This change also adds TimeRecorder which records time at the start
of each frame to capture the latest vsync target display time and
wires it in to the rasterizer to add trace events when there is a lag.
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.
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.
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.
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.
