When |OS_FUCHSIA| is defined (even when |FUCHSIA_SDK| is defined as
well), use the Fuchsia SDK trace macros rather than the Dart timeline.
Reasons for doing this include:
Fuchsia's trace macros support categories. This allows one to
distinguish between (e.g.) "flutter" and "skia" trace events for trace
recording and trace visualization.
Fuchsia has existing in tree benchmarks that depend on finding certain
events under category "flutter".
See the Fuchsia performance mailing list discussion for more context.
- Add the functionality to merge and unmerge MessageLoopTaskQueues
This introduces a notion of a "owning" and "subsumed" queue ids.
Owning queue will take care of the tasks submitted to both that and it's
subsumed queue.
- The tasks submitted still maintain the queue affinity
- Same for the task observers
- Also adds MergedQueuesRunner which grabs both the locks owner
and subsumed queues in RAII fashion.
- Also use task queue id to verify if we are running
in the same thread.
- This is to enable merging the backed message loop task
queues to enable dynamic thread merging in IOS.
This patch reworks image decompression and collection in the following ways
because of misbehavior in the described edge cases.
The current flow for realizing a texture on the GPU from a blob of compressed
bytes is to first pass it to the IO thread for image decompression and then
upload to the GPU. The handle to the texture on the GPU is then passed back to
the UI thread so that it can be included in subsequent layer trees for
rendering. The GPU contexts on the Render & IO threads are in the same
sharegroup so the texture ends up being visible to the Render Thread context
during rendering. This works fine and does not block the UI thread. All
references to the image are owned on UI thread by Dart objects. When the final
reference to the image is dropped, the texture cannot be collected on the UI
thread (because it has not GPU context). Instead, it must be passed to either
the GPU or IO threads. The GPU thread is usually in the middle of a frame
workload so we redirect the same to the IO thread for eventual collection. While
texture collections are usually (comparatively) fast, texture decompression and
upload are slow (order of magnitude of frame intervals).
For application that end up creating (by not necessarily using) numerous large
textures in straight-line execution, it could be the case that texture
collection tasks are pending on the IO task runner after all the image
decompressions (and upload) are done. Put simply, the collection of the first
image could be waiting for the decompression and upload of the last image in the
queue.
This is exacerbated by two other hacks added to workaround unrelated issues.
* First, creating a codec with a single image frame immediately kicks of
decompression and upload of that frame image (even if the frame was never
request from the codec). This hack was added because we wanted to get rid of
the compressed image allocation ASAP. The expectation was codecs would only be
created with the sole purpose of getting the decompressed image bytes.
However, for applications that only create codecs to get image sizes (but
never actually decompress the same), we would end up replacing the compressed
image allocation with a larger allocation (device resident no less) for no
obvious use. This issue is particularly insidious when you consider that the
codec is usually asked for the native image size first before the frame is
requested at a smaller size (usually using a new codec with same data but new
targetsize). This would cause the creation of a whole extra texture (at 1:1)
when the caller was trying to “optimize” for memory use by requesting a
texture of a smaller size.
* Second, all image collections we delayed in by the unref queue by 250ms
because of observations that the calling thread (the UI thread) was being
descheduled unnecessarily when a task with a timeout of zero was posted from
the same (recall that a task has to be posted to the IO thread for the
collection of that texture). 250ms is multiple frame intervals worth of
potentially unnecessary textures.
The net result of these issues is that we may end up creating textures when all
that the application needs is to ask it’s codec for details about the same (but
not necessarily access its bytes). Texture collection could also be delayed
behind other jobs to decompress the textures on the IO thread. Also, all texture
collections are delayed for an arbitrary amount of time.
These issues cause applications to be susceptible to OOM situations. These
situations manifest in various ways. Host memory exhaustion causes the usual OOM
issues. Device memory exhaustion seems to manifest in different ways on iOS and
Android. On Android, allocation of a new texture seems to be causing an
assertion (in the driver). On iOS, the call hangs (presumably waiting for
another thread to release textures which we won’t do because those tasks are
blocked behind the current task completing).
To address peak memory usage, the following changes have been made:
* Image decompression and upload/collection no longer happen on the same thread.
All image decompression will now be handled on a workqueue. The number of
worker threads in this workqueue is equal to the number of processors on the
device. These threads have a lower priority that either the UI or Render
threads. These workers are shared between all Flutter applications in the
process.
* Both the images and their codec now report the correct allocation size to Dart
for GC purposes. The Dart VM uses this to pick objects for collection. Earlier
the image allocation was assumed to 32bpp with no mipmapping overhead
reported. Now, the correct image size is reported and the mipmapping overhead
is accounted for. Image codec sizes were not reported to the VM earlier and
now are. Expect “External” VM allocations to be higher than previously
reported and the numbers in Observatory to line up more closely with actual
memory usage (device and host).
* Decoding images to a specific size used to decode to 1:1 before performing a
resize to the correct dimensions before texture upload. This has now been
reworked so that images are first decompressed to a smaller size supported
natively by the codec before final resizing to the requested target size. The
intermediate copy is now smaller and more promptly collected. Resizing also
happens on the workqueue worker.
* The drain interval of the unref queue is now sub-frame-interval. I am hesitant
to remove the delay entirely because I have not been able to instrument the
performance overhead of the same. That is next on my list. But now, multiple
frame intervals worth of textures no longer stick around.
The following issues have been addressed:
* https://github.com/flutter/flutter/issues/34070 Since this was the first usage
of the concurrent message loops, the number of idle wakes were determined to
be too high and this component has been rewritten to be simpler and not use
the existing task runner and MessageLoopImpl interface.
* Image decoding had no tests. The new `ui_unittests` harness has been added
that sets up a GPU test harness on the host using SwiftShader. Tests have been
added for image decompression, upload and resizing.
* The device memory exhaustion in this benchmark has been addressed. That
benchmark is still not viable for inclusion in any harness however because it
creates 9 million codecs in straight-line execution. Because these codecs are
destroyed in the microtask callbacks, these are referenced till those
callbacks are executed. So now, instead of device memory exhaustion, this will
lead to (slower) exhaustion of host memory. This is expected and working as
intended.
This patch only addresses peak memory use and makes collection of unused images
and textures more prompt. It does NOT address memory use by images referenced
strongly by the application or framework.
* Refactor to move Task Queue to its own class
- This is to help with sharing task queue among
multiple message loops going forward.
- currently there is 1:1 mapping between task queue
and message loop, we are still maintaining the semantics
for this change.
* Add mutex include
* Most of the waking up changes minus test failures
* Refactor MessageLoopImpl to be Wakeable
- Makes testing easier by letting us putting a TestWakeable
- Also move the waking up logic to the task queue
* add tests
* Fix formatting and license
* Refactor to move Task Queue to its own class
- This is to help with sharing task queue among
multiple message loops going forward.
- currently there is 1:1 mapping between task queue
and message loop, we are still maintaining the semantics
for this change.
* Add mutex include
* Add unit tests for task queue
* fix formatting
* license
* Allow specifying both Dart and non-Dart fixtures in engine unittests.
This fixes numerous issues in the way in which fixtures were managed
in the engine unit-tests.
* Instead of only being able to specify Dart fixtures, unit-tests may specify
non-Dart fixtures as well. These are simply copied over to the fixtures
directory known to the unit-test at runtime.
* An issue where numerous Dart files could be given to the kernel snapshotter
has been addressed. It was anticipated that such a (legal) invocation to the
kernel snapshotter would produce a snapshot with the contents of all the Dart
files added to the root library. This is incorrect and the behavior in this
case is undefined.
* Dart files referenced by the main Dart file are correctly tracked via a
depfile.
* The snapshotter arguments have been cleaned up to get rid of unused
arguments (`—strong`) and the use of the VM product mode argument has been
corrected to no longer depend on the Flutter product mode.
Corects a bnuch of typeos throughout teh engien codebsae. Also makes
a couple minor Commonwealth -> US spelling adjustments for consistency
with the rest of Flutter's codebase.
Made use of `misspell` tool:
https://github.com/client9/misspell
In practice, the implementations of std::chrono seem to match our
implementations, so this makes the code simpler and easier to maintain,
as well as potentially improving over time without changes on our part.
Addresses overflow issues with timer multiplication by using std::chrono
instead of QueryPerformanceCounter. The Windows implementation of
std::chrono uses QPC, so this should have no impact on performance or
clock resolution.
Fixes#32121
This does not actually import the runners into the engine. It only sets up the targets so they need no modifications are necessary when the migration is done. The engine has been verified to build in both buildroots.
While working on #8659, I had planned on renaming this file to
thread_local_unique_ptr.h, but decided against that at the last minute
before sending for review. However, when reverting the file rename, I
forgot to also revert the header guard change.
If the mapping callback is not set or it the callback returns invalid data, ICU initialization will be embedder responsibility.
This affects all embedders and the following have been audited:
* Android: Via a symbol mapping.
* iOS: Via a file mapping.
* Embedder: Via a file mapping.
* Fuchsia: Via a VMO mapping
* Test shells and Flutter tester: Via file mapping with ICU data needing to be next to the executable.
The GetMapping calls removed in this patch had the same code and had to be repeated across different test harnesses as well as in dart_snapshot.cc. Just make this a factory method so the code is less verbose.
This reverts commit 1c06891c101f530c1f46337b457ba02e0f6c8633.
Windows depends on referencing the snapshot symbols directly instead of
via dlsym. Something in the way these symbolsa are generated in
bin_to_assembly.py is causing them to be inaccessible at runtime.
When flutter/synchronization was first authored, we did not own fml (it was called fxl then). Now we do, so use a single spot for such utilities. The pipeline was meant to be a general purpose utility that was only ever used by the animator (it even has animator specific tracing), so move that to shell instead (where the animator resides).
Currently, all Flutter threads are managed by the engine itself. This works for
all threads except the platform thread. On this thread, the engine cannot see
the underlying event multiplexing mechanism. Using the new task runner
interfaces, the engine can relinquish the task of setting up the event
multiplexing mechanism and instead have the embedder provide one for it during
setup.
This scheme is only wired up for the platform thread. But, the eventual goal
is to expose this message loop interoperability for all threads.
This will allow us to easily visualize the time the platform informed the engine of a vsync event, its arguments, and when the engine began its UI thread workload using this information.
Verified that the tests fail on issues like https://github.com/flutter/engine/pull/8166. Unfortunately, there is no x-platform way to perform this check but this should gate incorrect traces being added to the engine.
