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And make them part of the blender_test runner. The one exception is blenlib
performance tests, which we don't want to run by default. They remain in their
own executable.
Differential Revision: https://developer.blender.org/D8498
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This removes extern-C blocks around other includes and adds
such blocks for some headers that need them.
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This is not currently used and will take some work to support with TBB, so
remove it until we have a new implementation based on TBB.
Fixes T76005, parallel range pool tests failing.
Ref D7475
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This code allows to push a set of different operations all based on
iterations over a range of indices, and then process them all at once
over multiple threads.
This commit also adds unit tests for both old un-pooled, and new pooled
task_parallel_range family of functions, as well as some basic
performances tests.
This is mainly interesting for relatively low amount of individual
tasks, as expected.
E.g. performance tests on a 32 threads machine, for a set of 10
different tasks, shows following improvements when using pooled version
instead of ten sequential calls to BLI_task_parallel_range():
| Num Items | Sequential | Pooled | Speed-up |
| --------- | ---------- | ------- | -------- |
| 10K | 365 us | 138 us | 2.5 x |
| 100K | 877 us | 530 us | 1.66 x |
| 1000K | 5521 us | 4625 us | 1.25 x |
Differential Revision: https://developer.blender.org/D6189
Note: Compared to previous commit yesterday, this reworks atomic handling in
parallel iter code, and fixes a dummy double-free bug.
Now we should only use the two critical values for synchronization from
atomic calls results, which is the proper way to do things.
Reading a value after an atomic operation does not guarantee you will
get the latest value in all cases (especially on Windows release builds
it seems).
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This reverts commit f9028a3be1f77c01edca44a68894e2ba9d9cfb14.
This is giving weird heisenbug crash on only Windows release builds...
Reverting until we understand to issue.
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This code allows to push a set of different operations all based on
iterations over a range of indices, and then process them all at once
over multiple threads.
This commit also adds unit tests for both old un-pooled, and new pooled
`task_parallel_range` family of functions, as well as some basic
performances tests.
This is mainly interesting for relatively low amount of individual
tasks, as expected.
E.g. performance tests on a 32 threads machine, for a set of 10
different tasks, shows following improvements when using pooled version
instead of ten sequential calls to `BLI_task_parallel_range()`:
| Num Items | Sequential | Pooled | Speed-up |
| --------- | ---------- | ------- | -------- |
| 10K | 365 us | 138 us | 2.5 x |
| 100K | 877 us | 530 us | 1.66 x |
| 1000K | 5521 us | 4625 us | 1.25 x |
Differential Revision: https://developer.blender.org/D6189
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This new function is part of the 'parallel for loops' functions. It
takes an iterator callback to generate items to be processed, in
addition to the usual 'process' func callback.
This allows to use common code from BLI_task for a wide range of custom
iteratiors, whithout having to re-invent the wheel of the whole tasks &
data chuncks handling.
This supports all settings features from `BLI_task_parallel_range()`,
including dynamic and static (if total number of items is knwon)
scheduling, TLS data and its finalize callback, etc.
One question here is whether we should provide usercode with a spinlock
by default, or enforce it to always handle its own sync mechanism.
I kept it, since imho it will be needed very often, and generating one
is pretty cheap even if unused...
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Additionaly, this commit converts (currently unused)
`BLI_task_parallel_listbase()` to use that generic code. This was done
mostly as proof of concept, but performance-wise it shows some
interesting data, roughly:
- Very light processing (that should not be threaded anyway) is several
times slower, which is expected due to more overhead in loop management
code.
- Heavier processing can be up to 10% quicker (probably thanks to the
switch from dynamic to static scheduling, which reduces a lot locking
to fill-in the per-tasks chunks of data). Similar speed-up in
non-threaded case comes as a surprise though, not sure what can
explain that.
While this conversion is not really needed, imho we should keep it
(instead of existing code for that function), it's easier to have
complex handling logic in as few places as possible, for maintaining and
for improving it.
Note: That work was initially done to allow for D5372 to be possible... Unfortunately that one proved to be not better than orig code on performances point of view.
Reviewed By: sergey
Differential Revision: https://developer.blender.org/D5371
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performances benchmarks.
Nothing special to mention about regression test itself, it basically
mimics the one for `BLI_task_parallel_mempool()`...
Basic performances benchmarks do not tell us much, besides the fact that
for very light processing of listbase, even with 100k items,
single-thread remains an order of magnitude faster than threaded code.
Synchronization is just way too expensive in that case with current
code. This should be partially solvable with much bigger (and
configurable) chunk sizes though (current ones are just ridiculous
for such cases ;) )...
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