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game-perf-profiling

Profiles game builds against frame-time, memory, GPU draw-call, and GC-spike budgets using Unity Profiler + Profile Analyzer + Performance Testing package and Unreal Insights + stat commands. Establishes pass/fail thresholds (16.6 ms at 60 fps, 33.3 ms at 30 fps), writes automated performance regression tests that run in CI, and emits a structured budget report per SKU. Use when a title must hit a declared frame-time or memory budget before a milestone gate or platform-cert submission, or when a recent change needs a performance regression check.

game-perf-profiling

Overview

Game performance QA verifies that a build meets its declared budgets across every target SKU before milestone sign-off or platform-cert submission. The performance category (category 4 in the game-test-categories-reference) covers frame-time, memory, GPU, thermal, and battery axes. This skill covers the two dominant engine stacks: Unity and Unreal Engine.

Frame-time budget anchors (from game-test-categories-reference):

Target frame rateFrame-time budget
60 fps16.67 ms per frame
30 fps33.33 ms per frame

Measuring averages is an anti-pattern: spikes hide in averages and cause cert failures. Measure p99 (99th-percentile frame time) and sustained-window maximums.

Step 1: Unity - Capture with the Profiler

The Unity Profiler is a performance analysis tool that collects CPU, GPU, and memory data either in the Editor or from a connected target device (docs.unity3d.com/Manual/Profiler.html).

Open it via Window > Analysis > Profiler (or Ctrl+7). Key modules to enable for a frame-time pass:

  • CPU Usage - game thread, render thread, and job-system costs per frame.
  • GPU Usage - available on Windows DirectX 11/12 and Linux OpenGL; breaks down time into Opaque, Transparent, Shadows/Depth, Deferred, and PostProcess passes with DrawCalls counts and GPU ms per entry (docs.unity3d.com/Manual/ProfilerGPU.html). Note: GPU profiling is unavailable when Graphics Jobs are enabled in Player Settings; disable them before a profiling session (docs.unity3d.com/Manual/ProfilerGPU.html).
  • Memory - tracks managed heap, GC allocations per frame in bytes, and reserved vs. in-use breakdowns for textures, meshes, materials, and animation clips (docs.unity3d.com/Manual/ProfilerMemory.html).

For standalone builds, use Deep Profiling or custom ProfilerMarker instrumentation to capture application-specific events without the full overhead of deep profiling (docs.unity3d.com/Manual/Profiler.html).

Save the capture as a .data file (Profiler toolbar > Save). Retain this alongside any Profile Analyzer .pdata export: the .pdata file does not embed the original profile frames (docs.unity3d.com/Packages/com.unity.performance.profile-analyzer@1.2/manual/index.html).

Step 2: Unity - Analyze with Profile Analyzer

Profile Analyzer (package com.unity.performance.profile-analyzer, Unity 2020.3+, install via Package Manager) aggregates and visualizes frame and marker data from a set of Profiler frames, enabling side-by-side comparison of two captures that the standard Profiler does not support (docs.unity3d.com/Packages/com.unity.performance.profile-analyzer@1.2/manual/index.html).

Open it via Window > Analysis > Profile Analyzer.

Workflow for a regression check:

  1. Load the baseline .data file as the left dataset.
  2. Load the candidate .data file as the right dataset.
  3. Compare median and p99 frame times per marker against the budget.
  4. Flag any marker whose p99 exceeds the per-frame budget allocation.

The Analyzer attempts to navigate to matching markers in the Profiler when you click a marker entry; you must make a selection in the Profiler beforehand for navigation to work (docs.unity3d.com/Packages/com.unity.performance.profile-analyzer@1.2/manual/index.html).

Step 3: Unity - Automated regression with Performance Testing package

Add "com.unity.test-framework.performance": "3.0.3" to Packages/manifest.json and reference Unity.PerformanceTesting in your assembly definition (docs.unity3d.com/Packages/com.unity.test-framework.performance@3.0/manual/index.html).

Measure.Method (Edit Mode or Play Mode)

[Test, Performance]
public void PathfindingCost_UnderBudget()
{
    Measure.Method(() => pathfinder.FindPath(start, goal))
        .WarmupCount(5)
        .MeasurementCount(20)
        .IterationsPerMeasurement(10)
        .Run();
}
  • WarmupCount(n): executes the method n times before recording to remove initialization overhead.
  • MeasurementCount(n): number of samples recorded; default is 7; 20+ improves stability.
  • IterationsPerMeasurement(n): repeats the code within each measurement to extend execution time above the 1 ms sensitivity floor.

(docs.unity3d.com/Packages/com.unity.test-framework.performance@3.0/manual/index.html)

Measure.Frames (Play Mode)

[UnityTest, Performance]
public IEnumerator CombatScene_FrameTime_UnderBudget()
{
    yield return Measure.Frames()
        .WarmupCount(5)
        .MeasurementCount(60)
        .Run();
}

Target standard deviation below 5%; avoid measurements under 1 ms due to environmental sensitivity (docs.unity3d.com/Packages/com.unity.test-framework.performance@3.0/manual/index.html).

Decorate tests [Test, Performance] for Edit Mode or [UnityTest, Performance] for Play Mode coroutines. View results via Window > Analysis > Performance Test Report.

GC-spike detection

Enable the ProfilerMarkers method on Measure.Method to target GC.Alloc markers specifically:

Measure.Method(() => SpawnWave())
    .ProfilerMarkers("GC.Alloc")
    .MeasurementCount(20)
    .Run();

A passing frame should produce 0 bytes of GC allocation in hot gameplay paths. Any non-zero sample is a regression candidate.

Disable VSync in Project Settings and remove cameras not needed for the measurement to keep results consistent between runs (docs.unity3d.com/Packages/com.unity.test-framework.performance@3.0/manual/index.html).

Step 4: Unreal - Stat commands for first-pass triage

Unreal Engine stat commands are entered into the PIE console while the game runs (dev.epicgames.com/documentation/en-us/unreal-engine/stat-commands-in-unreal-engine):

CommandShows
stat fpsFrames per second counter
stat unitFrame, Game thread, Draw (render thread), GPU, RHIT, and DynRes times; recommended starting point
stat gpuGPU statistics for the frame
stat scenerenderingGeneral rendering statistics; entry point for rendering bottleneck triage
stat gameHow long the various gameplay ticks are taking
stat memoryMemory usage by subsystem

stat unit is the first command to run on any new build: it identifies whether the bottleneck is game-thread, render-thread, or GPU, and measures how long the video card takes to render the scene (dev.epicgames.com/documentation/en-us/unreal-engine/stat-commands-in-unreal-engine). Run in a non-debug build for accurate results.

Step 5: Unreal - Deep analysis with Unreal Insights

Unreal Insights is a telemetry capture and analysis suite that captures events from a project at high data rates (dev.epicgames.com/documentation/en-us/unreal-engine/unreal-insights-in-unreal-engine).

Launch from the Editor via the Trace/Insights Status Bar Widget in the bottom toolbar, or from the prebuilt binary at Engine\Binaries\[Platform]\UnrealInsights.exe (dev.epicgames.com/documentation/en-us/unreal-engine/unreal-insights-in-unreal-engine).

Key trace channels:

ChannelCaptures
CPUThread-level timing per task and function
GPUPer-frame GPU timing
MemoryAllocation, reallocation, and deallocation events
NetworkingNetwork traffic for multiplayer titles
SlateUMG/Slate widget update costs
Asset loadingAsset load time per type

Live sessions appear in the Session Browser with a "LIVE" status indicator; Insights supports simultaneous connection to multiple sessions and records streams automatically for later replay (dev.epicgames.com/documentation/en-us/unreal-engine/unreal-insights-in-unreal-engine).

Primary views:

  • Timing Insights: CPU and GPU performance tracks with frames, filters, timers, counters, and caller/callee information.
  • Memory Insights: Reconstructs runtime memory usage patterns from traced allocation events.

(dev.epicgames.com/documentation/en-us/unreal-engine/unreal-insights-in-unreal-engine)

Step 6: GPU draw-call and overdraw budgets

Unity

The GPU Usage module's Hierarchy view shows DrawCalls count and GPU ms per rendering pass (docs.unity3d.com/Manual/ProfilerGPU.html). Typical mobile budget: under 100 draw calls per frame; PC/console budget is title-specific but PostProcess and Transparent passes are common over-budget culprits.

Overdraw (multiple pixels written per screen pixel per frame) is visible when Transparent pass GPU ms is disproportionate to scene complexity. Reduce by: lowering particle counts, using depth pre-pass, culling off-screen transparency.

Unreal

Run stat scenerendering to surface general rendering statistics as the entry point for rendering bottleneck identification (dev.epicgames.com/documentation/en-us/unreal-engine/stat-commands-in-unreal-engine). Follow with stat gpu to get per-pass GPU time, then use the GPU Visualizer (ProfileGPU console command) for a hierarchical breakdown of GPU passes to isolate overdraw-heavy translucent passes (dev.epicgames.com/documentation/en-us/unreal-engine/gpu-profiling-in-unreal-engine).

Step 7: CI regression gate

Unity

Run Performance Testing package tests in a headless Unity batch session:

unity -batchmode -runTests -testPlatform StandaloneWindows64 \
  -testResults results.xml -projectPath .

Parse results.xml; fail the build if any PerformanceMeasurement sample set has a median or p99 exceeding the declared threshold. A zero-tolerance GC.Alloc assertion on hot paths is a recommended gate: one stray allocation per frame compounds to hundreds of KB/s under sustained play.

For stable CI numbers: disable VSync, remove unused cameras, set a fixed Quality level, and disable hardware reporting in Player Settings (docs.unity3d.com/Packages/com.unity.test-framework.performance@3.0/manual/index.html).

Unreal

Unreal Automation System (see unreal-automation-system) exposes a PerformanceCapture test type. Combine with a CI step that launches Insights in server mode, runs the target map for N frames, exports the trace, and compares the exported GPU/CPU frame time histogram against stored baselines.

Budget report template

Emit one row per profiled scenario per SKU:

ScenarioSKUMetricBudgetMeasured (p50)Measured (p99)Status
Combat encounterPC HighFrame time16.67 ms11.2 ms18.4 msFAIL p99
Combat encounterPC HighGC alloc/frame0 B0 B128 BFAIL p99
Open-world traversalPC HighFrame time16.67 ms13.1 ms15.9 msPASS

A FAIL on p99 triggers a regression investigation before milestone sign-off.

Anti-patterns

Anti-patternWhy it failsFix
Averaging frame time across a levelSpikes hide in averages and cause cert failuresUse p99 and sustained-window maximums
Profiling with Graphics Jobs enabled in UnityGPU module is disabled; no GPU data collectedDisable Graphics Jobs before capture
GC alloc tolerance in hot pathsCompounds to MB/s under sustained playAssert 0 B per frame in CI on hot paths
Running perf tests in debug builds (Unreal)Inaccurate results; use non-debug buildsProfile in Development or Shipping builds
Single-device perf sign-offLow-end SKU (Series S, Switch handheld) is the binding constraintRun budget checks on every SKU in the compatibility matrix

Limitations