version: "1.0.1" name: hz-perfetto-debug license: Apache-2.0 description: Analyzes Meta Quest and Horizon OS VR performance using Perfetto traces — frame timing, CPU/GPU bottlenecks, render pass analysis. Use when profiling frame drops, jank, or thermal issues on Quest devices. allowed-tools: Bash(metavr:) Bash(hzdb:) Bash(npx:*)

Perfetto Debug Skill

When to Use

Use this skill when investigating VR performance issues on Meta Quest devices:

• Frame drops, jank, or stuttering
• CPU or GPU bottlenecks
• Render pass overhead and GPU utilization
• Thermal throttling and clock frequency changes
• Frame timing variance and missed vsync deadlines
• Thread contention and synchronization issues
• High draw call counts or overdraw

VR Frame Time Targets

These are the hard deadlines for each refresh rate. If a frame exceeds its target, the compositor must reproject or the user sees a stale frame.

Missing a frame deadline by even 1 ms causes a stale frame (reprojection). Stale frames above 10% of total frames indicate a serious performance problem.

metavr Setup

Perfetto tracing is powered by the metavr CLI. Invoke via npx — no install required:

npx -y metavr --version

Examples below use the bare metavr command for brevity; if it is not installed globally, replace metavr with npx -y metavr. Connect your Quest via USB with developer mode enabled before capturing traces.

Quick Start Workflow

1. Capture a Trace

# Capture a 5-second trace from the currently running VR app
metavr perf capture
 
# Specify duration and target app
metavr perf capture --duration 10000 --app com.example.myapp
 
# Enable GPU render stage tracing for detailed pass analysis
metavr perf capture --gpu-render-stage
 
# Enable XR runtime metrics
metavr perf capture --xr-runtime
 
# Custom output name
metavr perf capture -o my-session-name

The capture auto-detects the foreground VR app if --app is not specified. CPU scheduling and GPU metrics tracing are enabled by default. The trace is pulled to your local machine automatically.

2. List Available Traces

metavr perf traces

Returns .pftrace files sorted by modification time (newest first). Searches standard directories including ~/Documents, ~/Downloads, and the current working directory.

3. Load a Trace

metavr perf load <trace-file>

Loads and processes the trace for analysis. Accepts a hex session ID, filename (with or without .pftrace extension), or a full/relative path.

4. Get Performance Overview

metavr perf context

Returns a structured performance analysis including:

• CPU and GPU frame timing statistics
• Thread breakdown with utilization percentages
• GPU counter summaries (if available)
• Detected bottlenecks and recommendations

5. Run SQL Queries

metavr perf query <session-id> "SELECT ts, dur, name FROM slice WHERE name LIKE '%PlayerLoop%' LIMIT 20"

Executes arbitrary SQL against the loaded Perfetto trace database. All Perfetto tables are available: slice, thread_track, thread, process, counter, counter_track, args, sched_slice, and more.

6. Analyze Thread States

metavr perf thread-state <session-id> <utid>
 
# With time range
metavr perf thread-state <session-id> <utid> --start-ts 1000000 --end-ts 5000000000

Returns a thread state breakdown showing how much time the thread spent running, sleeping, blocked, or waiting for CPU. Useful for identifying whether a thread is CPU-bound, I/O-bound, or starved.

7. Get GPU Metrics

metavr perf gpu-counters <session-id> --start-ts 100,200,300 --end-ts 150,250,350

Returns GPU metric counters (mean, standard deviation, quantiles) for GPU frame ranges. Requires at least 20 frames for statistical accuracy. Metrics include texture fetch rates, shader ALU capacity, vertex processing, and fragment shading statistics.

Detailed Analysis Workflow

Follow these steps in order for a thorough performance investigation.

Step 1: Validate Trace Quality

Before analyzing, confirm the trace is usable:

• Duration: At least 2 seconds of data (ideally 3-5 seconds)
• Slice count: Should have thousands of slices for a meaningful trace
• Process presence: The target app process must be present

SELECT
  (MAX(ts) - MIN(ts)) / 1e9 AS duration_seconds,
  COUNT(*) AS total_slices
FROM slice

If the trace has fewer than 1000 slices or is under 1 second, it may not contain enough data for meaningful analysis. Capture a new trace with metavr perf capture.

Step 2: Identify Target Process

Find the application process (not system services):

SELECT upid, pid, name
FROM process
WHERE name NOT LIKE 'com.oculus%'
  AND name NOT LIKE '/system%'
  AND name NOT LIKE 'com.android%'
  AND name IS NOT NULL
ORDER BY pid

For known apps, filter directly by package name.

Step 3: Identify Game Engine

Look for engine-specific markers:

Step 4: Find Key Threads

Identify the threads that matter for VR rendering:

SELECT t.utid, t.tid, t.name, p.name AS process_name
FROM thread t
JOIN process p USING(upid)
WHERE p.name = '<target-process>'
ORDER BY t.name

Critical threads to locate:

Once you have a thread's utid, use metavr perf thread-state <session-id> <utid> to get a quick breakdown of its running/sleeping/blocked time.

Step 5: Detect Frame Boundaries

Find frame start/end markers to segment per-frame analysis:

• Unity: PlayerLoop slices on the main thread define frame boundaries
• Unreal: FEngineLoop::Tick slices on the game thread
• OpenXR: xrWaitFrame to xrEndFrame sequences

Step 6: Analyze Expensive Functions

Find what consumes the most time per frame:

SELECT name, COUNT(*) AS call_count, SUM(dur)/1e6 AS total_ms, AVG(dur)/1e6 AS avg_ms
FROM slice
WHERE track_id IN (
  SELECT id FROM thread_track WHERE utid = <main_thread_utid>
)
GROUP BY name
ORDER BY total_ms DESC
LIMIT 20

Step 7: Check High-Frequency Calls

Functions called excessively per frame can indicate batching issues:

SELECT name, COUNT(*) AS calls
FROM slice
WHERE track_id IN (
  SELECT id FROM thread_track WHERE utid = <utid>
)
  AND dur < 100000
GROUP BY name
HAVING calls > 1000
ORDER BY calls DESC

Step 8: Analyze GPU Render Passes

See the GPU analysis reference for detailed render pass breakdown, surface analysis, and GPU counter interpretation.

Key Perfetto Concepts

Performance Targets

Engine-Specific Notes

Unity

• PhaseSync: VR vsync alignment mechanism. Appears as idle time at the start of PlayerLoop. This is normal and intentional — do NOT flag as wasted time.
• Single-pass multiview: Both eyes rendered in one pass. If you see two render passes per frame, the app may be using multi-pass rendering (less efficient).
• Dynamic batching: Watch for high SetPass call counts, which indicate materials are not being batched.
• IL2CPP vs Mono: IL2CPP builds have different function naming in traces. Look for mangled C++ names instead of C# method names.

Unreal Engine

• RHI Thread: Unreal uses a separate RHI (Render Hardware Interface) thread for GPU command submission. Check this thread for driver overhead.
• Forward vs Deferred: Forward rendering is preferred on Quest. Deferred rendering has significantly higher GPU cost.
• Blueprint Tick: Heavy Blueprint usage shows up as UObject::ProcessEvent. High counts indicate Blueprints should be converted to C++.
• Nativized Blueprints: Show up with __StaticExec suffix in trace names.

Common Pitfalls

• Do NOT report PhaseSync or xrWaitFrame idle time as a performance problem — these are intentional frame pacing mechanisms.
• GPU render pass names like surface#0 are not descriptive — correlate them with the resolution and MSAA level to identify what they render.
• Thread names can be truncated in traces. UnityMain may appear as UnityMai or similar.
• Always use utid (not tid) when joining thread-related tables in SQL queries.
• Timestamps are nanoseconds. A common mistake is treating them as microseconds.
• Counter values are instantaneous samples, not averages over a period.

References

For detailed guides on specific topics, see:

• Capturing Traces — How to capture Perfetto traces on Quest
• Analyzing Traces — Step-by-step trace analysis with SQL queries
• GPU Analysis — Render pass analysis, GPU counters, and metrics
• Frame Timing — VR frame markers, pacing, and compositor timing
• SQL Queries — Ready-to-use SQL queries for common analysis tasks