Tag Archives: massif-visualizer

Heap profiling of gnome-software

The last week has been a fun process of starting to profile gnome-software with the aim of lowering its resource consumption and improving its startup speed. gnome-software is an important part of the desktop, so having it work speedily, especially on resource constrained computers, is important. This work is important for Endless OS, and is happening upstream.

To start with, I’ve looked at gnome-software’s use of heap memory, particularly during startup. While allocating lots of memory on the heap isn’t always a bad thing (caches are a good example of heap allocations being used to speed up a program overall), it’s often a sign of unnecessary work being done. Large heap allocations do take a few tens of milliseconds to be mapped through the allocator too. To do this profiling, I’ve been using valgrind’s massif tool, and massif-visualizer to explore the heap allocations. I could also have used heaptrack, or gobject-list, but they’re tools to explore another time.

Profile your app

Before diving into the process of optimising, the summary is that this work dropped gnome-software’s pixbuf heap usage by 24MB, and its non-pixbuf heap usage after initialisation (i.e. at the point when the main window is visible and ready to use) by 12%, from 15.7MB to 13.7MB (on my set of flatpak repositories on Fedora 32). I’ve been doing this work upstream, and it’ll trickle down to the downstream copy of gnome-software in Endless OS.

There is more low-hanging fruit to explore, and plenty of opportunities to dive in and trim more memory usage from gnome-software, or other apps. If you’re interested, please dive in! Get in touch if you have questions, or post them on GNOME’s Discourse instance and tag me. I’ll be happy to help!

How to profile heap usage

Profiling heap usage using massif is an iterative process: run your program under massif, do some actions in the program, quit, then open the resulting massif.out.pid file in massif-visualizer and see where allocations are coming from. Pick an allocation which looks large or unnecessary, find it in the code, optimise the code (if possible), and then repeat the process.

When running it, I wait for gnome-software to finish loading its main window, then I exit; so all this profiling work is for allocations during startup.

I run massif using this script, which I’ve put in ~/.local/bin/massif:

export G_SLICE=always-malloc
exec valgrind --tool=massif --num-callers=50 --suppressions=/usr/share/glib-2.0/valgrind/glib.supp --trace-children=no --threshold=0.1 --alloc-fn=g_malloc --alloc-fn=g_object_new --alloc-fn=g_malloc0 --alloc-fn=g_malloc0_n --alloc-fn=g_malloc_n --alloc-fn=g_realloc --alloc-fn=g_realloc_n --alloc-fn=g_slice_alloc --alloc-fn=g_slice_alloc0 --alloc-fn=g_type_create_instance --alloc-fn=g_object_new_internal --alloc-fn=g_object_new_with_properties --alloc-fn=g_object_newv --alloc-fn=g_object_new_valist --alloc-fn=g_try_malloc --alloc-fn=g_try_malloc_n --alloc-fn=g_hash_table_realloc_key_or_value_array --alloc-fn=realloc_arrays --alloc-fn=g_hash_table_resize --alloc-fn=g_hash_table_maybe_resize --alloc-fn=g_hash_table_insert_node --alloc-fn=g_hash_table_insert_internal --alloc-fn=g_hash_table_setup_storage "$@"

All the --alloc-fn arguments hide internal GLib functions so that the output is a little easier to interpret directly. There currently isn’t a way to store them in a config file or suppression file.

Some typical output from massif-visualizer before any code improvements:

massif-visualizer output from before code improvements to gnome-software, libxmlb or json-glib. The majority of the allocations are for pixbufs.

The window shows heap allocations against time in instructions executed. The breakdown of where each allocation came from is known in detail at key snapshots (which are expandable in the side pane), and the total heap usage is known in summary for the other snapshots, which allows the graph to be drawn. Allocations coming from different functions are coloured differently in the graph.

There are two sets of allocations to focus on: the red plateau between time 1e+10 and 1.6e+10, and the orange step from time 1e+10 onwards.

Selecting the red plateau shows the backtrace which led to its allocation in the side pane, and (despite some missing debug symbols, leading to the ‘???’ entries), it seems to have come from within libjpeg, as part of loading a JPEG pixbuf. gnome-software has various large JPEG images which are displayed in the featured app banners. It seems that libjpeg makes some big temporary allocations when loading a JPEG.

The orange step from 1e+10 onwards is another target. Looking at the backtraces, it seems it’s a series of similar allocations for pixbuf pixel storage for the featured app banners and for app icons. Some quick calculations show that each 1024×400 pixel banner will take around 6.5MB of memory to store its uncompressed pixels (at 16B per pixel).

From the graph and the backtraces, it seems that almost 100MB is used for pixbuf data for featured app banners. At 6.5MB per banner, that’s 15 banners, which seems reasonable. But actually gnome-software limits itself to 5 banners, so something’s amiss.

Style providers aren’t cheap

After adding some debug prints in GTK where it loads the pixbufs for CSS background properties, it became evident that the same few images were being loaded multiple times. CSS is used to style each featured app tile, including setting the background, since that allows a lot of artistic freedom quite easily. However, the CSS was being refreshed and set a few times for each tile, with a new GtkCssProvider each time. The old provider was staying in place, but with its properties overridden. This included the previously-loaded background image, which remained loaded but unused (essentially, leaked!). With that chased down, it was possible to fix the problem.

Back to profiling

With one issue investigated and fixed, the next step is to do another profiling run, find another target for reducing heap allocations, and repeat.

While we might have fixed one pixbuf bug in gnome-software, it does still use a lot of memory for pixbufs, since it displays a lot of high-resolution app icons. Those pixbuf allocations occupy a lot of space in the massif-visualizer view, and take up a large percentage of the ‘threshold’ of heap allocations which massif includes in its traces.

massif provides the --ignore-fn argument to allow certain allocations to be ignored, so that you can more easily profile others. So I did further profiling runs with a series of --ignore-fn arguments to ignore pixbuf allocations.

massif-visualizer output from before code improvements to gnome-software, libxmlb or json-glib, with pixbuf allocations ignored.

With the --ignore-fn arguments, and increasing the ‘Stacked diagrams’ level in the toolbar to show more individual areas on the graph, it’s now possible to see more detail on the largest non-pixbuf allocations, and hence easier to choose where to focus next.

massif-visualizer output from before code improvements to gnome-software, libxmlb or json-glib, with pixbuf allocations ignored, and more stacked diagrams shown.

From this screenshot, perhaps the next place to focus on would be GHashTable creation and insertions, since that totals around 1MB of the heap usage (once pixbufs are ignored).

Summary

I have iterated through the gnome-software massif profiles a few times, and have submitted various other fixes to gnome-software and libxmlb which are in the process of being reviewed and merged, but I won’t walk through each of them. There are still improvements to be made in future: gnome-software is quite complex!

In total, the changes reduced gnome-software’s heap usage at startup by 26MB, though the actual numbers will vary on other systems depending on how often feature tiles get refreshed, and how many apps and repositories you have configured.

These changes have not made a significant improvement to the startup time of gnome-software, which is more significantly influenced by network activity and file parsing (and the subject of some future work).

Hopefully this post gives a workable introduction to how to use massif on your own software. Please speak up if you have any questions. If you do profiling work on your software, please blog about it — it would be interesting to see what improvements are possible.