == handlers_map.end())
{
viennacl::ocl::DOUBLE_PRECISION_CHECKER<NumericT>::apply(ctx);
namespace ds = viennacl::device_specific;
viennacl::ocl::device const & device = ctx.current_device();
std::string program_name = viennacl::ocl::type_to_string<NumericT>::apply() + (is_row_major?"_matrix_prod_row":"_matrix_prod_col");
handlers_map.insert(std::make_pair(key, ds::execution_handler(program_name, ctx, device)));
ds::execution_handler & handler = viennacl::device_specific::at(handlers_map, key);
ds::matrix_product_template::parameters_type matrix_product_params_NN = ds::builtin_database::matrix_product_params<NumericT>(device, 'N', 'N');
ds::matrix_product_template::parameters_type matrix_product_params_TN = ds::builtin_database::matrix_product_params<NumericT>(device, 'T', 'N');
ds::matrix_product_template::parameters_type matrix_product_params_NT = ds::builtin_database::matrix_product_params<NumericT>(device, 'N', 'T');
ds::matrix_product_template::parameters_type matrix_product_params_TT = ds::builtin_database::matrix_product_params<NumericT>(device, 'T', 'T');
tools::shared_ptr<viennacl::matrix_base<NumericT> > pC;
if (is_row_major)
pC.reset(new viennacl::matrix<NumericT, viennacl::row_major>());
else
pC.reset(new viennacl::matrix<NumericT, viennacl::column_major>());
//Dummy types. The values don't matter for the kernel generation.
viennacl::matrix_base<NumericT>& C = *pC;
viennacl::matrix<NumericT, viennacl::column_major> A;
viennacl::matrix<NumericT, viennacl::column_major> B;
NumericT alpha = 1;
NumericT beta = 0;
handler.add("prod_NN", ds::matrix_product_template(matrix_product_params_NN, 'N', 'N'), scheduler::preset::mat_mat_prod(alpha, &A, false, &B, false, beta, &C));
handler.add("prod_TN", ds::matrix_product_template(matrix_product_params_TN, 'T', 'N'), scheduler::preset::mat_mat_prod(alpha, &A, true, &B, false, beta, &C));
handler.add("prod_NT", ds::matrix_product_template(matrix_product_params_NT, 'N', 'T'), scheduler::preset::mat_mat_prod(alpha, &A, false, &B, true, beta, &C));
handler.add("prod_TT", ds::matrix_product_template(matrix_product_params_TT, 'T', 'T'), scheduler::preset::mat_mat_prod(alpha, &A, true, &B, true, beta, &C));
}
return viennacl::device_specific::at(handlers_map, key);
}
};
// main kernel class
template<typename NumericT, typename LayoutT>
struct matrix_legacy
{
static std::string program_name()
{
return viennacl::ocl::type_to_string<NumericT>::apply() + "_matrix_legacy_" + detail::type_to_string(LayoutT());
}
static void init(viennacl::ocl::context & ctx)
{
static std::map<cl_context, bool> init_done;
if (!init_done[ctx.handle().get()])
{
viennacl::ocl::DOUBLE_PRECISION_CHECKER<NumericT>::apply(ctx);
std::string numeric_string = viennacl::ocl::type_to_string<NumericT>::apply();
bool is_row_major = viennacl::is_row_major<LayoutT>::value;
std::string source;
source.reserve(8192);
viennacl::ocl::append_double_precision_pragma<NumericT>(ctx, source);
// kernels with mostly predetermined skeleton:
generate_scaled_rank1_update(source, numeric_string, is_row_major, true);
generate_scaled_rank1_update(source, numeric_string, is_row_major, false);
if (numeric_string == "float" || numeric_string == "double")
{
generate_fft(source, numeric_string, is_row_major);
generate_lu(source, numeric_string, is_row_major);
generate_triangular_substitute_inplace(source, numeric_string, is_row_major);
generate_trans_kernel(source, numeric_string, is_row_major);
}
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_matrix_convert_row(StringT & source, std::string const & dest_type, std::string const & src_type)
{
source.append(" __kernel void convert_row_" + dest_type + "_" + src_type + "( \n");
source.append(" __global " + dest_type + " * dest, \n");
source.append(" unsigned int start1_dest, unsigned int inc1_dest, unsigned int size1_dest, unsigned int internal_size1_dest, \n");
source.append(" unsigned int start2_dest, unsigned int inc2_dest, unsigned int size2_dest, unsigned int internal_size2_dest, \n");
source.append(" __global const " + src_type + " * src, \n");
source.append(" unsigned int start1_src, unsigned int inc1_src, unsigned int size1_src, unsigned int internal_size1_src, \n");
source.append(" unsigned int start2_src, unsigned int inc2_src, unsigned int size2_src, unsigned int internal_size2_src) \n");
source.append(" { \n");
source.append(" for (unsigned int i = get_group_id(0); i < size1_dest; i += get_num_groups(0)) \n");
source.append(" for (unsigned int j = get_local_id(0); j < size2_dest; j += get_local_size(0)) \n");
source.append(" dest[(start1_dest + i * inc1_dest) * internal_size2_dest + (start2_dest + j * inc2_dest)] = src[(start1_src + i * inc1_src) * internal_size2_src + (start2_src + j * inc2_src)]; \n");
source.append(" } \n");
}
template<typename StringT>
void generate_matrix_convert_col(StringT & source, std::string const & dest_type, std::string const & src_type)
{
source.append(" __kernel void convert_col_" + dest_type + "_" + src_type + "( \n");
source.append(" __global " + dest_type + " * dest, \n");
source.append(" unsigned int start1_dest, unsigned int inc1_dest, unsigned int size1_dest, unsigned int internal_size1_dest, \n");
source.append(" unsigned int start2_dest, unsigned int inc2_dest, unsigned int size2_dest, unsigned int internal_size2_dest, \n");
source.append(" __global const " + src_type + " * src, \n");
source.append(" unsigned int start1_src, unsigned int inc1_src, unsigned int size1_src, unsigned int internal_size1_src, \n");
source.append(" unsigned int start2_src, unsigned int inc2_src, unsigned int size2_src, unsigned int internal_size2_src) \n");
source.append(" { \n");
source.append(" for (unsigned int j = get_group_id(0); j < size2_dest; j += get_num_groups(0)) \n");
source.append(" for (unsigned int i = get_local_id(0); i < size1_dest; i += get_local_size(0)) \n");
source.append(" dest[(start1_dest + i * inc1_dest) + (start2_dest + j * inc2_dest) * internal_size1_dest] = src[(start1_src + i * inc1_src) + (start2_src + j * inc2_src) * internal_size1_src]; \n");
source.append(" } \n");
}
template<typename StringT>
void generate_matrix_convert(StringT & source, std::string const & dest_type, std::string const & src_type)
{
generate_matrix_convert_row(source, dest_type, src_type);
generate_matrix_convert_col(source, dest_type, src_type);
}
struct matrix_convert
{
public:
static std::string program_name()
{
return "matrix_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_matrix_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<float>::apply());
// unsigned int
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<float>::apply());
// long
generate_matrix_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<float>::apply());
// unsigned long
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<float>::apply());
// float
generate_matrix_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_matrix_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_matrix_convert(source, viennacl::ocl::type_to_string<int>::apply(), viennacl::ocl::type_to_string<double>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned int>::apply(), viennacl::ocl::type_to_string<double>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<long>::apply(), viennacl::ocl::type_to_string<double>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<unsigned long>::apply(), viennacl::ocl::type_to_string<double>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<float>::apply(), viennacl::ocl::type_to_string<double>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<unsigned int>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<unsigned long>::apply());
generate_matrix_convert(source, viennacl::ocl::type_to_string<double>::apply(), viennacl::ocl::type_to_string<float>::apply());
generate_matrix_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
viennacl::linalg::opencl::kernels::matrix_prod
Main kernel class for generating OpenCL kernels for operations on/with viennacl::vector<> without inv...
Definition: matrix.hpp:969
platform.hpp
Implements a OpenCL platform within ViennaCL.
viennacl::linalg::opencl::kernels::generate_fft
void generate_fft(StringT &source, std::string const &numeric_string, bool is_row_major)
Definition: matrix.hpp:405
viennacl::linalg::opencl::kernels::generate_triangular_substitute_inplace
void generate_triangular_substitute_inplace(StringT &source, std::string const &numeric_string, bool is_row_major)
Definition: matrix.hpp:784
viennacl::device_specific::execution_handler
Definition: execution_handler.hpp:39
viennacl::linalg::opencl::kernels::generate_ambm_impl
void generate_ambm_impl(StringType &source, std::string const &numeric_string, ambm_config const &cfg)
Definition: matrix.hpp:143
viennacl::is_row_major
Helper class for checking whether a matrix has a row-major layout.
Definition: forwards.h:484
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::ambm_config::a
ambm_scalar_type a
Definition: matrix.hpp:59
viennacl::ocl::context
Manages an OpenCL context and provides the respective convenience functions for creating buffers...
Definition: context.hpp:55
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::matrix_base< NumericT >
viennacl::ocl::device
A class representing a compute device (e.g. a GPU)
Definition: device.hpp:49
viennacl::matrix
A dense matrix class.
Definition: forwards.h:375
viennacl::linalg::opencl::kernels::generate_element_op
void generate_element_op(StringType &source, std::string const &numeric_string, bool is_row_major)
Definition: matrix.hpp:328
viennacl::linalg::opencl::kernels::ambm_config::b
ambm_scalar_type b
Definition: matrix.hpp:60
viennacl::ocl::context::handle
const viennacl::ocl::handle< cl_context > & handle() const
Returns the context handle.
Definition: context.hpp:611
viennacl::linalg::opencl::kernels::matrix::init
static void init(viennacl::ocl::context &ctx)
Definition: matrix.hpp:933
NumericT
float NumericT
Definition: bisect.cpp:40
viennacl::linalg::opencl::kernels::generate_vec_mul
void generate_vec_mul(StringT &source, std::string const &numeric_string)
Definition: compressed_compressed_matrix.hpp:40
viennacl::linalg::opencl::kernels::matrix_prod::execution_handler
static device_specific::execution_handler & execution_handler(bool is_row_major, viennacl::ocl::context &ctx)
Definition: matrix.hpp:972
viennacl::ocl::DOUBLE_PRECISION_CHECKER::apply
static void apply(viennacl::ocl::context const &)
Definition: utils.hpp:40
viennacl::linalg::opencl::kernels::ambm_config::with_stride_and_range
bool with_stride_and_range
Definition: matrix.hpp:56
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
viennacl::linalg::opencl::kernels::generate_ambm
void generate_ambm(StringType &source, std::string const &numeric_string, bool is_row_major)
Definition: matrix.hpp:252
preset.hpp
viennacl::linalg::opencl::kernels::matrix::program_name
static std::string program_name()
Definition: matrix.hpp:928
viennacl::linalg::opencl::kernels::VIENNACL_AMBM_CPU
Definition: matrix.hpp:47
viennacl::linalg::opencl::kernels::matrix_legacy::init
static void init(viennacl::ocl::context &ctx)
Definition: matrix.hpp:1025
viennacl::linalg::opencl::kernels::matrix_legacy
Main kernel class for generating OpenCL kernels for operations on/with dense matrix objects of type v...
Definition: matrix.hpp:1018
viennacl::linalg::opencl::kernels::generate_trans_kernel
void generate_trans_kernel(StringT &source, std::string const &numeric_string, bool is_row_major)
Definition: matrix.hpp:838
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::tools::shared_ptr
A shared pointer class similar to boost::shared_ptr. Reimplemented in order to avoid a Boost-dependen...
Definition: shared_ptr.hpp:83
viennacl::linalg::opencl::kernels::generate_trans_vec_mul
void generate_trans_vec_mul(StringType &source, std::string const &numeric_string, bool is_row_major)
Definition: matrix.hpp:734
viennacl::linalg::opencl::kernels::matrix_legacy::program_name
static std::string program_name()
Definition: matrix.hpp:1020
viennacl::linalg::opencl::kernels::ambm_config::assign_op
std::string assign_op
Definition: matrix.hpp:58
matrix_product.hpp
viennacl::linalg::opencl::kernels::ambm_config
Configuration struct for generating OpenCL kernels for linear combinations of matrices.
Definition: matrix.hpp:52
viennacl::linalg::opencl::kernels::generate_ambm_impl2
void generate_ambm_impl2(StringType &source, ambm_config const &cfg, bool mult_alpha, bool mult_beta)
Definition: matrix.hpp:66
viennacl::linalg::opencl::kernels::generate_lu
void generate_lu(StringT &source, std::string const &numeric_string, bool is_row_major)
Definition: matrix.hpp:627
viennacl::scheduler::preset::mat_mat_prod
statement mat_mat_prod(NumericT alpha, viennacl::matrix_base< NumericT > const *A, bool A_trans, viennacl::matrix_base< NumericT > const *B, bool B_trans, NumericT beta, viennacl::matrix_base< NumericT > const *C)
Definition: preset.hpp:33
viennacl::linalg::opencl::kernels::matrix
Main kernel class for generating OpenCL kernels for operations on/with dense matrix objects of type v...
Definition: matrix.hpp:926
viennacl::linalg::opencl::kernels::matrix_convert
Main kernel class for vector conversion routines (e.g. convert vector<int> to vector<float>).
Definition: matrix.hpp:1106
viennacl::linalg::opencl::kernels::generate_scaled_rank1_update
void generate_scaled_rank1_update(StringT &source, std::string const &numeric_string, bool is_row_major, bool alpha_on_cpu)
Definition: matrix.hpp:682
kernel.hpp
Representation of an OpenCL kernel in ViennaCL.
viennacl::linalg::opencl::kernels::VIENNACL_AMBM_NONE
Definition: matrix.hpp:46
viennacl::linalg::opencl::kernels::ambm_config::ambm_config
ambm_config()
Definition: matrix.hpp:54
viennacl::linalg::opencl::kernels::detail::type_to_string
std::string type_to_string(viennacl::row_major)
Definition: matrix.hpp:917
viennacl::tools::shared_ptr::reset
void reset()
Definition: shared_ptr.hpp:123
viennacl::linalg::opencl::kernels::VIENNACL_AMBM_GPU
Definition: matrix.hpp:48
viennacl::linalg::opencl::kernels::generate_matrix_convert_col
void generate_matrix_convert_col(StringT &source, std::string const &dest_type, std::string const &src_type)
Definition: matrix.hpp:1082
viennacl::column_major
A tag for column-major storage of a dense matrix.
Definition: forwards.h:321
viennacl::linalg::opencl::kernels::ambm_scalar_type
ambm_scalar_type
Enumeration for the scalar type in ambm-like operations.
Definition: matrix.hpp:44
viennacl::linalg::opencl::kernels::generate_diagonal_assign_cpu
void generate_diagonal_assign_cpu(StringType &source, std::string const &numeric_string, bool is_row_major)
Definition: matrix.hpp:309
viennacl::linalg::opencl::kernels::generate_matrix_convert
void generate_matrix_convert(StringT &source, std::string const &dest_type, std::string const &src_type)
Definition: matrix.hpp:1099
viennacl::device_specific::at
ValueT const & at(std::map< KeyT, ValueT > const &map, KeyT const &key)
Emulation of C++11's .at() member for std::map<>, const-version.
Definition: forwards.h:142
viennacl::linalg::opencl::kernels::matrix_convert::init
static void init(viennacl::ocl::context &ctx)
Definition: matrix.hpp:1115
viennacl::ocl::type_to_string
Helper class for converting a type to its string representation.
Definition: utils.hpp:57
viennacl::row_major
A tag for row-major storage of a dense matrix.
Definition: forwards.h:304
viennacl::linalg::opencl::kernels::matrix_convert::program_name
static std::string program_name()
Definition: matrix.hpp:1110
execution_handler.hpp
Helper for handling fallbacks, lazy compilation, input-dependent kernels, etc.
viennacl::linalg::opencl::kernels::ambm_config::is_row_major
bool is_row_major
Definition: matrix.hpp:57
viennacl::linalg::opencl::kernels::generate_matrix_convert_row
void generate_matrix_convert_row(StringT &source, std::string const &dest_type, std::string const &src_type)
Definition: matrix.hpp:1065