High-Performance Physics Engine

Physics_Engine

Introduction

Physics_Engine is a high-performance physics engine developed in C++ with OpenGL during my third year at Gaming Campus (GTech, Tech – Engine specialization), in a team of three developers.

The objective of this project was particularly ambitious:

  • Build a realistic physics simulation
  • Handle a very large number of dynamic entities
  • Deliver a smooth and stable experience
  • All within a strict one-week timeframe

To validate the engine, we chose to simulate a ball pit scenario, pushing the system to handle thousands of interacting objects in real time.

Project Context & Constraints

At the beginning of the project, we faced several major constraints:

  • A very short deadline (1 week)
  • The need for real-time performance
  • A requirement for physically coherent interactions
  • A team-based workflow with fast iteration cycles

Our main goal quickly became clear:

Maximize the number of simulated entities while maintaining stable performance.

This led us to prioritize:

  • Performance over feature completeness
  • Efficient algorithms
  • GPU acceleration wherever possible

Rendering & Simulation Setup

We used OpenGL for rendering due to its:

  • Simplicity of integration
  • Low-level control
  • Good compatibility with GPU-based workflows

The simulation consisted of:

  • Thousands of spherical rigid bodies
  • Continuous gravity and collision interactions
  • A confined environment mimicking a ball pool

Ball Pit

Performance Results

Thanks to our optimizations, we achieved:

  • ~5000 simulated balls at ~30 FPS
  • ~2000 balls at a stable 120 FPS (on my machine)

These results demonstrated:

  • The scalability of our architecture
  • The effectiveness of our GPU-based approach

Ball Pit

Spatial Partitioning

One of the first major optimizations we implemented was spatial partitioning.

Instead of checking collisions between every pair of objects (which would be extremely costly), we:

  • Divided space into logical regions
  • Assigned entities to these regions
  • Only tested collisions between nearby objects

This preprocessing step:

  • Reduced the number of collision checks dramatically
  • Improved overall performance significantly
  • Made large-scale simulation feasible

Partitioning

GPU-Based Physics & Collision

The most impactful optimization was moving both:

  • Collision detection
  • Physics resolution

…entirely onto the GPU.

By doing so, we:

  • Leveraged massive parallelism
  • Avoided CPU bottlenecks
  • Drastically increased the number of manageable entities

This approach required:

  • Careful data structuring
  • Efficient buffer transfers
  • A strong understanding of parallel computation constraints

It was a key factor in reaching our performance targets.

Debug & Visualization Tools

To better understand and debug the simulation, we implemented several visual debugging tools:

  • Wireframe rendering for collision volumes
  • Visualization of spatial partitioning zones
  • Real-time feedback on entity interactions

These tools were essential to:

  • Validate collision correctness
  • Tune performance
  • Identify edge cases and instability

Debug

Additional Feature: Water Simulation

At the end of the project, we had enough time to experiment with an additional system: a simple water simulation.

We implemented:

  • A flat water surface
  • Dynamic interactions such as:
    • Raindrop impacts
    • Wave propagation
    • Interference between waves

The simulation was also handled on the GPU, allowing:

  • Real-time performance
  • Smooth and reactive behavior

Notably, we reproduced effects such as:

  • The delayed upward splash after a droplet impact
  • The interaction between waves that combine and cancel each other

Rain Drop

Technical Challenges

Extreme Time Constraint

Building a physics engine in one week required:

  • Fast decision-making
  • Clear task distribution
  • Efficient iteration

Large-Scale Collision Handling

Handling thousands of objects meant:

  • Avoiding O(n²) collision checks
  • Designing scalable systems from the start

GPU Transition

Moving physics to the GPU introduced challenges such as:

  • Data synchronization
  • Debugging complexity
  • Adapting algorithms to parallel execution models

Conclusion

Physics_Engine was an intense and highly rewarding project focused on performance-oriented system design.

In just one week, we managed to:

  • Build a scalable physics engine
  • Simulate thousands of interacting entities
  • Leverage the GPU for both physics and rendering
  • Deliver a stable and visually convincing simulation

This project strengthened my skills in:

  • Optimization techniques
  • Parallel programming
  • Physics simulation fundamentals
  • Real-time system design under constraints

It also reinforced an important lesson:

Smart architecture and the right optimizations matter more than raw complexity when working under tight constraints.