Best Practices For Offloading Expensive Tasks In Unity Games
Optimizing Performance of Expensive Operations
Ensuring optimal performance in Unity games often requires identifying and optimizing resource-intensive tasks that can cause bottlenecks. Common examples include physics calculations for complex simulation, pathfinding and decision making logic in AI systems, and visually complex shader programming. This article explores best practices for optimizing or offloading these expensive operations to maintain high frame rates and smooth gameplay.
Identifying Resource-Intensive Tasks
The first step is using Unity’s profiling tools to detect segments of game logic that are resource-intensive. Examples of expensive tasks include:
- Physics simulations with large numbers of colliders, joints, and rigidbodies
- AI systems with costly pathfinding checks or decision making logic
- Visually complex scenes with many vertex/fragment shader calculations per frame
- Individual gameobjects with high polygon counts
It’s important to understand the cost of these operations and how frequently they occur. Physics may run at a fixed frame rate, AI logic may be checked each frame, and visual effects can barrage the GPU with draw calls. Identifying the biggest bottlenecks provides optimization targets.
Offloading Work to Secondary Threads
Unity provides a job system that allows time-consuming operations to be moved to secondary worker threads, freeing up the main thread responsible for gameplay logic and rendering. Examples include:
- Putting pathfinding and other expensive AI reasoning into jobs
- Performing physics queries on worker threads
- Parallelizing batches of visual effect simulation logic
This allows multiple CPU cores to work simultaneously. The job system handles synchronization between threads and the main Unity dispatch loop.
Unity’s job system
Jobs should inherit from IJob and override an Execute method that runs the desired logic. The job is then scheduled by passing it to a job handle:
public class ExpensiveAIJob : IJob
{
public void Execute()
{
//Time-consuming pathfinding logic
}
}
ExpensiveAIJob job = new ExpensiveAIJob();
JobHandle handle = job.Schedule();
Jobs are ParallelFor jobs when they need to divide work across indexes, with each index running concurrently:
public class BatchEffectJob : IJobParallelFor
{
public void Execute(int index)
{
//Visual effect logic for index
}
}
Burst compilation can further speed up job performance by compiling C# jobs to highly optimized native code.
Leveraging the GPU
Modern GPUs are designed to massively parallelize visual, compute and simulation workloads across thousands of cores. Examples of leveraging GPU processing include:
- Complex fragment and vertex shader effects
- General compute operations like physics simulation
- Procedural generation and simulation of visual data
GPU processing strengths and limitations
GPUs excel at data parallel work on identical operations across vertices or pixels. However, they have limitations:
- Latency launching new GPU kernels has overhead
- Data transfer between CPU and GPU has a cost
- Less optimized for unpredictable data access and branching logic
Understanding these tradeoffs allows intelligent division of labor between CPU and GPU.
Object Pooling
Instantiating and destroying gameobjects like bullets or visual effects can be surprisingly costly. Object pooling reduces this by keeping a pool of reusable objects.
Reducing expensive instantiate/destroy calls
Instead of directly instantiating objects, a pool manager object is used:
public class PoolManager{
List bulletPool;
public GameObject GetBullet(){
if(bulletPool.Count > 0){
return bulletPool.Pop();
}
else {
return Instantiate(bulletPrefab);
}
}
public ReturnBullet(GameObject bullet){
bulletPool.Add(bullet);
}
}
The pool manager removes inactive objects from scenes but avoids destroy calls. This skips expensive creation steps.
Implementing reusable object pools
Generic pooling controllers can support different prefab types. Object pooling should be monitored to appropriately size pools and avoid memory issues.
Data-Oriented Design
How data is structured and accessed also impacts performance. Data-oriented design focuses on optimizing data layout.
Optimizing data layout and access patterns
Examples include:
- Sequentially packing transform data to use cache effectively
- Sorting objects by material to minimize costly batch breaks
- Using chunked iteration approaches over traditional object hierarchies
These optimizations aim to maximize data cache coherency and effectiveness of prefetching.
Examples in Unity
Unity’s Entity Component System (ECS) provides a data-oriented framework. ECS focuses on decoupling entity data from objects to allow more cache-friendly iterations over entities with the same components.
Asset Bundles
AssetBundles allow content to be downloaded dynamically or loaded from disk only when needed.
Streaming content from disk/network
Common uses include:
- Decompressing compressed art assets during scene loading
- Streaming new game levels without hitting application memory limits
- Lazy-loading of downloadable content
This strategy reduces initial startup burden and constraints during runtime.
Reducing application startup workload
Resource-intensive manager systems can load content asynchronously while showing splash screens. Systems that don’t need immediate availability are candidates, including complex AI behaviors.
Profiling Tools
Built-in and third-party profiling tools help identify optimization targets.
Unity profiler
The Unity profiler includes:
- CPU usage breakouts
- Memory heap allocations
- Draw call counts for identifying GPU bottlenecks
- Mono heap profiles to reduce C# garbage collection
The profiler is critical for diagnosing spikes and hiccups during gameplay.
Third-party profiling tools
Standalone tools like dotTrace and JetBrains Rider provide low-level call stack sampling and timing data for fine-grained optimization. They help drill down on C# hot paths and native plug-in code.
