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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Authors: dmitriy.korchemkin@gmail.com (Dmitriy Korchemkin)
//
#ifndef CERES_INTERNAL_CUDA_PARTITIONED_BLOCK_SPARSE_CRS_VIEW_H_
#define CERES_INTERNAL_CUDA_PARTITIONED_BLOCK_SPARSE_CRS_VIEW_H_
#include "ceres/internal/config.h"
#ifndef CERES_NO_CUDA
#include <memory>
#include "ceres/block_sparse_matrix.h"
#include "ceres/cuda_block_structure.h"
#include "ceres/cuda_buffer.h"
#include "ceres/cuda_sparse_matrix.h"
#include "ceres/cuda_streamed_buffer.h"
namespace ceres::internal {
// We use cuSPARSE library for SpMV operations. However, it does not support
// neither block-sparse format with varying size of the blocks nor
// submatrix-vector products. Thus, we perform the following operations in order
// to compute products of partitioned block-sparse matrices and dense vectors on
// gpu:
// - Once per block-sparse structure update:
// - Compute CRS structures of left and right submatrices from block-sparse
// structure
// - Check if values of F sub-matrix can be copied without permutation
// matrices
// - Once per block-sparse values update:
// - Copy values of E sub-matrix
// - Permute or copy values of F sub-matrix
//
// It is assumed that cells of block-sparse matrix are laid out sequentially in
// both of sub-matrices and there is exactly one cell in row-block of E
// sub-matrix in the first num_row_blocks_e_ row blocks, and no cells in E
// sub-matrix below num_row_blocks_e_ row blocks.
//
// This class avoids storing both CRS and block-sparse values in GPU memory.
// Instead, block-sparse values are transferred to gpu memory as a disjoint set
// of small continuous segments with simultaneous permutation of the values into
// correct order using block-structure.
class CERES_NO_EXPORT CudaPartitionedBlockSparseCRSView {
public:
// Initializes internal CRS matrix and block-sparse structure on GPU side
// values. The following objects are stored in gpu memory for the whole
// lifetime of the object
// - matrix_e_: left CRS submatrix
// - matrix_f_: right CRS submatrix
// - block_structure_: copy of block-sparse structure on GPU
// - streamed_buffer_: helper for value updating
CudaPartitionedBlockSparseCRSView(const BlockSparseMatrix& bsm,
const int num_col_blocks_e,
ContextImpl* context);
// Update values of CRS submatrices using values of block-sparse matrix.
// Assumes that bsm has the same block-sparse structure as matrix that was
// used for construction.
void UpdateValues(const BlockSparseMatrix& bsm);
const CudaSparseMatrix* matrix_e() const { return matrix_e_.get(); }
const CudaSparseMatrix* matrix_f() const { return matrix_f_.get(); }
CudaSparseMatrix* mutable_matrix_e() { return matrix_e_.get(); }
CudaSparseMatrix* mutable_matrix_f() { return matrix_f_.get(); }
private:
// Value permutation kernel performs a single element-wise operation per
// thread, thus performing permutation in blocks of 8 megabytes of
// block-sparse values seems reasonable
static constexpr int kMaxTemporaryArraySize = 1 * 1024 * 1024;
std::unique_ptr<CudaSparseMatrix> matrix_e_;
std::unique_ptr<CudaSparseMatrix> matrix_f_;
std::unique_ptr<CudaStreamedBuffer<double>> streamed_buffer_;
std::unique_ptr<CudaBlockSparseStructure> block_structure_;
bool f_is_crs_compatible_;
int num_row_blocks_e_;
ContextImpl* context_;
};
} // namespace ceres::internal
#endif // CERES_NO_CUDA
#endif // CERES_INTERNAL_CUDA_PARTITIONED_BLOCK_SPARSE_CRS_VIEW_H_