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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)
- #include "ceres/cuda_block_sparse_crs_view.h"
- #ifndef CERES_NO_CUDA
- #include "ceres/cuda_kernels_bsm_to_crs.h"
- namespace ceres::internal {
- CudaBlockSparseCRSView::CudaBlockSparseCRSView(const BlockSparseMatrix& bsm,
- ContextImpl* context)
- : context_(context) {
- block_structure_ = std::make_unique<CudaBlockSparseStructure>(
- *bsm.block_structure(), context);
- CudaBuffer<int32_t> rows(context, bsm.num_rows() + 1);
- CudaBuffer<int32_t> cols(context, bsm.num_nonzeros());
- FillCRSStructure(block_structure_->num_row_blocks(),
- bsm.num_rows(),
- block_structure_->first_cell_in_row_block(),
- block_structure_->cells(),
- block_structure_->row_blocks(),
- block_structure_->col_blocks(),
- rows.data(),
- cols.data(),
- context->DefaultStream(),
- context->is_cuda_memory_pools_supported_);
- is_crs_compatible_ = block_structure_->IsCrsCompatible();
- // if matrix is crs-compatible - we can drop block-structure and don't need
- // streamed_buffer_
- if (is_crs_compatible_) {
- VLOG(3) << "Block-sparse matrix is compatible with CRS, discarding "
- "block-structure";
- block_structure_ = nullptr;
- } else {
- streamed_buffer_ = std::make_unique<CudaStreamedBuffer<double>>(
- context_, kMaxTemporaryArraySize);
- }
- crs_matrix_ = std::make_unique<CudaSparseMatrix>(
- bsm.num_cols(), std::move(rows), std::move(cols), context);
- UpdateValues(bsm);
- }
- void CudaBlockSparseCRSView::UpdateValues(const BlockSparseMatrix& bsm) {
- if (is_crs_compatible_) {
- // Values of CRS-compatible matrices can be copied as-is
- CHECK_EQ(cudaSuccess,
- cudaMemcpyAsync(crs_matrix_->mutable_values(),
- bsm.values(),
- bsm.num_nonzeros() * sizeof(double),
- cudaMemcpyHostToDevice,
- context_->DefaultStream()));
- return;
- }
- streamed_buffer_->CopyToGpu(
- bsm.values(),
- bsm.num_nonzeros(),
- [bs = block_structure_.get(), crs = crs_matrix_.get()](
- const double* values, int num_values, int offset, auto stream) {
- PermuteToCRS(offset,
- num_values,
- bs->num_row_blocks(),
- bs->first_cell_in_row_block(),
- bs->cells(),
- bs->row_blocks(),
- bs->col_blocks(),
- crs->rows(),
- values,
- crs->mutable_values(),
- stream);
- });
- }
- } // namespace ceres::internal
- #endif // CERES_NO_CUDA
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