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/* ************************************************************************
* Copyright (C) 2022 Advanced Micro Devices, Inc. All rights Reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*
* ************************************************************************ */
#pragma once
//
//
// THIS FILE CONTAINS VERY FEW ROUTINES FOR:
// - using a Random Number Generator, we use the most basic one for extreme simplicity
// - random initialization of dense vectors.
// - initializing a sparse matrix corresponding to 9-points stencil 2D-Laplacian matrix with few sparse formats (csr, coo and ell).
// - some utilities (get rocsparse_indextype/_datatype from standard types)
// No more.
//
//
#include <rocsparse/rocsparse.h>
#include <stdlib.h>
#include <vector>
template <typename I>
inline rocsparse_indextype utils_indextype(void);
template <typename T>
inline rocsparse_datatype utils_datatype(void);
//
// Not intended for integral types (instances are only numeric in this file).
//
template <typename T>
inline T utils_random(T a = static_cast<T>(0), T b = static_cast<T>(1));
//
// @brief Convert csr indexing to coordinates indexing.
// @param M number of rows of the csr indexing.
// @param nnz number of non-zeros of the csr indexing.
// @param csr_row_ptr indices to the beginning of each row of the csr indexing.
// @param coo_row_ind indices to the beginning of each row of the csr indexing.
// @param base base index.
//
template <typename I, typename J>
inline void utils_csr_to_coo(J M,
I nnz,
const std::vector<I>& csr_row_ptr,
std::vector<J>& coo_row_ind,
rocsparse_index_base base);
template <typename I, typename J, typename T>
inline void utils_csr_to_ell(J M,
const std::vector<I>& csr_row_ptr,
const std::vector<J>& csr_col_ind,
const std::vector<T>& csr_val,
std::vector<J>& ell_col_ind,
std::vector<T>& ell_val,
J& ell_width,
rocsparse_index_base csr_base,
rocsparse_index_base ell_base);
template <typename T>
inline void utils_init(T* A,
size_t M,
size_t N,
size_t lda,
size_t stride = 0,
size_t batch_count = 1,
T a = static_cast<T>(0),
T b = static_cast<T>(1));
template <typename T>
inline void utils_init(std::vector<T>& A,
size_t M,
size_t N,
size_t lda,
size_t stride = 0,
size_t batch_count = 1,
T a = static_cast<T>(0),
T b = static_cast<T>(1));
template <typename I, typename J, typename T>
inline void utils_init_csr_laplace2d(std::vector<I>& row_ptr,
std::vector<J>& col_ind,
std::vector<T>& val,
int32_t dim_x,
int32_t dim_y,
J& M,
J& N,
I& nnz,
rocsparse_index_base base);
template <typename I, typename T>
inline void utils_init_ell_laplace2d(std::vector<I>& col_ind,
std::vector<T>& val,
int32_t dim_x,
int32_t dim_y,
I& M,
I& N,
I& width,
rocsparse_index_base base);
template <typename I, typename T>
inline void utils_init_coo_laplace2d(std::vector<I>& row_ind,
std::vector<I>& col_ind,
std::vector<T>& val,
int32_t dim_x,
int32_t dim_y,
I& M,
I& N,
I& nnz,
rocsparse_index_base base);
template <>
inline double utils_random<double>(double a, double b)
{
const double t = static_cast<double>(rand()) / static_cast<double>(RAND_MAX);
return a * (static_cast<double>(1) - t) + b * t;
}
template <>
inline float utils_random<float>(float a, float b)
{
const float t = static_cast<float>(rand()) / static_cast<float>(RAND_MAX);
return a * (static_cast<float>(1) - t) + b * t;
}
template <>
inline rocsparse_float_complex utils_random<rocsparse_float_complex>(rocsparse_float_complex a,
rocsparse_float_complex b)
{
float theta = utils_random<float>(0.0f, 2.0f * acos(-1.0f));
float r = utils_random<float>(std::abs(a), std::abs(b));
return rocsparse_float_complex(r * cos(theta), r * sin(theta));
}
template <>
inline rocsparse_double_complex utils_random<rocsparse_double_complex>(rocsparse_double_complex a,
rocsparse_double_complex b)
{
double theta = utils_random<double>(0.0, 2.0 * acos(-1.0));
double r = utils_random<double>(std::abs(a), std::abs(b));
return rocsparse_double_complex(r * cos(theta), r * sin(theta));
}
inline void utils_seedrand()
{
srand(0);
}
template <>
inline rocsparse_indextype utils_indextype<uint16_t>(void)
{
return rocsparse_indextype_u16;
}
template <>
inline rocsparse_indextype utils_indextype<int32_t>(void)
{
return rocsparse_indextype_i32;
}
template <>
inline rocsparse_indextype utils_indextype<int64_t>(void)
{
return rocsparse_indextype_i64;
}
template <>
inline rocsparse_datatype utils_datatype<float>(void)
{
return rocsparse_datatype_f32_r;
}
template <>
inline rocsparse_datatype utils_datatype<double>(void)
{
return rocsparse_datatype_f64_r;
}
template <>
inline rocsparse_datatype utils_datatype<rocsparse_float_complex>(void)
{
return rocsparse_datatype_f32_c;
}
template <>
inline rocsparse_datatype utils_datatype<rocsparse_double_complex>(void)
{
return rocsparse_datatype_f64_c;
}
inline double utils_time_us(void)
{
auto now = std::chrono::steady_clock::now();
auto duration
= std::chrono::duration_cast<std::chrono::microseconds>(now.time_since_epoch()).count();
return (static_cast<double>(duration));
};
template <typename T>
inline void
utils_init(T* A, size_t M, size_t N, size_t lda, size_t stride, size_t batch_count, T a, T b)
{
for(size_t i_batch = 0; i_batch < batch_count; i_batch++)
for(size_t j = 0; j < N; ++j)
for(size_t i = 0; i < M; ++i)
{
A[i + j * lda + i_batch * stride] = utils_random<T>(a, b);
}
}
template <typename T>
inline void utils_init(
std::vector<T>& A, size_t M, size_t N, size_t lda, size_t stride, size_t batch_count, T a, T b)
{
utils_init(A.data(), M, N, lda, stride, batch_count, a, b);
}
template <typename I, typename J, typename T>
inline void utils_init_csr_laplace2d(std::vector<I>& row_ptr,
std::vector<J>& col_ind,
std::vector<T>& val,
int32_t dim_x,
int32_t dim_y,
J& M,
J& N,
I& nnz,
rocsparse_index_base base)
{
// Do nothing
if(dim_x == 0 || dim_y == 0)
{
return;
}
M = dim_x * dim_y;
N = dim_x * dim_y;
// Approximate 9pt stencil
I nnz_mat = 9 * M;
row_ptr.resize(M + 1);
col_ind.resize(nnz_mat);
val.resize(nnz_mat);
nnz = base;
row_ptr[0] = base;
// Fill local arrays
#ifdef _OPENMP
#pragma omp parallel for schedule(dynamic, 1024)
#endif
for(int32_t iy = 0; iy < dim_y; ++iy)
{
for(int32_t ix = 0; ix < dim_x; ++ix)
{
J row = iy * dim_x + ix;
for(int32_t sy = -1; sy <= 1; ++sy)
{
if(iy + sy > -1 && iy + sy < dim_y)
{
for(int32_t sx = -1; sx <= 1; ++sx)
{
if(ix + sx > -1 && ix + sx < dim_x)
{
J col = row + sy * dim_x + sx;
col_ind[nnz - base] = col + base;
val[nnz - base] = (col == row) ? 8.0 : -1.0;
++nnz;
}
}
}
}
row_ptr[row + 1] = nnz;
}
}
// Adjust nnz by index base
nnz -= base;
}
template <typename I, typename T>
inline void utils_init_ell_laplace2d(std::vector<I>& col_ind,
std::vector<T>& val,
int32_t dim_x,
int32_t dim_y,
I& M,
I& N,
I& width,
rocsparse_index_base base)
{
I csr_nnz;
std::vector<I> csr_row_ptr;
std::vector<I> csr_col_ind;
std::vector<T> csr_val;
// Sample CSR matrix
utils_init_csr_laplace2d(csr_row_ptr, csr_col_ind, csr_val, dim_x, dim_y, M, N, csr_nnz, base);
// Convert to ELL
utils_csr_to_ell(M, csr_row_ptr, csr_col_ind, csr_val, col_ind, val, width, base, base);
}
template <typename I, typename T>
inline void utils_init_coo_laplace2d(std::vector<I>& row_ind,
std::vector<I>& col_ind,
std::vector<T>& val,
int32_t dim_x,
int32_t dim_y,
I& M,
I& N,
I& nnz,
rocsparse_index_base base)
{
std::vector<I> row_ptr;
// Sample CSR matrix
utils_init_csr_laplace2d(row_ptr, col_ind, val, dim_x, dim_y, M, N, nnz, base);
// Convert to COO
utils_csr_to_coo(M, nnz, row_ptr, row_ind, base);
}
template <typename I, typename J>
inline void utils_csr_to_coo(J M,
I nnz,
const std::vector<I>& csr_row_ptr,
std::vector<J>& coo_row_ind,
rocsparse_index_base base)
{
// Resize coo_row_ind
coo_row_ind.resize(nnz);
#ifdef _OPENMP
#pragma omp parallel for schedule(dynamic, 1024)
#endif
for(J i = 0; i < M; ++i)
{
I row_begin = csr_row_ptr[i] - base;
I row_end = csr_row_ptr[i + 1] - base;
for(I j = row_begin; j < row_end; ++j)
{
coo_row_ind[j] = i + base;
}
}
}
template <typename I, typename J, typename T>
inline void utils_csr_to_ell(J M,
const std::vector<I>& csr_row_ptr,
const std::vector<J>& csr_col_ind,
const std::vector<T>& csr_val,
std::vector<J>& ell_col_ind,
std::vector<T>& ell_val,
J& ell_width,
rocsparse_index_base csr_base,
rocsparse_index_base ell_base)
{
// Determine ELL width
ell_width = 0;
for(J i = 0; i < M; ++i)
{
J row_nnz = csr_row_ptr[i + 1] - csr_row_ptr[i];
ell_width = std::max(row_nnz, ell_width);
}
// Compute ELL non-zeros
I ell_nnz = ell_width * M;
ell_col_ind.resize(ell_nnz);
ell_val.resize(ell_nnz);
#ifdef _OPENMP
#pragma omp parallel for schedule(dynamic, 1024)
#endif
for(J i = 0; i < M; ++i)
{
J p = 0;
I row_begin = csr_row_ptr[i] - csr_base;
I row_end = csr_row_ptr[i + 1] - csr_base;
J row_nnz = row_end - row_begin;
// Fill ELL matrix with data
for(I j = row_begin; j < row_end; ++j)
{
I idx = p * M + i;
ell_col_ind[idx] = csr_col_ind[j] - csr_base + ell_base;
ell_val[idx] = csr_val[j];
++p;
}
// Add padding to ELL structures
for(J j = row_nnz; j < ell_width; ++j)
{
I idx = p * M + i;
ell_col_ind[idx] = -1;
ell_val[idx] = static_cast<T>(0);
++p;
}
}
}
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