Project 1: The MonoGame Core Loop

A deterministic update and render loop with frame pacing telemetry and live debug overlay.

Quick Reference

Attribute Value
Difficulty Level 2
Time Estimate Weekend
Main Programming Language C# (.NET 8) + MonoGame
Alternative Programming Languages F#, C++ (raylib), Godot C#
Coolness Level Level 3
Business Potential Level 1
Prerequisites Deterministic loop basics, debugging discipline, content pipeline fundamentals
Key Topics Frame pacing invariants, Input sampling windows, Deterministic state updates

1. Learning Objectives

  1. Translate one concrete production question into a testable implementation plan.
  2. Implement and validate the feature in a MonoGame runtime context.
  3. Instrument success and failure paths with actionable diagnostics.
  4. Produce a repeatable demo artifact for portfolio or interview use.

2. All Theory Needed (Per-Concept Breakdown)

Frame pacing invariants

Fundamentals Frame pacing invariants is central to this project because it defines the non-negotiable behavioral contract for the feature. You should be able to describe valid inputs, legal state transitions, and expected outputs under normal and failure conditions.

Deep Dive into the concept Treat Frame pacing invariants as a boundary-setting mechanism. Start by defining the smallest deterministic scenario that proves the feature works. Stress that scenario under altered timing, altered content inputs, and altered user actions. If behavior changes unexpectedly, document hidden coupling and sequence assumptions. Keep transitions explicit and observable via logs or debug panels. Connect each transition to an event record so regression analysis is possible after refactors.

Input sampling windows

Fundamentals Input sampling windows ensures the project scales from local prototype behavior to repeatable system behavior.

Deep Dive into the concept Use Input sampling windows to reason about data flow ownership and mutation timing. Document where writes occur, when validation runs, and how rollback behaves if a write fails.

Deterministic state updates

Fundamentals Deterministic state updates connects this project to shipping reality by forcing you to think about operational constraints early.

Deep Dive into the concept Define one production-like failure mode related to Deterministic state updates and build a mitigation checklist. The solution is complete when you can demonstrate both a golden path and a controlled failure path.

3. Project Specification

3.1 What You Will Build

A MonoGame runtime scene that decouples fixed simulation updates from variable rendering, with live telemetry that proves deterministic timing under changing render load.

Visible game deliverable:

  • Top-left HUD shows FPS, UPS, frame-time graph, backlog counter
  • Center test sprite moves in deterministic path regardless of render FPS
  • Bottom status line shows VSync state and update-step cap

3.2 Functional Requirements

  1. Render and update loops are instrumented separately with per-frame metrics.
  2. Simulation runs at fixed tick (60 Hz) and clamps catch-up steps.
  3. Input sampling happens before each simulation step, not during draw.
  4. A stress toggle validates stability under GPU/CPU load swings.

3.3 Non-Functional Requirements

  • Performance: Must remain inside project-appropriate frame budget.
  • Reliability: Must recover from at least one injected failure mode.
  • Usability: Outcome must be observable by a reviewer in under two minutes.

3.4 Example Usage / Output

[HUD] fps=142 ups=60 backlog=0
[HUD] fps=58 ups=60 backlog=0
[CHECK] deterministic_path hash=8fd1a2 PASS

3.5 Data Formats / Schemas / Protocols

  • Event record: {timestamp, module, action, result}
  • Feature state snapshot: {version, state, counters, flags}

3.6 Edge Cases

  • Window minimize/restore spikes elapsed time.
  • VSync state changes mid-session.
  • Frame limiter disabled on high-refresh monitors.

3.7 Real World Outcome

This is a game-facing outcome you can see and play immediately.

What you will see in the game window:

  • Top-left HUD shows FPS, UPS, frame-time graph, backlog counter
  • Center test sprite moves in deterministic path regardless of render FPS
  • Bottom status line shows VSync state and update-step cap

Project 1 The MonoGame Core Loop Window Mockup

How you interact:

  • F1 toggles debug HUD
  • V toggles VSync
  • R toggles render stress mode

3.7.1 How to Run (Copy/Paste)

$ dotnet restore
$ dotnet build
$ dotnet run --project src/Game -- --scene core-loop

3.7.2 Golden Path Demo (Deterministic)

  1. Start the scene and confirm all HUD panels load.
  2. Perform the three core interactions listed above.
  3. Verify the success signal appears without warnings.

3.7.3 If CLI: exact transcript

$ dotnet run --project src/Game -- --scene core-loop
[HUD] fps=142 ups=60 backlog=0
[HUD] fps=58 ups=60 backlog=0
[CHECK] deterministic_path hash=8fd1a2 PASS

3.7.7 If GUI / Desktop

+------------------------------------------------------+
| core-loop                                   [F1 HUD] |
|------------------------------------------------------|
| PLAYFIELD: gameplay objects and interactions         |
| HUD: key metrics + status badges                    |
| STATUS: success/failure cues and prompts            |
+------------------------------------------------------+

4. Solution Architecture

4.1 High-Level Design

Input Sampler -> Fixed-Step Accumulator -> Simulation Tick -> Render Pass -> Telemetry HUD

Input Sampler -> Fixed-Step Accumulator -> Simulation Tick -> Render Pass -> Telemetry HUD

4.2 Key Components

Component Responsibility Key Decisions
LoopClock Tracks wall clock, accumulator, and fixed-step budget Clamp catch-up to avoid spiral of death
SimulationStage Executes deterministic world updates No gameplay decisions in Draw
TelemetryOverlay Renders timing metrics and warnings Always available in debug builds

4.4 Algorithm Overview

  1. Validate preconditions.
  2. Apply deterministic transition.
  3. Emit feedback and telemetry.
  4. Persist if required.

5. Implementation Guide

5.3 The Core Question You’re Answering

“How do you keep simulation deterministic when rendering speed changes?”

5.4 Concepts You Must Understand First

  1. Frame pacing invariants
  2. Input sampling windows
  3. Deterministic state updates

5.5 Questions to Guide Your Design

  1. How will you prove update determinism when render FPS changes drastically?
  2. What threshold triggers a backlog warning and how will you surface it?
  3. Which metrics are mandatory for diagnosing frame pacing regressions?

5.6 Thinking Exercise

Trace one full success path and one failure path on paper before implementation.

5.7 The Interview Questions They’ll Ask

  1. Why did you pick this architecture boundary?
  2. Which failure mode did you prioritize first and why?
  3. How does your instrumentation accelerate debugging?
  4. How would you scale this feature to a larger game?

5.8 Hints in Layers

  • Hint 1: Stabilize one invariant before feature expansion.
  • Hint 2: Add diagnostics before optimization.
  • Hint 3: Keep platform calls at system boundaries.
  • Hint 4: Re-run deterministic scenario after each refactor.

5.9 Books That Will Help

Topic Book Chapter
Core concept “Game Programming Patterns by Robert Nystrom” Relevant concept chapter
Reliability “Release It!” Failure handling chapters
Architecture “Clean Architecture” Boundary and dependency chapters

6. Testing Strategy

  1. Golden path completes and emits success signal.
  2. Injected failure path recovers without crash.
  3. Re-run scenario after restart and confirm consistency.

7. Common Pitfalls & Debugging

  • Hidden initialization order coupling
  • Time-coupled behavior tied to render rate
  • Missing fallback behavior on platform call failure

8. Extensions & Challenges

  • Beginner: add one extra diagnostics panel metric.
  • Intermediate: add replay capture for event flow.
  • Advanced: add automated stress test harness.

9. Real-World Connections

This project mirrors shipping feature-module work in real indie and mid-size game teams.

10. Resources

  • Steamworks official docs
  • MonoGame docs
  • Gemini image generation docs (for asset-related projects)

11. Self-Assessment Checklist

  • I can explain the feature invariant and prove it in a demo.
  • I can trigger and handle one deterministic failure scenario.
  • I can describe tradeoffs and future scaling choices.

12. Submission / Completion Criteria

Minimum Viable Completion:

  • Feature works in deterministic golden path.
  • One controlled failure path is handled gracefully.
  • Core diagnostics are visible and documented.

Full Completion:

  • All minimum criteria plus edge-case coverage and regression checks.

Excellence:

  • Includes polished instrumentation and clear productionization notes.