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Runtime Insights

Runtime Insights

Section titled “Runtime Insights”

Module: Tech · Category: Performance · Tool ID: RuntimeInsightsSuite

Four-tab end-to-end runtime profiling pipeline. Profiler monitors stat unit live with CPU vs GPU bottleneck classification. Trace Analyzer parses .utrace files with per-thread event breakdown. Benchmarks runs spline-based automated benchmarks with CSV/JSON export for CI/CD regression tracking. AI Analysis does LLM-powered trace interpretation (Coming Soon).

Screenshot 01 — Hero shot — Runtime Insights on Profiler tab with live frame metrics, CPU/GPU bottleneck classification visible.

When to use it

Section titled “When to use it”
  • Runtime performance investigation during PIE / packaged builds
  • Trace file analysis — opening .utrace files captured during play
  • CI/CD performance regression tracking — automated benchmark + JSON export
  • Bottleneck classification — “is this CPU-bound or GPU-bound?”

When not to use it

Section titled “When not to use it”
  • Don’t expect asset-side analysis — runtime profiling only
  • Don’t expect this to replace Unreal Insights — complementary; UE Insights handles deep traces

Opening the tool

Section titled “Opening the tool”
  1. Open the EQLabs Hub and search for Runtime Insights (or browse to Tech → Performance)
  2. Click the tool card
  3. Use the tab bar: Profiler / Trace Analyzer / Benchmarks / AI Analysis

Tab 1 — Profiler

Section titled “Tab 1 — Profiler”

Live frame monitoring with stat unit parsing + bottleneck classification (CPU-bound / GPU-bound / Sync-limited).

Tab 2 — Trace Analyzer

Section titled “Tab 2 — Trace Analyzer”

Parse .utrace files. Per-thread event breakdown, hot-function detection.

Tab 3 — Benchmarks

Section titled “Tab 3 — Benchmarks”

Automated benchmark runner:

  • Spline-based camera path
  • Capture frame-time per sample point
  • CSV/JSON export
  • Suitable for CI/CD perf regression detection

Tab 4 — AI Analysis

Section titled “Tab 4 — AI Analysis”

Coming Soon — LLM-powered trace interpretation. Feed a trace, get plain-English diagnosis.

Screenshot 02 — Benchmark run — Benchmark tab with spline path defined, frame-time graph visible after a run.

Workflow

Section titled “Workflow”

Live profile

Section titled “Live profile”
  1. Switch to Profiler
  2. Start PIE
  3. Watch frame metrics + bottleneck classification

Trace analysis

Section titled “Trace analysis”
  1. Capture a trace (UE’s Insights or via console trace.start)
  2. Switch to Trace Analyzer
  3. Open the .utrace file
  4. Walk per-thread events

CI benchmark

Section titled “CI benchmark”
  1. Set up a spline path through the level
  2. Switch to Benchmarks
  3. Run — captures frame-times along the path
  4. Export JSON; plug into your CI’s regression-detection pipeline

Settings reference

Section titled “Settings reference”

Per-tab settings persist via standard tool framework.

Output

Section titled “Output”
  • Live in-panel metrics — Profiler tab during PIE
  • Trace analysis — read-only display from .utrace
  • Benchmark CSV / JSON — for CI/CD pipeline ingestion

Tips & gotchas

Section titled “Tips & gotchas”
  • Profiler is a stat unit consumer — same data UE shows in console, presented with bottleneck classification
  • Benchmark is reproducible — same spline path = same benchmark, useful for regression detection
  • AI Analysis is Coming Soon — V1 leaves the tab as a placeholder with a “coming soon” banner
Section titled “Related tools”
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