mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 08:34:37 +08:00
Dmitriy Korchemkin
5e4b22f7fc
- Perform temporary buffer size estimation only once - Allow construction from existing buffers with col/row structure Change-Id: I73c291328f1e8ed9184aba5d7058df71cbc6a15d
109 lines
4.7 KiB
C++
109 lines
4.7 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2023 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Authors: dmitriy.korchemkin@gmail.com (Dmitriy Korchemkin)
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//
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#ifndef CERES_INTERNAL_CUDA_BLOCK_SPARSE_CRS_VIEW_H_
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#define CERES_INTERNAL_CUDA_BLOCK_SPARSE_CRS_VIEW_H_
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#include "ceres/internal/config.h"
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#ifndef CERES_NO_CUDA
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#include <memory>
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#include "ceres/block_sparse_matrix.h"
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#include "ceres/cuda_block_structure.h"
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#include "ceres/cuda_buffer.h"
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#include "ceres/cuda_sparse_matrix.h"
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#include "ceres/cuda_streamed_buffer.h"
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namespace ceres::internal {
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// We use cuSPARSE library for SpMV operations. However, it does not support
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// block-sparse format with varying size of the blocks. Thus, we perform the
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// following operations in order to compute products of block-sparse matrices
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// and dense vectors on gpu:
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// - Once per block-sparse structure update:
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// - Compute CRS structure from block-sparse structure and check if values of
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// block-sparse matrix would have the same order as values of CRS matrix
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// - Once per block-sparse values update:
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// - Update values in CRS matrix with values of block-sparse matrix
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//
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// Only block-sparse matrices with sequential order of cells are supported.
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//
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// UpdateValues method updates values:
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// - In a single host-to-device copy for matrices with CRS-compatible value
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// layout
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// - Simultaneously transferring and permuting values using CudaStreamedBuffer
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// otherwise
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class CERES_NO_EXPORT CudaBlockSparseCRSView {
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public:
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// Initializes internal CRS matrix using structure and values of block-sparse
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// matrix For block-sparse matrices that have value layout different from CRS
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// block-sparse structure will be stored/
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CudaBlockSparseCRSView(const BlockSparseMatrix& bsm, ContextImpl* context);
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const CudaSparseMatrix* crs_matrix() const { return crs_matrix_.get(); }
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CudaSparseMatrix* mutable_crs_matrix() { return crs_matrix_.get(); }
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// Update values of crs_matrix_ using values of block-sparse matrix.
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// Assumes that bsm has the same block-sparse structure as matrix that was
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// used for construction.
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void UpdateValues(const BlockSparseMatrix& bsm);
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// Returns true if block-sparse matrix had CRS-compatible value layout
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bool IsCrsCompatible() const { return is_crs_compatible_; }
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void LeftMultiplyAndAccumulate(const CudaVector& x, CudaVector* y) const {
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crs_matrix()->LeftMultiplyAndAccumulate(x, y);
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}
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void RightMultiplyAndAccumulate(const CudaVector& x, CudaVector* y) const {
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crs_matrix()->RightMultiplyAndAccumulate(x, y);
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}
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private:
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// Value permutation kernel performs a single element-wise operation per
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// thread, thus performing permutation in blocks of 8 megabytes of
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// block-sparse values seems reasonable
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static constexpr int kMaxTemporaryArraySize = 1 * 1024 * 1024;
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std::unique_ptr<CudaSparseMatrix> crs_matrix_;
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// Only created if block-sparse matrix has non-CRS value layout
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std::unique_ptr<CudaStreamedBuffer<double>> streamed_buffer_;
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// Only stored if block-sparse matrix has non-CRS value layout
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std::unique_ptr<CudaBlockSparseStructure> block_structure_;
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bool is_crs_compatible_;
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ContextImpl* context_;
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};
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} // namespace ceres::internal
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#endif // CERES_NO_CUDA
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#endif // CERES_INTERNAL_CUDA_BLOCK_SPARSE_CRS_VIEW_H_
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