Use the more modern form of accessing the data array of a vector
rather than grabbing the pointer to the first element. The latter
can lead to errors if the vector is of zero length.
Change-Id: Ifc8fc969b06b3ba1a9385e8a3a8d5c50b25db5a8
* Added GPU device and CUDA compute capability identification.
* Added GpuMemoryAvailable() to aid downstream optimizations based
on GPU memory availability.
Change-Id: I326dc1e4b7a6a7f5571b7e5479eb9aa300ad1075
With -flto=auto, GCC emits multiple warnings in release builds such as
In function ‘make_unique’,
inlined from ‘Create’ at ceres-solver/internal/ceres/scratch_evaluate_preparer.cc:43:75,
inlined from ‘CreateEvaluatePreparers’ at ceres-solver/internal/ceres/compressed_row_jacobian_writer.h:95:66,
inlined from ‘__ct ’ at ceres-solver/internal/ceres/program_evaluator.h:120:9,
inlined from ‘Evaluate.constprop.isra’ at ceres-solver/internal/ceres/problem_impl.cc:695:65:
/usr/include/c++/12.1.1/bits/unique_ptr.h:1080:30: warning: argument 1 value ‘18446744073709551615’ exceeds maximum object size 9223372036854775807 [-Walloc-size-larger-than=]
1080 | { return unique_ptr<_Tp>(new remove_extent_t<_Tp>[__num]()); }
| ^
because a signed integer is used to specify the size of allocated arrays
instead of the expected unsigned (specifically, std::size_t) without
checking for negative values at the call site.
Change-Id: I923b1d074241535426bfea041568ef1dc7f3ec86
1. Rename BlockJacobiPreconditionet to BlockSparseJacobiPreconditioner.
2. Add CompressedRowSparseJacobiPreconditioner which is a
block Jacobi preconditioner for CompressedRowSparseMatrix objects.
3. Re-write the tests to be more comprehensive.
Change-Id: Icbc91f9ad2cefaad593c11397f8cdcf805d7e118
* Added CudaCgnrSolver, a new CUDA-accelerated CGNR.
* To use CudaCgnrSolver, the user must select CGNR as the linear_solver
and CUDA_SPARSE as the sparse_linear_algebra_library.
* Updated ConjugateGradientSolver to work with an array of pointers to
scratch to support CudaVectors as scratch.
* Moved CUDA initialization to run in Solver::Solve as needed.
Some performance comparisons on an Ubuntu 20.04 desktop with an
Intel i9-9940X CPU @ 3.30GHz, and an nVidia Quadro RTX 6000,
all configurations run with 24 threads, and 10 iterations.
=================================================
CGNR + CUDA_SPARSE + IDENTITY Preconditioner
problem-1778-993923-pre.txt
=================================================
Cost:
Initial 2.563973e+08
Final 1.724755e+06
Change 2.546725e+08
Minimizer iterations 11
Successful steps 7
Unsuccessful steps 4
Time (in seconds):
Preprocessor 4.020158
Residual only evaluation 1.567092 (10)
Jacobian & residual evaluation 7.847130 (7)
Linear solver 31.688898 (10)
Minimizer 46.834987
Postprocessor 0.353974
Total 51.209120
=================================================
SPARSE_SCHUR (CPU) + SUITE_SPARSE + AMD
problem-1778-993923-pre.txt
=================================================
Cost:
Initial 2.563973e+08
Final 1.651617e+06
Change 2.547457e+08
Minimizer iterations 11
Successful steps 11
Unsuccessful steps 0
Time (in seconds):
Preprocessor 35.812003
Residual only evaluation 1.658980 (10)
Jacobian & residual evaluation 12.218799 (11)
Linear solver 76.409992 (10)
Minimizer 98.809773
Postprocessor 0.372712
Total 134.994489
=================================================
ITERATIVE_SCHUR (CPU) + JACOBI Preconditioner
problem-1778-993923-pre.txt
=================================================
Cost:
Initial 2.563973e+08
Final 1.684447e+06
Change 2.547128e+08
Minimizer iterations 11
Successful steps 8
Unsuccessful steps 3
Time (in seconds):
Preprocessor 15.331614
Residual only evaluation 1.606114 (10)
Jacobian & residual evaluation 8.502166 (8)
Linear solver 351.910080 (10)
Minimizer 368.797327
Postprocessor 0.363536
Total 384.492478
=================================================
CGNR + CUDA_SPARSE + IDENTITY Preconditioner
problem-13682-4456117-pre.txt
=================================================
Cost:
Initial 1.126372e+09
Final 2.269329e+07
Change 1.103678e+09
Minimizer iterations 11
Successful steps 7
Unsuccessful steps 4
Time (in seconds):
Preprocessor 19.140087
Residual only evaluation 8.721920 (10)
Jacobian & residual evaluation 41.955923 (7)
Linear solver 214.121861 (10)
Minimizer 296.636890
Postprocessor 1.971827
Total 317.748804
Change-Id: I3a09f31aa6903f661e91f595afd39d427583e856
* Added CudaSparseMatrix to manage and operate on sparse matrices with
cuSparse.
* Added tests for CudaSparseMatrix.
* Added a new sparse linear operator benchmark.
Change-Id: Id09df46de3b40be1f14441528088b54dab5844af
CompressedRowSparseMatrix.
Since the conversion from BlockSparseMatrix to CompressedRowSparseMatrix
is not used in any performance-critical context, this CL simplifies it
by re-using existing conversions.
Change-Id: I51263bc95cc056efb31961ccda548cd7be35b2a4
Clang emits the following warning:
internal/ceres/schur_complement_solver.cc:71:51: warning: class with destructor marked 'final' cannot be inherited from [-Wfinal-dtor-non-final-class]
virtual ~BlockRandomAccessSparseMatrixAdapter() final {}
Change-Id: I9da96711fd1e924ce8504988ff171cb476b6222f
Add an option to use schur power series expansion for initialization
of pcg solution in ITERATIVE_SCHUR linear solver.
Change-Id: Ifb8bce02bc5f5ceebc74f961eefd3f6dd2ffab4a
Use same instance of a PRNG throughout by passing it to methods and
functions as an argument to generate random numbers without breaking the
sequence.
Change-Id: Ib024bbc1ea2d14e4b9afb71857856a5fb77b1667
The code that verifies that the linear solver is configuration
specified by the user has grown into a rat's nest. This CL
attempts to bring some order to this madness.
Fixes https://github.com/ceres-solver/ceres-solver/issues/852
Change-Id: I3f34c0e27da13a6412117dee43ef2d9ec3835b64
These changes came about from testing the power bundle adjustment
integration CL.
1. Allow Solver::Options::max_linear_solver_iterations == 0.
2. Simplify the logic for when inverse(F'F) is computed.
3. norm_b -> norm_rhs in ConjugateGradientsSolver.
Change-Id: I50c19e1f24a4cc08ed60e3a3032b96b37bcada9f
Implementation of "Power Bundle Adjustment for Large-Scale 3D
Reconstruction" by Weber et. al. added in the form of preconditioner.
Change-Id: Ie85526a5fc46f74256f6dfe9173c3571f7160f3a
These methods were historically poorly named and every time I read code
I get confused whether they are just multiplying or multiplying and
adding. Clarifying them also gives us the changce to introduce
RightMultiply and LeftMultiply methods in the base class which will
simplify a number call sites in a subsequent CL.
Fixes https://github.com/ceres-solver/ceres-solver/issues/855
Change-Id: Ice4fb483f1acd02527a6dd753ef0c5a66037f4b0
1. Convert it from a class to a template function. Where the
template parameter is "DenseVectorType". This allows us
to have a single implementation of Conjugate Gradients
without worrying about where the matrix and the vectors
are stored or what their internal representation is.
For the case of CPU based vectors, we abstract operations
on Eigen vectors using eigen_vector_ops.
2. Introduce ConjugateGradientsLinearOperator which is
templated on DenseVectorType. It is the matrix vector
multiplication abstraction.
3. Port the tests and all usages of ConjugateGradientsSolver
to this new implementation.
4. Introduce Eigen::Vector based RightMultiply and LeftMultiply
methods into LinearOperator which by default delete to the
bare pointer based interfaces.
5. Add an identity preconditioner.
These changes are being made in preparation for adding a CUDA
based CGNR solver.
Change-Id: I9da36dc6c131856dd1a4aa7e645aaf12d25dd79b
This PR changes the Sphere and Line Manifold formulations so that their
tangent spaces represent traveled angles (for the sphere and the line
direction vector) and traveled distance (for the line origin). These
magnitudes were previously halved according to "Hartley & Zisserman
(2nd Edition)", but in the majority of the state of the art this is not
done, following the convention that magnitudes in the tangent space of
the unit sphere represent geodesic distances traveled on that manifold.
The same scale factor appears in the Quaternion Manifold implementation
and will be studied in a further PR.
This PR also adds an additional case in the Sphere Minus operator when
hy_norm == 0. The value of y_minus_x was fixed to 0 but actually its
last term can also be Pi depending on y_last.
Finally, new unit tests for the Plus and Minus operator are added, along
with new tests for the 2D Sphere (a.k.a. Circle) Manifold.
Change-Id: I9456f1675b20da49bede5d6759aabf3cdfb26eae
1. Remove an unused variable from block_sparse_matrix.cc
2. Add explicit types to the distributions to get around
-Wctad-maybe-unsupported
Change-Id: Ib7d606fbfe2b93ba4fce408f38ee4f7626b74ff0
* Fixed an alignment bug with EventLogger.
* Added expected solutions to some LinearLeastSquaresProblem tests.
* Expanded ContextImpl to initialize cuSparse.
Change-Id: I2d7556a0509e9b7c56c9cd5cfa75cb85614395dc
InnerProductComputer was assuming that the input matrix had
at least one structurally non-zero block. As a result sometimes
when InnerProductComputer.SubMatrix test generated matrices
where the submatrix was structurally zero it would cause
a segmentation fault.
This changes fixes this issue and reduces the threshold for the
minimum block density so that there is a much higher chance of
such matrices occuring as part of testing.
Fixes https://github.com/ceres-solver/ceres-solver/issues/820
Change-Id: Iec3a2431e646f0c2aac9e0b994531fa72323f329
* Renamed several interfaces to CudaBuffer for clarity and consistency.
* Added unit tests for custom Cuda kernels.
* Set specific CUDA architectures if the CMake version supports it.
Change-Id: I269fb1089b80b25e17bca772ef8d70e7894214b8
sparse linear solvers.
* Add methods to convert TripletSparseMatrix and BlockSparseMatrix to
CRSMatrix structure.
* Added tests for conversion of TripletSparseMatrix and BlockSparseMatrix
to CRSMatrix structure.
* Added documentation on the BlockSparseMatrix structure.
Change-Id: I020cfa91c301567ceeb39ff2064183c5d88c9ed5
On problem-744-543562-pre.txt
The time spent in linear solver on my M1 Pro is
eigen 81.550970
eigen+mixed 54.107383
LAPACK 47.078127
LAPACK+mixed 28.639868
Solution quality is unaffected.
The implementation of RefinedDenseCholesky and DenseIterativeRefiner
are straightforward ports of RefinedSparseCholesky and
SparseIterativeRefiner (formerly IterativeRefiner).
It maybe possible to refactor the SparseCholesky and DenseCholesky
interfaces so that this code duplication can be removed in the
future.
Change-Id: I921334224cb97629a60390f2add822de207f7923
CERES_METIS_VERSION needs to be set if either Eigen or
SuiteSparse are using it. Previously, we were conditioning it
only on EIGENMETIS being ON.
Change-Id: I380a3b138b79903aa142b560ed46f438ff549a82
Because we store the matrix as row major matrix, the
obvious Eigen expression performs rather poorly. A straight
c++ loop speeds things up considerably.
Also replace use of matrix() with direct use of m_.
Change-Id: I3d6166df4765ad8400ab9602a54b65fd21b1d50f
Sameer Agarwal