1. Add abseil-cpp as a submodule. We are tracking the latest LTS
release, which is lts_2024_01_16.
2. Replace glog/gflags with absl::log and absl::flags.
3. Remove miniglog
4. Also take a whack at making the bazel build work with
abseil-cpp and gtest.
There are a number of TODOs in this CL that still need to be resolved.
Change-Id: I39355ed7d61375be4ebcbc8596d9cc70acc1c678
1. Add a version history
2. Update copyright years across the code base
3. Run format_all.sh
4. Update version strings from 2.1.0 to 2.2.0 in the docs and
elsewhere.
Change-Id: I46d8d479d54bd6002d532785e67342106e73c9ac
- TripletSparseMatrix in BlockRandomAccessSparseMatrix is replaced with
BlockSparseMatrix
- BlockSparseMatrix::ToCompressedRowSparseMatrix is performed in a
direct sort-less way
Change-Id: Ib951fda1b9394050e2c47a9721172c5e3c674801
Main focus of this change is to parallelize remaining operations (most of them
are operations on vectors) in code-path utilized with iterative Schur
complement.
Parallelization is handled using lazy evaluation of Eigen expressions.
On linux pc with intel 8176 processor parallelization of vector operations has
the following effect:
Running ./bin/parallel_vector_operations_benchmark
Run on (112 X 3200.32 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: 3.30, 8.41, 11.82
-----------------------------------
Benchmark Time
-----------------------------------
SetZero 10009532 ns
SetZeroParallel/1 10024139 ns
...
SetZeroParallel/16 877606 ns
Negate 4978856 ns
NegateParallel/1 5145413 ns
...
NegateParallel/16 721823 ns
Assign 10731408 ns
AssignParallel/1 10749944 ns
...
AssignParallel/16 1829381 ns
D2X 15214399 ns
D2XParallel/1 15623245 ns
...
D2XParallel/16 2687060 ns
DivideSqrt 8220050 ns
DivideSqrtParallel/1 9088467 ns
...
DivideSqrtParallel/16 905569 ns
Clamp 3502010 ns
ClampParallel/1 4507897 ns
...
ClampParallel/16 759576 ns
Norm 4426782 ns
NormParallel/1 4442805 ns
...
NormParallel/16 430290 ns
Dot 9023276 ns
DotParallel/1 9031304 ns
...
DotParallel/16 1157267 ns
Axpby 14608289 ns
AxpbyParallel/1 14570825 ns
...
AxpbyParallel/16 2672220 ns
-----------------------------------
Multi-threading of vector operations in ISC and program evaluation results into
the following improvement:
Running ./bin/evaluation_benchmark
--------------------------------------------------------------------------------------
Benchmark this 2fd81de
--------------------------------------------------------------------------------------
Residuals<problem-13682-4456117-pre.txt>/1 4136 ms 4292 ms
Residuals<problem-13682-4456117-pre.txt>/2 2919 ms 2670 ms
Residuals<problem-13682-4456117-pre.txt>/4 2065 ms 2198 ms
Residuals<problem-13682-4456117-pre.txt>/8 1458 ms 1609 ms
Residuals<problem-13682-4456117-pre.txt>/16 1152 ms 1227 ms
ResidualsAndJacobian<problem-13682-4456117-pre.txt>/1 19759 ms 20084 ms
ResidualsAndJacobian<problem-13682-4456117-pre.txt>/2 10921 ms 10977 ms
ResidualsAndJacobian<problem-13682-4456117-pre.txt>/4 6220 ms 6941 ms
ResidualsAndJacobian<problem-13682-4456117-pre.txt>/8 3490 ms 4398 ms
ResidualsAndJacobian<problem-13682-4456117-pre.txt>/16 2277 ms 3172 ms
Plus<problem-13682-4456117-pre.txt>/1 339 ms 322 ms
Plus<problem-13682-4456117-pre.txt>/2 220 ms
Plus<problem-13682-4456117-pre.txt>/4 128 ms
Plus<problem-13682-4456117-pre.txt>/8 78.0 ms
Plus<problem-13682-4456117-pre.txt>/16 49.8 ms
ISCRightMultiplyAndAccumulate<problem-13682-4456117-pre.txt>/1 2434 ms 2478 ms
ISCRightMultiplyAndAccumulate<problem-13682-4456117-pre.txt>/2 2706 ms 2688 ms
ISCRightMultiplyAndAccumulate<problem-13682-4456117-pre.txt>/4 1430 ms 1548 ms
ISCRightMultiplyAndAccumulate<problem-13682-4456117-pre.txt>/8 742 ms 883 ms
ISCRightMultiplyAndAccumulate<problem-13682-4456117-pre.txt>/16 438 ms 555 ms
ISCRightMultiplyAndAccumulateDiag<problem-13682-4456117-pre.txt>/1 2438 ms 2481 ms
ISCRightMultiplyAndAccumulateDiag<problem-13682-4456117-pre.txt>/2 2565 ms 2790 ms
ISCRightMultiplyAndAccumulateDiag<problem-13682-4456117-pre.txt>/4 1434 ms 1551 ms
ISCRightMultiplyAndAccumulateDiag<problem-13682-4456117-pre.txt>/8 765 ms 892 ms
ISCRightMultiplyAndAccumulateDiag<problem-13682-4456117-pre.txt>/16 435 ms 559 ms
JacobianSquaredColumnNorm<problem-13682-4456117-pre.txt>/1 1278 ms
JacobianSquaredColumnNorm<problem-13682-4456117-pre.txt>/2 1555 ms
JacobianSquaredColumnNorm<problem-13682-4456117-pre.txt>/4 833 ms
JacobianSquaredColumnNorm<problem-13682-4456117-pre.txt>/8 459 ms
JacobianSquaredColumnNorm<problem-13682-4456117-pre.txt>/16 250 ms
JacobianScaleColumns<problem-13682-4456117-pre.txt>/1 1468 ms
JacobianScaleColumns<problem-13682-4456117-pre.txt>/2 1871 ms
JacobianScaleColumns<problem-13682-4456117-pre.txt>/4 957 ms
JacobianScaleColumns<problem-13682-4456117-pre.txt>/8 528 ms
JacobianScaleColumns<problem-13682-4456117-pre.txt>/16 294 ms
End-to-end improvements with bundle_adjuster invoked with
./bin/bundle_adjuster --num_threads 28 --num_iterations 40 \
--linear_solver iterative_schur \
--preconditioner jacobi --input
---------------------------------------------
Problem this 2fd81de
---------------------------------------------
problem-13682-4456117-pre.txt 508.6 892.7
problem-1778-993923-pre.txt 763.8 1129.9
problem-1723-156502-pre.txt 6.3 14.4
problem-356-226730-pre.txt 76.3 116.2
problem-257-65132-pre.txt 38.6 52.0
Change-Id: Ie31cc5015f13fa479c16ffb5ce48c9b880990d49
Parallel for with user-supplied [cumulative] iteration costs allows to
get performance improvements on problems with significantly different
time requirements per parallel loop iteration.
One of those problems is left multiplication with block-sparse matrix.
Using number of non-zero values per column block, we partition column
blocks into contiguous sets with approximately equal number of
operations to be performed.
Change-Id: I4a862a10a586cdfbec22e8168a3423537039abc2
Add structure of transposed matrix to BlockSparseMatrix
Number of non-zero values per row block and cumulative non-zero
values count are maintained for transposed structure
Change-Id: Icf38bb7a734ca695c788579eece1c92d36d78e54
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
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
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
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
- Change formatting standard to Cpp11. Main difference is not having
the space between two closing >> for nested templates. We don't
choose c++14, because older versions of clang-format (version 9
and earlier) don't know this value yet, and it doesn't make a
difference in the formatting.
- Apply clang-format to all (non generated) internal source files.
- Manually fix some code sections (clang-format on/off) and c-strings
- Exclude some embedded external files with very different formatting
(gtest/gmock)
- Add script to format all source files
Change-Id: Ic6cea41575ad6e37c9e136dbce176b0d505dc44d
Since Ceres is moving to using GitHub for issues, and the Google
Code URL in the current copyright header will soon become invalid,
update all the headers.
Change-Id: I1fce70375d1bcf098591f07b4d8f01a5c1e0789c
Sameer Agarwal