blob: 6da6fdd2a6ee44927ad467e8af404a00cc6533e3 [file] [log] [blame] [edit]
// 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_BLOCK_STRUCTURE_H_
#define CERES_INTERNAL_CUDA_BLOCK_STRUCTURE_H_
#include "ceres/internal/config.h"
#ifndef CERES_NO_CUDA
#include "ceres/block_structure.h"
#include "ceres/cuda_buffer.h"
namespace ceres::internal {
class CudaBlockStructureTest;
// This class stores a read-only block-sparse structure in gpu memory.
// Invariants are the same as those of CompressedRowBlockStructure.
// In order to simplify allocation and copying data to gpu, cells from all
// row-blocks are stored in a single array sequentially. Array
// first_cell_in_row_block of size num_row_blocks + 1 allows to identify range
// of cells corresponding to a row-block. Cells corresponding to i-th row-block
// are stored in sub-array cells[first_cell_in_row_block[i]; ...
// first_cell_in_row_block[i + 1] - 1], and their order is preserved.
class CERES_NO_EXPORT CudaBlockSparseStructure {
public:
// CompressedRowBlockStructure is contains a vector of CompressedLists, with
// each CompressedList containing a vector of Cells. We precompute a flat
// array of cells on cpu and transfer it to the gpu.
CudaBlockSparseStructure(const CompressedRowBlockStructure& block_structure,
ContextImpl* context);
// In the case of partitioned matrices, number of non-zeros in E and layout of
// F are computed
CudaBlockSparseStructure(const CompressedRowBlockStructure& block_structure,
const int num_col_blocks_e,
ContextImpl* context);
int num_rows() const { return num_rows_; }
int num_cols() const { return num_cols_; }
int num_cells() const { return num_cells_; }
int num_nonzeros() const { return num_nonzeros_; }
// When partitioned matrix constructor was used, returns number of non-zeros
// in E sub-matrix
int num_nonzeros_e() const { return num_nonzeros_e_; }
int num_row_blocks() const { return num_row_blocks_; }
int num_row_blocks_e() const { return num_row_blocks_e_; }
int num_col_blocks() const { return num_col_blocks_; }
// Returns true if values from block-sparse matrix (F sub-matrix in
// partitioned case) can be copied to CRS matrix as-is. This is possible if
// each row-block is stored in CRS order:
// - Row-block consists of a single row
// - Row-block contains a single cell
bool IsCrsCompatible() const { return is_crs_compatible_; }
// Device pointer to array of num_row_blocks + 1 indices of the first cell of
// row block
const int* first_cell_in_row_block() const {
return first_cell_in_row_block_.data();
}
// Device pointer to array of num_row_blocks + 1 indices of the first value in
// this or subsequent row-blocks of submatrix F
const int* value_offset_row_block_f() const {
return value_offset_row_block_f_.data();
}
// Device pointer to array of num_cells cells, sorted by row-block
const Cell* cells() const { return cells_.data(); }
// Device pointer to array of row blocks
const Block* row_blocks() const { return row_blocks_.data(); }
// Device pointer to array of column blocks
const Block* col_blocks() const { return col_blocks_.data(); }
private:
int num_rows_;
int num_cols_;
int num_cells_;
int num_nonzeros_;
int num_nonzeros_e_;
int num_row_blocks_;
int num_row_blocks_e_;
int num_col_blocks_;
bool is_crs_compatible_;
CudaBuffer<int> first_cell_in_row_block_;
CudaBuffer<int> value_offset_row_block_f_;
CudaBuffer<Cell> cells_;
CudaBuffer<Block> row_blocks_;
CudaBuffer<Block> col_blocks_;
friend class CudaBlockStructureTest;
};
} // namespace ceres::internal
#endif // CERES_NO_CUDA
#endif // CERES_INTERNAL_CUDA_BLOCK_SPARSE_STRUCTURE_H_