This is a Godot project that stores and runs a collection of benchmarks. It is used to test performance of different areas of Godot such as rendering and scripting.

Interested in adding new benchmarks? See CONTRIBUTING.md.

Open the project in the editor, then run it from the editor or from an export template binary. Select benchmarks you want to run, then click the Run button in the bottom-right corner.

Once benchmarks are run, you can copy the results JSON using the Copy JSON to Clipboard button at the bottom. The results JSON is also printed to standard output, which you can see if you're running the project from a terminal.

After opening the project in the editor (required so that resources can be imported), you can run benchmarks from an editor or export template binary. The project will automatically quit after running benchmarks.

The results JSON is printed to standard output once all benchmarks are run. You can save the results JSON to a file using --save-json="path/to/file.json" (the target folder must exist).

Note

To import the project in the editor from the command line, use godot --editor --quit. If this doesn't work, use timeout 30 godot --editor.

Note

godot is assumed to be in your PATH environment variable here. If this is not the case, replace godot with the absolute path to your Godot editor or export template binary.

# The first `--` is important.
# Otherwise, Godot won't pass the CLI arguments to the project.
godot -- --run-benchmarks

--json-results-prefix=<string> can be used to nest individual results within a dictionary that has the name <string>. This can be used for easier merging of separate result runs with jq.

The --include-benchmarks CLI argument can be used to specify the name. The project will print a message to acknowledge that your argument was taken into account for filtering benchmarks.

Benchmark names all follow category/subcategory/some_name naming, with category/subcategory being the name all path components (folders) and some_name being the name of the benchmark's scene file without the .tscn extension.

godot -- --run-benchmarks --include-benchmarks="rendering/culling/basic_cull"

Use glob syntax (with * acting as a wildcard) to run a category of benchmarks:

--include-benchmarks="rendering/culling/basic_cull"

You can exclude specific benchmarks using the --exclude-benchmarks command line argument. This argument also supports globbing and can be used at the same time as --include-benchmarks.

For each benchmark, the project will track how long the main thread spent setting up the scene, then run the scene for five seconds and log the average per-frame statistics. (All times given are in milliseconds. Lower values are better.)

• Render CPU: Average CPU time spent rendering each frame (such as setting up draw calls). This metric does not take process/physics process functions into account.
• Render GPU: Average GPU time spent per frame.
• Idle: Average CPU time spent in C++ and GDScript process functions per second.
• Physics: Average CPU time spent in C++ and GDScript physics process functions per second.
• Main Thread Time: Time spent setting up the scene on the main thread. For rendering benchmarks, this acts as a loading time measurement.

Note that not all benchmarks involve running a scene (for example, GDScript benchmarks). In those cases, per-frame statistics will not be recorded, and Main Thread Time will reflect the runtime of the entire benchmark.

jq is a command line tool that greatly simplifies the task of processing JSON files. You can use the following command as a starting point for creating benchmark comparisons:

jq -n --tab '
	[inputs.benchmarks] | transpose[] | select(all(.results != {})) |
	.[0].results = (
		[[.[].results | to_entries] | transpose[] | select(all(.value != 0)) |
		{key: .[0].key, value: {
			a: .[0].value,
			b: .[1].value,
			a_div_b: (.[0].value / .[1].value)
		}}] | from_entries
	) | .[0]
' a.json b.json

Sample output (truncated to a single benchmark for brevity):

{
	"category": "Rendering > Lights And Meshes",
	"name": "Sphere 1000",
	"results": {
		"render_cpu": {
			"a": 3.952,
			"b": 4.031,
			"a_div_b": 0.9804018853882412
		},
		"render_gpu": {
			"a": 45.36,
			"b": 45.44,
			"a_div_b": 0.9982394366197184
		},
		"time": {
			"a": 38.95,
			"b": 35.04,
			"a_div_b": 1.1115867579908676
		}
	}
}