mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 00:50:37 +08:00
Dmitriy Korchemkin
b1fe603305
Parallel implementations for right-multiply by dense vector for: - Partitioned matrix view - Block-sparse matrix - CRS matrix (non-symmetric only) When coupled with non-interleaving indexes in parallel for, this simple aproach provides a reasonable speedup. For example, in CRS case difference with GPGPU approach reduces closer to memory throughput ratio for high enough core count. ./bin/spmv_benchmark ------------------------------------------------------------------- Benchmark Time ------------------------------------------------------------------- BM_BlockSparseRightMultiplyAndAccumulateBA/1 28.5 ms BM_BlockSparseRightMultiplyAndAccumulateBA/2 15.7 ms BM_BlockSparseRightMultiplyAndAccumulateBA/4 9.01 ms BM_BlockSparseRightMultiplyAndAccumulateBA/8 5.60 ms BM_BlockSparseRightMultiplyAndAccumulateBA/16 3.86 ms BM_BlockSparseRightMultiplyAndAccumulateBA/28 3.84 ms BM_BlockSparseRightMultiplyAndAccumulateUnstructured/1 23.8 ms BM_BlockSparseRightMultiplyAndAccumulateUnstructured/2 15.0 ms BM_BlockSparseRightMultiplyAndAccumulateUnstructured/4 8.01 ms BM_BlockSparseRightMultiplyAndAccumulateUnstructured/8 4.02 ms BM_BlockSparseRightMultiplyAndAccumulateUnstructured/16 2.39 ms BM_BlockSparseRightMultiplyAndAccumulateUnstructured/28 1.68 ms BM_BlockSparseLeftMultiplyAndAccumulateBA 30.7 ms BM_BlockSparseLeftMultiplyAndAccumulateUnstructured 41.5 ms BM_CRSRightMultiplyAndAccumulateBA/1 24.1 ms BM_CRSRightMultiplyAndAccumulateBA/2 13.6 ms BM_CRSRightMultiplyAndAccumulateBA/4 8.70 ms BM_CRSRightMultiplyAndAccumulateBA/8 5.34 ms BM_CRSRightMultiplyAndAccumulateBA/16 3.99 ms BM_CRSRightMultiplyAndAccumulateBA/28 4.00 ms BM_CRSRightMultiplyAndAccumulateUnstructured/1 21.1 ms BM_CRSRightMultiplyAndAccumulateUnstructured/2 10.83 ms BM_CRSRightMultiplyAndAccumulateUnstructured/4 5.88 ms BM_CRSRightMultiplyAndAccumulateUnstructured/8 3.68 ms BM_CRSRightMultiplyAndAccumulateUnstructured/16 2.21 ms BM_CRSRightMultiplyAndAccumulateUnstructured/28 1.71 ms BM_CRSLeftMultiplyAndAccumulateBA 23.6 ms BM_CRSLeftMultiplyAndAccumulateUnstructured 22.5 ms BM_CudaRightMultiplyAndAccumulateBA 0.679 ms BM_CudaRightMultiplyAndAccumulateUnstructured 0.480 ms BM_CudaLeftMultiplyAndAccumulateBA 0.774 ms BM_CudaLeftMultiplyAndAccumulateUnstructured 0.361 ms ./bin/partitioned_matrix_view_benchmark ----------------------------------------------------------------- Benchmark Time ----------------------------------------------------------------- BM_PatitionedViewRightMultiplyAndAccumulateE_Static/1 18.5 ms BM_PatitionedViewRightMultiplyAndAccumulateE_Static/2 10.7 ms BM_PatitionedViewRightMultiplyAndAccumulateE_Static/4 6.34 ms BM_PatitionedViewRightMultiplyAndAccumulateE_Static/8 4.26 ms BM_PatitionedViewRightMultiplyAndAccumulateE_Static/16 3.86 ms BM_PatitionedViewRightMultiplyAndAccumulateE_Static/28 3.75 ms BM_PatitionedViewRightMultiplyAndAccumulateF_Static/1 18.8 ms BM_PatitionedViewRightMultiplyAndAccumulateF_Static/2 11.9 ms BM_PatitionedViewRightMultiplyAndAccumulateF_Static/4 6.94 ms BM_PatitionedViewRightMultiplyAndAccumulateF_Static/8 4.41 ms BM_PatitionedViewRightMultiplyAndAccumulateF_Static/16 3.63 ms BM_PatitionedViewRightMultiplyAndAccumulateF_Static/28 3.86 ms Timings correspond to intel 8176 cpu and 2080ti nvidia gpu, with OpenMP threading backend. Change-Id: Idc07d0563103d057ca3c8412de81a7823fe232af
79 lines
3.5 KiB
C++
79 lines
3.5 KiB
C++
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// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 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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// Author: sameeragarwal@google.com (Sameer Agarwal)
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#ifndef CERES_INTERNAL_FAKE_BUNDLE_ADJUSTMENT_JACOBIAN
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#define CERES_INTERNAL_FAKE_BUNDLE_ADJUSTMENT_JACOBIAN
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#include <memory>
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#include <random>
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#include "ceres/block_sparse_matrix.h"
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#include "ceres/partitioned_matrix_view.h"
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namespace ceres::internal {
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std::unique_ptr<BlockSparseMatrix> CreateFakeBundleAdjustmentJacobian(
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int num_cameras,
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int num_points,
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int camera_size,
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int point_size,
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double visibility,
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std::mt19937& prng);
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template <int kEBlockSize = 3, int kFBlockSize = 6>
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std::pair<std::unique_ptr<PartitionedMatrixView<2, kEBlockSize, kFBlockSize>>,
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std::unique_ptr<BlockSparseMatrix>>
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CreateFakeBundleAdjustmentPartitionedJacobian(int num_cameras,
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int num_points,
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double visibility,
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std::mt19937& rng) {
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using PartitionedView = PartitionedMatrixView<2, kEBlockSize, kFBlockSize>;
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auto block_sparse_matrix = CreateFakeBundleAdjustmentJacobian(
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num_cameras, num_points, kFBlockSize, kEBlockSize, visibility, rng);
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auto partitioned_view =
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std::make_unique<PartitionedView>(*block_sparse_matrix, num_points);
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return std::make_pair(std::move(partitioned_view),
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std::move(block_sparse_matrix));
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}
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std::pair<
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std::unique_ptr<PartitionedMatrixView<2, Eigen::Dynamic, Eigen::Dynamic>>,
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std::unique_ptr<BlockSparseMatrix>>
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CreateFakeBundleAdjustmentPartitionedJacobian(int num_cameras,
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int num_points,
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int camera_size,
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int landmark_size,
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double visibility,
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std::mt19937& rng);
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} // namespace ceres::internal
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#endif // CERES_INTERNAL_FAKE_BUNDLE_ADJUSTMENT_JACOBIAN
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