Profiling Uno Platform Applications

Profiling .NET Android/iOS applications

.NET provides the ability to do CPU profiling through dotnet-trace for iOS and Android applications.

Pre-requisites

Run the following commands

dotnet tool update -g dotnet-dsrouter
dotnet tool update -g dotnet-trace
dotnet tool update -g dotnet-gcdump

Profiling .NET iOS applications

[!NOTE] This documentation is based on .NET iOS profiling and .NET Android profiling documentation.

Profiling iOS apps needs to be done on a mac machine.

First, create an alias to mlaunch:

bash

cd [your-folder-with-the-csproj]
alias mlaunch=$(dotnet build -getProperty:MlaunchPath *.csproj -f net10.0-ios)

Profiling on an iOS Simulator

Build the app with the following parameters:

dotnetcli

cd [your-folder-with-the-csproj]
dotnet build -f net10.0-ios -p:DiagnosticAddress=127.0.0.1 -p:DiagnosticPort=9000 -p:DiagnosticSuspend=true -p:DiagnosticListenMode=listen

Find the simulator you want to run on:
dotnetcli
```
$ xcrun simctl list devices
```
Find a device that is shutdown or booted, and take note of its UDID.

Launch the app (it will be paused on startup waiting for the .NET tooling to connect):

bash

mlaunch --device :v2:udid=xxxxxxxx-yyyy-zzzz-aaaa-bbbbbbbbbbbb --wait-for-exit --stdout=$(tty) --stderr=$(tty) --launchsim=[your-app-path]/bin/Debug/net*-ios/*/*.app

Replace the UDID with the one you found above.

Once the app is waiting, go ahead and start profiling:
dotnetcli
```
dotnet-trace collect --dsrouter ios-sim --format speedscope
```

Optionally take a GC dump:

dotnetcli

dotnet-gcdump collect --dsrouter ios-sim

Profiling on a physical iOS device

Build the app with the following parameters:

dotnetcli

cd [your-folder-with-the-csproj]
dotnet build -f net10.0-ios -p:DiagnosticAddress=127.0.0.1 -p:DiagnosticPort=9000 -p:DiagnosticSuspend=true -p:DiagnosticListenMode=listen

Install & launch the app:

bash

mlaunch --installdev bin/Debug/net*/*/*.app --devname ... 
mlaunch --launchdev bin/Debug/net*/*/*.app --devname ... --wait-for-exit

Start CPU profiling:

dotnetcli

dotnet-trace collect --dsrouter ios --format speedscope

Optionally take a GC dump:
dotnetcli
```
dotnet-gcdump collect --dsrouter ios
```

Profiling on Android

Enable profiling in your application

In Platforms/Android/environment.conf, add one of the following lines:

For devices:

text

DOTNET_DiagnosticPorts=127.0.0.1:9000,suspend,connect

For emulators:

text

DOTNET_DiagnosticPorts=10.0.2.2:9000,suspend,connect

The suspend directive means that the application will wait for dotnet-trace connections before starting, nosuspend may also be used.

Profiling the application

Start the diagnostics router:
- For devices, run adb reverse tcp:9000 tcp:9001 then dotnet-dsrouter android -v debug
- For emulators, run dotnet-dsrouter android-emu -v debug
Run dotnet-trace, in the folder where you want your traces to be stored, using the PID provided by the dotnet-dsrouter output:
dotnetcli
```
dotnet-trace collect -p PID --format speedscope
```
Start the x64 emulator or the arm64 device

Running on a 32 bits device is not supported and will generate unusable traces in SpeedScope

Build the application with profiling enabled

dotnetcli

dotnet build -c Release -f net10.0-android -r android-arm64 -t:Run -p:AndroidEnableProfiler=true

Use -r android-x64 for emulators instead.

The app will start and dotnet-trace will display a MB number counting up
Use the app, once done, stop dotnet-trace by pressing Enter or Ctrl+C
Open a browser at https://speedscope.app and drop the *.speedscope.json file in it

Analyzing the trace data

This section provides insights into what to look for when analyzing flame charts.

When building without AOT, a lot of the startup traces will show time spent in System.Private.CoreLib!System.Runtime.CompilerServices.RuntimeHelpers.CompileMethod(object), indicating that that the JIT is doing a lot of work. This can make performance improvements harder to find.
When building with AOT, most of the IL is compiled to native code with some exceptions. You may still find RuntimeHelpers.CompileMethod invocations. In such cases, you may need to find what is causing the AOT compiler to skip IL portions. If the JIT still impacts cold paths of your application, you may still need to adjust your code to avoid the JIT. For instance, some generics constructs force the AOT compiler to still use JITing. In other cases, it could be accessing static-type members. The JIT conditions are runtime version dependent, and looking at the runtime code can help to find out which ones.
Some of the time is spent in the .NET Android binding framework (e.g. Android.Runtime.JNIEnv or Java.Interop.TypeManager), operations that cannot be adjusted by the application. One change to consider is to reduce the native code invocations to a strict minimum, where impactful.

Analyzing GC memory dumps

You can analyze the GC memory .gcdump files using the Visual Studio 2022/2026 memory profiler by using the File / Open menu and navigating the results.

Profiling Skia Desktop applications

Profiling Skia-based Uno Platform targets can be done on Windows in Visual Studio 2019 and 2022 using time and memory profilers.

Profiling WebAssembly applications with runtime diagnostics

As of Dotnet 10.0, runtime diagnostics like performance traces and GC dumps can be collected by calling some Javascript methods exposed by the Dotnet runtime. For more details, see the dotnet 10.0 release notes

Profiling WebAssembly applications with the browser's DevTools

Profiling WebAssembly applications can be done through the use of AOT compilation, and browsers' performance tab.

Setup the WebAssembly application for profiling

Enable emcc profiling:

xml

<PropertyGroup>
    <WasmShellEnableEmccProfiling>true</WasmShellEnableEmccProfiling>
</PropertyGroup>

Enable AOT compilation:

xml

<PropertyGroup>
    <WasmShellMonoRuntimeExecutionMode>InterpreterAndAOT</WasmShellMonoRuntimeExecutionMode>
</PropertyGroup>

Build and deploy the application
Open the Performance tab in your browser
Use your application or restart your application while recording the trace

Troubleshooting

Deep traces found in large async code patterns or complex UI trees may hit this chromium issue. This generally makes traces very long to load; you'll need to be patient.