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# Objective - Document how to do profiling with Tracy # Solution - The documentation of setting `RUST_LOG=info` in order to capture `wgpu` spans depends on https://github.com/bevyengine/bevy/pull/5182
97 lines
6.4 KiB
Markdown
97 lines
6.4 KiB
Markdown
# Profiling
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## Runtime Flame Graph: `tracing` spans
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Bevy has built-in [tracing](https://github.com/tokio-rs/tracing) spans to make it cheap and easy to profile Bevy ECS systems, render logic, engine iternals, and user app code. Enable the `trace` cargo feature to enable Bevy's built-in spans.
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If you also want to include `wgpu` tracing spans when profiling, they are emitted at the `tracing` `info` level so you will need to make sure they are not filtered out by the `LogSettings` resource's `filter` member which defaults to `wgpu=error`. You can do this by setting the `RUST_LOG=info` environment variable when running your application.
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You also need to select a `tracing` backend using the following cargo features:
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### Backend: trace_chrome
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`cargo run --release --features bevy/trace_chrome`
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After running your app a `json` file in the "chrome tracing format" will be produced. You can open this file in your browser using <https://ui.perfetto.dev>. It will look something like this (make sure you expand `Process 1`):
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### Backend: trace_tracy
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The [Tracy profiling tool](https://github.com/wolfpld/tracy) is:
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> A real time, nanosecond resolution, remote telemetry, hybrid frame and sampling profiler for games and other applications.
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There are binaries available for Windows, and installation / build instructions for other operating systems can be found in the [Tracy documentation PDF](https://github.com/wolfpld/tracy/releases/latest/download/tracy.pdf).
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It has a command line capture tool that can record the execution of graphical applications, saving it as a profile file. Tracy has a GUI to inspect these profile files. The GUI app also supports live capture, showing you in real time the trace of your app.
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In one terminal, run:
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`./capture-release -o my_capture.tracy`
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This will sit and wait for a tracy-instrumented application to start, and when it does, it will automatically connect and start capturing. Note that on Windows, the capture tool is called `capture.exe`.
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Then run your application, enabling the `trace_tracy` feature:
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`cargo run --release --features bevy/trace_tracy`
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After running your app, you can open the captured profile file (`my_capture.tracy` in the example above) in the Tracy GUI application to see a timeline of the executed spans.
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Alternatively, directly run the tracy GUI and then run your application, for live capture. However, beware that running the live capture on the same machine will be a competing graphical application, which may impact results. Pre-recording the profile data through the CLI tool is recommended for more accurate traces.
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In any case, you'll see your trace in the GUI window:
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There is a button to display statistics of mean time per call (MTPC) for all systems:
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Or you can select an individual system and inspect its statistics (available through the "statistics" button in the top menu) to see things like the distribution of execution times in a graph, or statistical aggregates such as mean, median, standard deviation, etc. It will look something like this:
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### Adding your own spans
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Add spans to your app like this (these are in `bevy::prelude::*` and `bevy::log::*`, just like the normal logging macros).
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```rust
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{
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// creates a span and starts the timer
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let my_span = info_span!("span_name", name = "span_name").entered();
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do_something_here();
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} // my_span is dropped here ... this stops the timer
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// You can also "manually" enter the span if you need more control over when the timer starts
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// Prefer the previous, simpler syntax unless you need the extra control.
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let my_span = info_span!("span_name", name = "span_name");
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{
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// starts the span's timer
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let guard = my_span.enter();
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do_something_here();
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} // guard is dropped here ... this stops the timer
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```
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Search for `info_span!` in this repo for some real-world examples.
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For more details, check out the [tracing span docs](https://docs.rs/tracing/*/tracing/span/index.html).
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## `perf` Runtime Flame Graph
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This approach requires no extra instrumentation and shows finer-grained flame graphs of actual code call trees. This is useful when you want to identify the specific function of a "hot spot". The downside is that it has higher overhead, so your app will run slower than it normally does.
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Install [cargo-flamegraph](https://github.com/killercup/cargo-flamegraph), [enable debug symbols in your release build](https://github.com/killercup/cargo-flamegraph#improving-output-when-running-with---release), then run your app using one of the following commands. Note that `cargo-flamegraph` forwards arguments to cargo. You should treat the `cargo-flamegraph` command as a replacement for `cargo run --release`. The commands below include `--example EXAMPLE_NAME` to illustrate, but you can remove those arguments in favor of whatever you use to run your app:
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* Graph-Like Flame Graph: `RUSTFLAGS='-C force-frame-pointers=y' cargo flamegraph -c "record -g" --example EXAMPLE_NAME`
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* Flat-ish Flame Graph: `RUSTFLAGS='-C force-frame-pointers=y' cargo flamegraph --example EXAMPLE_NAME`
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After closing your app, an interactive `svg` file will be produced:
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## Project Compile Times
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Append `--timings` to your app's cargo command (ex: `cargo build --timings`).
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If you want a "full" profile, make sure you run `cargo clean` first (note: this will clear previously generated reports).
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The command will tell you where it saved the report, which will be in your target directory under `cargo-timings/`.
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The report is a `.html` file and can be opened and viewed in your browser.
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This will show how much time each crate in your app's dependency tree took to build.
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