// 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_SPARSE_CRS_VIEW_H_ #define CERES_INTERNAL_CUDA_BLOCK_SPARSE_CRS_VIEW_H_ #include "ceres/internal/config.h" #ifndef CERES_NO_CUDA #include #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 // block-sparse format with varying size of the blocks. Thus, we perform the // following operations in order to compute products of block-sparse matrices // and dense vectors on gpu: // - Once per block-sparse structure update: // - Compute CRS structure from block-sparse structure and check if values of // block-sparse matrix would have the same order as values of CRS matrix // - Once per block-sparse values update: // - Update values in CRS matrix with values of block-sparse matrix // // Only block-sparse matrices with sequential order of cells are supported. // // UpdateValues method updates values: // - In a single host-to-device copy for matrices with CRS-compatible value // layout // - Simultaneously transferring and permuting values using CudaStreamedBuffer // otherwise class CERES_NO_EXPORT CudaBlockSparseCRSView { public: // Initializes internal CRS matrix using structure and values of block-sparse // matrix For block-sparse matrices that have value layout different from CRS // block-sparse structure will be stored/ CudaBlockSparseCRSView(const BlockSparseMatrix& bsm, ContextImpl* context); const CudaSparseMatrix* crs_matrix() const { return crs_matrix_.get(); } CudaSparseMatrix* mutable_crs_matrix() { return crs_matrix_.get(); } // Update values of crs_matrix_ 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); // Returns true if block-sparse matrix had CRS-compatible value layout bool IsCrsCompatible() const { return is_crs_compatible_; } void LeftMultiplyAndAccumulate(const CudaVector& x, CudaVector* y) const { crs_matrix()->LeftMultiplyAndAccumulate(x, y); } void RightMultiplyAndAccumulate(const CudaVector& x, CudaVector* y) const { crs_matrix()->RightMultiplyAndAccumulate(x, y); } 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 crs_matrix_; // Only created if block-sparse matrix has non-CRS value layout std::unique_ptr> streamed_buffer_; // Only stored if block-sparse matrix has non-CRS value layout std::unique_ptr block_structure_; bool is_crs_compatible_; ContextImpl* context_; }; } // namespace ceres::internal #endif // CERES_NO_CUDA #endif // CERES_INTERNAL_CUDA_BLOCK_SPARSE_CRS_VIEW_H_