CUDA CGNR, Part 4: CudaCgnrSolver

* 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
21 files changed
tree: caba246cd62e6635b98233af2b8e14c76569d707
  1. .github/
  2. bazel/
  3. cmake/
  4. config/
  5. data/
  6. docs/
  7. examples/
  8. include/
  9. internal/
  10. scripts/
  11. .clang-format
  12. .gitignore
  13. BUILD
  14. CITATION.cff
  15. CMakeLists.txt
  16. CONTRIBUTING.md
  17. LICENSE
  18. package.xml
  19. README.md
  20. WORKSPACE
README.md

Android Linux macOS Windows

Ceres Solver

Ceres Solver is an open source C++ library for modeling and solving large, complicated optimization problems. It is a feature rich, mature and performant library which has been used in production at Google since 2010. Ceres Solver can solve two kinds of problems.

  1. Non-linear Least Squares problems with bounds constraints.
  2. General unconstrained optimization problems.

Please see ceres-solver.org for more information.