Welcome to the Summer Practice 2024 project: Boids Behaviour with high optimization! This repository demonstrates how to leverage the Unity Job System to optimize your game’s performance by efficiently distributing work across multiple CPU cores. This README will guide you through the key features and benefits of using the Unity Job System.
The Unity Job System is a powerful tool for optimizing game performance by taking advantage of modern multi-core processors. This project showcases how to use the Unity Job System to implement parallel processing, leading to more responsive and scalable games.
- Parallel Processing: Break down complex tasks into smaller jobs that run in parallel across multiple CPU cores.
- Scalability: Efficiently handle large-scale computations, such as physics calculations or AI pathfinding, without bottlenecks.
- Thread-Safe Operations: Utilize the
NativeContainertypes to manage data safely across threads. - Seamless Integration: Easily integrate the Job System into existing Unity projects without major refactoring.
-
Enhanced Performance: The Job System allows you to maximize CPU usage by distributing work across multiple cores, reducing the load on the main thread and improving overall frame rates.
-
Improved Responsiveness: By offloading heavy computations to background threads, the main thread remains free to handle critical tasks like rendering and input processing, leading to a smoother gameplay experience.
-
Efficient Resource Utilization: The Job System is designed to manage CPU resources effectively, allowing your game to scale across various hardware configurations, from high-end gaming rigs to mobile devices.
-
Simplified Multithreading: Unity’s Job System abstracts the complexity of traditional multithreading, making it accessible even for developers with minimal experience in concurrent programming.
-
Deterministic Execution: Jobs in Unity are executed in a predictable order, ensuring consistent results across different runs and platforms.
Demo.mp4
Just download the release v0.1.0 "First Release" from here: https://github.com/Hllib/SummerPractice2024/releases/tag/v0.1.0