Optimizing Level Generation: Keep Your Unity Games Responsive

Reducing Load Times with Efficient Generation

When creating levels procedurally through code, it is important to optimize the generation algorithms to reduce load times. Complex algorithms that iterate through many steps to create detailed levels can lead to delays when loading scenes. In contrast, efficient algorithms that focus only on necessary content run faster.

For example, a maze generation algorithm might walk through a grid, carving out random paths to create a complex labyrinth. This can be optimized by using a simplified topological approach that builds rooms and corridors to connect them without all the cell-by-cell logic. By streamlining the generation logic, mazes can be created faster at runtime without compromising quality.

Streamlining Runtime Performance

It’s not just load times that matter when optimizing generated levels – the gameplay experience needs to be smooth and responsive as well. There are a few key techniques that can optimize performance during gameplay.

Firstly, manage memory allocations by reusing game objects via object pooling instead of destroying and instantiating new ones. By maintaining a pool of enemy objects, they can be turned inactive when not needed and reactivated later to appear in different parts of the level.

For example, an enemy pool would allocate a fixed number of enemy objects at the start. Enemies would then be activated from this pool as they enter the player’s area and returned to the pool when moving off-screen.

Asynchronous Loading and Unloading

As players progress through generated levels, new scenes need to be loaded containing upcoming areas. Use asynchronous operations to load in the background, avoiding pauses and interruptions.

Plan out a loading sequence that begins loading the next scene when the player approaches transition points. This can happen discretely over a period of time instead of all at once when crossing between scenes. Any unused assets from previous scenes should be unloaded as well so that new content can utilize that memory.

For example, upon approaching the exit to a level, the next scene containing the upcoming level geometry and enemies would begin loading in increments. Any enemies, textures, or geometry no longer needed from the current scene are then unloaded. By the time the player finishes the level, the next one will be loaded and ready for a smooth transition.

Optimizing Draw Calls and Graphics

Draw calls that render level geometry, characters, and environmental objects can quickly add up, especially for large procedurally generated environments. Minimize draw calls by reducing the number of meshes needed to represent the level.

For example, utilizing occlusion culling will prevent invisible objects behind walls and terrain from being rendered unnecessarily. Combining all wall sections and floors into larger meshes reduces the number of separate draw calls as well. Lastly, draw call batching can be used to issue one call for large groups of objects using the same texture atlas or material.

As an example, a maze scene could combine all wall segments into a single mesh and use draw call batching for enemies, props, and floor tiles. Occlusion culling would disable rendering elements not visible to the camera. This greatly reduces graphics workload compared to individually drawing each element.

Testing Level Generation on Target Devices

After applying optimizations, test directly on the target platform – whether mobile, console, or desktop. Use profiling tools to analyze runtime performance, identify any bottlenecks in the generation algorithms or gameplay logic.

Compare the build size and boot times to previous versions to quantify optimizations. Test on minimum specification devices that represent the lower end target hardware to ensure responsiveness.

For example, a maze game could be tested on a low-end Android phone to validate boot time and gameplay frame rates. Any frame rate drops or pauses might indicate further optimizations needed in either content or code to improve the experience.

Conclusion

Optimizing procedurally generated levels requires analyzing both load time and runtime performance. Simplify generation logic, utilize object pooling, implement asynchronous loading, minimize draw calls, and test extensively on target platforms.

Keeping an eye on optimization from the beginning of development, regularly testing on real hardware, and targeting the minimum viable platform ensures a responsive experience even for procedurally dense and complex games.

For more on optimizing procedural games, see the following resources:

• Procedural Generation in Game Design by Tanya X. Short et al.
• “Runtime Generation of Complex Levels for Puzzle Games” byfaced Ken Perlin
• “Optimization Strategies for Procedural Content Generation” by Stefan Van Der Spek