With the addition of C++11 support we can simplify the parallel for code by
removing the ifdef branching. Converts coordinate_descent_minimizer.cc to use
the thread_id ParallelFor API.
Tested by building with OpenMP, C++11 threads, TBB, and no threads. All tests
pass.
Also compared timing via the bundle adjuster.
./bin/bundle_adjuster --input=../problem-744-543562-pre.txt
With OpenMP num_threads=8
Head:
Time (in seconds):
Residual only evaluation 0.807753 (5)
Jacobian & residual evaluation 4.489404 (6)
Linear solver 41.826481 (5)
Minimizer 50.745857
Total 73.294424
CL:
Time (in seconds):
Residual only evaluation 0.970483 (5)
Jacobian & residual evaluation 4.647438 (6)
Linear solver 41.781892 (5)
Minimizer 50.848904
Total 73.089983
With OpenMP num_threads=1
HEAD:
Time (in seconds):
Residual only evaluation 2.990246 (5)
Jacobian & residual evaluation 14.132090 (6)
Linear solver 79.631951 (5)
Minimizer 100.281847
Total 122.946267
CL:
Time (in seconds):
Residual only evaluation 3.075178 (5)
Jacobian & residual evaluation 13.966451 (6)
Linear solver 77.005441 (5)
Minimizer 97.568712
Total 120.410454
Change-Id: I1857d7943073be7465b6c6476bf46ab11c5475a3
Previously, the thread ID was acquired and released on every iteration
of the for loop. The C++11 concurrent queue implementation is much
slower than TBB's version and consequently this was a huge bottleneck.
This introduces another ParallelFor API which takes the thread ID as a
parameter in the evaluation function. This allows us to acquire and
release the thread ID for each block of work which drastically improves
the performance.
This change brings us on par with OpenMP and TBB. See below for a
timing comparison. Note: in this example this CLs C++11 version is
faster to compute the residuals because TBB still must acquire the
thread ID on every iteration, which has some overhead.
Tested by building and running tests for no threading, OpenMP, TBB, and
C++11 threads. Also ran bazel tests.
./bin/bundle_adjuster --input=problem-744-543562-pre.txt --num_threads=8
C++11 @Head
Time (in seconds):
Residual only evaluation 7.819692 (5)
Jacobian & residual evaluation 11.606063 (6)
Linear solver 47.860195 (5)
Minimizer 70.877072
Total 90.806338
---------------------------------------------------
C++11 (This CL)
Time (in seconds):
Residual only evaluation 1.217500 (5)
Jacobian & residual evaluation 5.796112 (6)
Linear solver 44.080873 (5)
Minimizer 54.635524
Total 77.640072
---------------------------------------------------
OpenMP
Time (in seconds):
Residual only evaluation 0.797023 (5)
Jacobian & residual evaluation 5.633916 (6)
Linear solver 43.280020 (5)
Minimizer 53.199058
Total 76.250861
---------------------------------------------------
TBB
Time (in seconds):
Residual only evaluation 1.911095 (5)
Jacobian & residual evaluation 5.557807 (6)
Linear solver 44.074680 (5)
Minimizer 55.002688
Total 78.052687
---------------------------------------------------
No Threads
Time (in seconds):
Residual only evaluation 2.939212 (5)
Jacobian & residual evaluation 18.519874 (6)
Linear solver 74.017837 (5)
Minimizer 98.980080
Total 122.216391
Change-Id: I3af959b0771bbdfe8cad8c13896191d6ac903181
This change while algebraically equivalent was causing non-trivial
changes in the numbers being reported by ceres and in some cases
they got worse.
Also fix a small header include in parallel_for_test.cc which
was discovered as part of testing this patch.
Change-Id: I8c8d61538819f0b25af6059fa75ad068fc22f5e4
A Ceres Context holds common global state that can be re-used within
Ceres. The Context current contains a thread pool if compiling with
C++11 threading support. Threads are expensive to create and destroy so
it is good to maintain across multiple Ceres solves.
Tested by compiling with and without TBB support and ran unit tests. Ran
bazel as well.
Change-Id: I82f598dfae642aa0e81a6039dc174608a5e8dbfb
This is in preparation for adding support for a c++11 based parallel
for implementation. The parallel for abstraction does not have the
ability to constrain the total number of threads in nested for loops.
This is solved by distributing the number of threads evenly between
the nested for loops. Adds a TODO to consolidate the next for loops
into a single loop that can be properly split between threads.
Tested by building with TBB and running tests.
Change-Id: I546973b9a4d19b9cdd53caff55d1c80bac8ea953
Mike Vitus