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bdee4d6172
Instead of pre-computing pemutation from block-sparse to CRS order, index of value in CRS matrix is computed in the process of updating values using block-sparse structure. When it is possible to update values via a simple host-to-device copy, block-sparse structure on GPU is discarded after computing CRS structure. Computing index is significantly slower than using pre-computed permutation, but is still hidden by host-to-device transfer. On problems from BAL dataset this results into reduction of extra gpu memory consumption from 33% (permutation stored as 32-bit indices) to ~10% for storing block-sparse structure. Benchmark results: ======================= CUDA Device Properties ====================== Cuda version : 11.8 Device ID : 0 Device name : NVIDIA GeForce RTX 2080 Ti Total GPU memory : 11012 MiB GPU memory available : 10852 MiB Compute capability : 7.5 Warp size : 32 Max threads per block: 1024 Max threads per dim : 1024 1024 64 Max grid size : 2147483647 65535 65535 Multiprocessor count : 68 ==================================================================== Running ./bin/evaluation_benchmark Run on (112 X 3200 MHz CPU s) CPU Caches: L1 Data 32 KiB (x56) L1 Instruction 32 KiB (x56) L2 Unified 1024 KiB (x56) L3 Unified 39424 KiB (x2) Load Average: 24.58, 11.75, 8.52 ----------------------------------------------------------------------- Benchmark Time ----------------------------------------------------------------------- Using on-the-fly computation of CRS index corresponding to block-sparse index: JacobianToCRS<g/final/problem-4585-1324582-pre.txt> 1607 ms JacobianToCRSView<g/final/problem-4585-1324582-pre.txt> 564 ms JacobianToCRSMatrix<g/final/problem-4585-1324582-pre.txt> 2226 ms JacobianToCRSViewUpdate<g/final/problem-4585-1324582-pre.txt> 228 ms JacobianToCRSMatrixUpdate<g/final/problem-4585-1324582-pre.txt> 400 ms Using precomputed permutation: JacobianToCRS</final/problem-4585-1324582-pre.txt> 1656 ms JacobianToCRSView</final/problem-4585-1324582-pre.txt> 553 ms JacobianToCRSMatrix</final/problem-4585-1324582-pre.txt> 2255 ms JacobianToCRSViewUpdate</final/problem-4585-1324582-pre.txt> 228 ms JacobianToCRSMatrixUpdate</final/problem-4585-1324582-pre.txt> 406 ms Performance of JacobianToCRSViewUpdate is still limited by host-to-device transfer, and JacobianToCRSView is faster than computing CRS structure on CPU. Change-Id: Ifb6910fb01ae6071400d36c277846fadc5857964
103 lines
4.4 KiB
C++
103 lines
4.4 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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#include "ceres/cuda_block_sparse_crs_view.h"
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#ifndef CERES_NO_CUDA
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#include "ceres/cuda_kernels_bsm_to_crs.h"
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namespace ceres::internal {
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CudaBlockSparseCRSView::CudaBlockSparseCRSView(const BlockSparseMatrix& bsm,
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ContextImpl* context)
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: context_(context) {
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block_structure_ = std::make_unique<CudaBlockSparseStructure>(
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*bsm.block_structure(), context);
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// Only block-sparse matrices with sequential layout of cells are supported
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CHECK(block_structure_->sequential_layout());
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crs_matrix_ = std::make_unique<CudaSparseMatrix>(
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bsm.num_rows(), bsm.num_cols(), bsm.num_nonzeros(), context);
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FillCRSStructure(block_structure_->num_row_blocks(),
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bsm.num_rows(),
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block_structure_->first_cell_in_row_block(),
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block_structure_->cells(),
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block_structure_->row_blocks(),
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block_structure_->col_blocks(),
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crs_matrix_->mutable_rows(),
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crs_matrix_->mutable_cols(),
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context->DefaultStream());
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is_crs_compatible_ = block_structure_->IsCrsCompatible();
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// if matrix is crs-compatible - we can drop block-structure and don't need
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// streamed_buffer_
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if (is_crs_compatible_) {
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VLOG(3) << "Block-sparse matrix is compatible with CRS, discarding "
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"block-structure";
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block_structure_ = nullptr;
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} else {
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streamed_buffer_ = std::make_unique<CudaStreamedBuffer<double>>(
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context_, kMaxTemporaryArraySize);
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}
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UpdateValues(bsm);
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}
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void CudaBlockSparseCRSView::UpdateValues(const BlockSparseMatrix& bsm) {
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if (is_crs_compatible_) {
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// Values of CRS-compatible matrices can be copied as-is
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CHECK_EQ(cudaSuccess,
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cudaMemcpyAsync(crs_matrix_->mutable_values(),
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bsm.values(),
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bsm.num_nonzeros() * sizeof(double),
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cudaMemcpyHostToDevice,
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context_->DefaultStream()));
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return;
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}
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streamed_buffer_->CopyToGpu(
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bsm.values(),
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bsm.num_nonzeros(),
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[bs = block_structure_.get(), crs = crs_matrix_.get()](
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const double* values, int num_values, int offset, auto stream) {
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PermuteToCRS(offset,
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num_values,
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bs->num_row_blocks(),
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bs->first_cell_in_row_block(),
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bs->cells(),
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bs->row_blocks(),
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bs->col_blocks(),
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crs->rows(),
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values,
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crs->mutable_values(),
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stream);
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});
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}
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} // namespace ceres::internal
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#endif // CERES_NO_CUDA
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