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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
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// 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 "absl/log/check.h"
#include "absl/log/log.h"
#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