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ceres-solver/internal/ceres/cuda_block_sparse_crs_view.cc
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Dmitriy Korchemkin bdee4d6172 Block-sparse to CRS conversion using block-structure
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
2023-05-26 01:12:47 +03:00

103 lines
4.4 KiB
C++

// 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);
// Only block-sparse matrices with sequential layout of cells are supported
CHECK(block_structure_->sequential_layout());
crs_matrix_ = std::make_unique<CudaSparseMatrix>(
bsm.num_rows(), bsm.num_cols(), bsm.num_nonzeros(), context);
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(),
crs_matrix_->mutable_rows(),
crs_matrix_->mutable_cols(),
context->DefaultStream());
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);
}
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