derek.holloway
7502bf64a4
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424 lines
15 KiB
C++
424 lines
15 KiB
C++
/* ************************************************************************
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* Copyright (C) 2022-2026 Advanced Micro Devices, Inc. All rights Reserved.
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to deal
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* in the Software without restriction, including without limitation the rights
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* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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* copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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* THE SOFTWARE.
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*
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* ************************************************************************ */
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#pragma once
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//
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//
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// THIS FILE CONTAINS VERY FEW ROUTINES FOR:
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// - using a Random Number Generator, we use the most basic one for extreme simplicity
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// - random initialization of dense vectors.
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// - initializing a sparse matrix corresponding to 9-points stencil 2D-Laplacian matrix with few sparse formats (csr, coo and ell).
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// - some utilities (get rocsparse_indextype/_datatype from standard types)
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// No more.
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//
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//
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#include "rocsparse/rocsparse.h"
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#include <chrono>
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#include <stdlib.h>
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#include <vector>
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template <typename I>
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inline rocsparse_indextype utils_indextype(void);
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template <typename T>
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inline rocsparse_datatype utils_datatype(void);
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//
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// Not intended for integral types (instances are only numeric in this file).
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//
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template <typename T>
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inline T utils_random(T a = static_cast<T>(0), T b = static_cast<T>(1));
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//
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// @brief Convert csr indexing to coordinates indexing.
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// @param M number of rows of the csr indexing.
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// @param nnz number of non-zeros of the csr indexing.
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// @param csr_row_ptr indices to the beginning of each row of the csr indexing.
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// @param coo_row_ind indices to the beginning of each row of the csr indexing.
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// @param base base index.
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//
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template <typename I, typename J>
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inline void utils_csr_to_coo(J M,
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I nnz,
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const std::vector<I>& csr_row_ptr,
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std::vector<J>& coo_row_ind,
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rocsparse_index_base base);
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template <typename I, typename J, typename T>
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inline void utils_csr_to_ell(J M,
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const std::vector<I>& csr_row_ptr,
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const std::vector<J>& csr_col_ind,
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const std::vector<T>& csr_val,
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std::vector<J>& ell_col_ind,
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std::vector<T>& ell_val,
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J& ell_width,
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rocsparse_index_base csr_base,
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rocsparse_index_base ell_base);
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template <typename T>
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inline void utils_init(T* A,
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size_t M,
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size_t N,
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size_t lda,
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size_t stride = 0,
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size_t batch_count = 1,
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T a = static_cast<T>(0),
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T b = static_cast<T>(1));
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template <typename T>
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inline void utils_init(std::vector<T>& A,
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size_t M,
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size_t N,
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size_t lda,
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size_t stride = 0,
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size_t batch_count = 1,
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T a = static_cast<T>(0),
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T b = static_cast<T>(1));
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template <typename I, typename J, typename T>
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inline void utils_init_csr_laplace2d(std::vector<I>& row_ptr,
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std::vector<J>& col_ind,
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std::vector<T>& val,
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int32_t dim_x,
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int32_t dim_y,
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J& M,
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J& N,
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I& nnz,
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rocsparse_index_base base);
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template <typename I, typename T>
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inline void utils_init_ell_laplace2d(std::vector<I>& col_ind,
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std::vector<T>& val,
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int32_t dim_x,
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int32_t dim_y,
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I& M,
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I& N,
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I& width,
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rocsparse_index_base base);
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template <typename I, typename T>
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inline void utils_init_coo_laplace2d(std::vector<I>& row_ind,
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std::vector<I>& col_ind,
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std::vector<T>& val,
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int32_t dim_x,
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int32_t dim_y,
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I& M,
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I& N,
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I& nnz,
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rocsparse_index_base base);
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template <>
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inline double utils_random<double>(double a, double b)
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{
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const double t = static_cast<double>(rand()) / static_cast<double>(RAND_MAX);
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return a * (static_cast<double>(1) - t) + b * t;
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}
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template <>
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inline float utils_random<float>(float a, float b)
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{
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const float t = static_cast<float>(rand()) / static_cast<float>(RAND_MAX);
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return a * (static_cast<float>(1) - t) + b * t;
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}
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template <>
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inline rocsparse_float_complex utils_random<rocsparse_float_complex>(rocsparse_float_complex a,
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rocsparse_float_complex b)
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{
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float theta = utils_random<float>(0.0f, 2.0f * acos(-1.0f));
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float r = utils_random<float>(std::abs(a), std::abs(b));
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return rocsparse_float_complex(r * cos(theta), r * sin(theta));
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}
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template <>
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inline rocsparse_double_complex utils_random<rocsparse_double_complex>(rocsparse_double_complex a,
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rocsparse_double_complex b)
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{
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double theta = utils_random<double>(0.0, 2.0 * acos(-1.0));
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double r = utils_random<double>(std::abs(a), std::abs(b));
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return rocsparse_double_complex(r * cos(theta), r * sin(theta));
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}
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inline void utils_seedrand()
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{
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srand(0);
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}
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template <>
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inline rocsparse_indextype utils_indextype<int32_t>(void)
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{
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return rocsparse_indextype_i32;
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}
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template <>
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inline rocsparse_indextype utils_indextype<int64_t>(void)
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{
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return rocsparse_indextype_i64;
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}
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template <>
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inline rocsparse_datatype utils_datatype<float>(void)
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{
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return rocsparse_datatype_f32_r;
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}
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template <>
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inline rocsparse_datatype utils_datatype<double>(void)
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{
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return rocsparse_datatype_f64_r;
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}
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template <>
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inline rocsparse_datatype utils_datatype<rocsparse_float_complex>(void)
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{
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return rocsparse_datatype_f32_c;
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}
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template <>
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inline rocsparse_datatype utils_datatype<rocsparse_double_complex>(void)
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{
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return rocsparse_datatype_f64_c;
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}
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inline double utils_time_us(void)
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{
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auto now = std::chrono::steady_clock::now();
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auto duration
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= std::chrono::duration_cast<std::chrono::microseconds>(now.time_since_epoch()).count();
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return (static_cast<double>(duration));
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};
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template <typename T>
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inline void
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utils_init(T* A, size_t M, size_t N, size_t lda, size_t stride, size_t batch_count, T a, T b)
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{
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for(size_t i_batch = 0; i_batch < batch_count; i_batch++)
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for(size_t j = 0; j < N; ++j)
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for(size_t i = 0; i < M; ++i)
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{
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A[i + j * lda + i_batch * stride] = utils_random<T>(a, b);
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}
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}
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template <typename T>
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inline void utils_init(
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std::vector<T>& A, size_t M, size_t N, size_t lda, size_t stride, size_t batch_count, T a, T b)
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{
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utils_init(A.data(), M, N, lda, stride, batch_count, a, b);
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}
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template <typename I, typename J, typename T>
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inline void utils_init_csr_laplace2d(std::vector<I>& row_ptr,
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std::vector<J>& col_ind,
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std::vector<T>& val,
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int32_t dim_x,
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int32_t dim_y,
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J& M,
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J& N,
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I& nnz,
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rocsparse_index_base base)
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{
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// Do nothing
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if(dim_x == 0 || dim_y == 0)
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{
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return;
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}
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M = dim_x * dim_y;
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N = dim_x * dim_y;
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// Approximate 9pt stencil
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I nnz_mat = 9 * M;
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row_ptr.resize(M + 1);
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col_ind.resize(nnz_mat);
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val.resize(nnz_mat);
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nnz = base;
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row_ptr[0] = base;
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// Fill local arrays
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#ifdef _OPENMP
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#pragma omp parallel for schedule(dynamic, 1024)
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#endif
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for(int32_t iy = 0; iy < dim_y; ++iy)
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{
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for(int32_t ix = 0; ix < dim_x; ++ix)
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{
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J row = iy * dim_x + ix;
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for(int32_t sy = -1; sy <= 1; ++sy)
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{
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if(iy + sy > -1 && iy + sy < dim_y)
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{
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for(int32_t sx = -1; sx <= 1; ++sx)
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{
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if(ix + sx > -1 && ix + sx < dim_x)
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{
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J col = row + sy * dim_x + sx;
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col_ind[nnz - base] = col + base;
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val[nnz - base] = (col == row) ? 8.0 : -1.0;
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++nnz;
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}
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}
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}
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}
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row_ptr[row + 1] = nnz;
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}
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}
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// Adjust nnz by index base
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nnz -= base;
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}
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template <typename I, typename T>
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inline void utils_init_ell_laplace2d(std::vector<I>& col_ind,
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std::vector<T>& val,
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int32_t dim_x,
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int32_t dim_y,
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I& M,
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I& N,
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I& width,
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rocsparse_index_base base)
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{
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I csr_nnz;
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std::vector<I> csr_row_ptr;
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std::vector<I> csr_col_ind;
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std::vector<T> csr_val;
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// Sample CSR matrix
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utils_init_csr_laplace2d(csr_row_ptr, csr_col_ind, csr_val, dim_x, dim_y, M, N, csr_nnz, base);
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// Convert to ELL
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utils_csr_to_ell(M, csr_row_ptr, csr_col_ind, csr_val, col_ind, val, width, base, base);
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}
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template <typename I, typename T>
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inline void utils_init_coo_laplace2d(std::vector<I>& row_ind,
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std::vector<I>& col_ind,
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std::vector<T>& val,
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int32_t dim_x,
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int32_t dim_y,
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I& M,
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I& N,
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I& nnz,
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rocsparse_index_base base)
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{
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std::vector<I> row_ptr;
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// Sample CSR matrix
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utils_init_csr_laplace2d(row_ptr, col_ind, val, dim_x, dim_y, M, N, nnz, base);
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// Convert to COO
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utils_csr_to_coo(M, nnz, row_ptr, row_ind, base);
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}
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template <typename I, typename J>
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inline void utils_csr_to_coo(J M,
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I nnz,
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const std::vector<I>& csr_row_ptr,
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std::vector<J>& coo_row_ind,
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rocsparse_index_base base)
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{
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// Resize coo_row_ind
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coo_row_ind.resize(nnz);
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#ifdef _OPENMP
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#pragma omp parallel for schedule(dynamic, 1024)
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#endif
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for(J i = 0; i < M; ++i)
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{
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I row_begin = csr_row_ptr[i] - base;
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I row_end = csr_row_ptr[i + 1] - base;
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for(I j = row_begin; j < row_end; ++j)
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{
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coo_row_ind[j] = i + base;
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}
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}
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}
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template <typename I, typename J, typename T>
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inline void utils_csr_to_ell(J M,
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const std::vector<I>& csr_row_ptr,
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const std::vector<J>& csr_col_ind,
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const std::vector<T>& csr_val,
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std::vector<J>& ell_col_ind,
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std::vector<T>& ell_val,
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J& ell_width,
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rocsparse_index_base csr_base,
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rocsparse_index_base ell_base)
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{
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// Determine ELL width
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ell_width = 0;
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for(J i = 0; i < M; ++i)
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{
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J row_nnz = csr_row_ptr[i + 1] - csr_row_ptr[i];
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ell_width = std::max(row_nnz, ell_width);
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}
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// Compute ELL non-zeros
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I ell_nnz = ell_width * M;
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ell_col_ind.resize(ell_nnz);
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ell_val.resize(ell_nnz);
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#ifdef _OPENMP
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#pragma omp parallel for schedule(dynamic, 1024)
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#endif
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for(J i = 0; i < M; ++i)
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{
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J p = 0;
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I row_begin = csr_row_ptr[i] - csr_base;
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I row_end = csr_row_ptr[i + 1] - csr_base;
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J row_nnz = row_end - row_begin;
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// Fill ELL matrix with data
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for(I j = row_begin; j < row_end; ++j)
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{
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I idx = p * M + i;
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ell_col_ind[idx] = csr_col_ind[j] - csr_base + ell_base;
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ell_val[idx] = csr_val[j];
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++p;
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}
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// Add padding to ELL structures
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for(J j = row_nnz; j < ell_width; ++j)
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{
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I idx = p * M + i;
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ell_col_ind[idx] = -1;
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ell_val[idx] = static_cast<T>(0);
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++p;
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}
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}
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}
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