Book Image

Unity 2017 Game Optimization - Second Edition

By : Chris Dickinson
Book Image

Unity 2017 Game Optimization - Second Edition

By: Chris Dickinson

Overview of this book

Unity is an awesome game development engine. Through its massive feature-set and ease-of-use, Unity helps put some of the best processing and rendering technology in the hands of hobbyists and professionals alike. This book shows you how to make your games fly with the recent version of Unity 2017, and demonstrates that high performance does not need to be limited to games with the biggest teams and budgets. Since nothing turns gamers away from a game faster than a poor user-experience, the book starts by explaining how to use the Unity Profiler to detect problems. You will learn how to use stopwatches, timers and logging methods to diagnose the problem. You will then explore techniques to improve performance through better programming practices. Moving on, you will then learn about Unity’s built-in batching processes; when they can be used to improve performance, and their limitations. Next, you will import your art assets using minimal space, CPU and memory at runtime, and discover some underused features and approaches for managing asset data. You will also improve graphics, particle system and shader performance with a series of tips and tricks to make the most of GPU parallel processing. You will then delve into the fundamental layers of the Unity3D engine to discuss some issues that may be difficult to understand without a strong knowledge of its inner-workings. The book also introduces you to the critical performance problems for VR projects and how to tackle them. By the end of the book, you will have learned to improve the development workflow by properly organizing assets and ways to instantiate assets as quickly and waste-free as possible via object pooling.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback
Software and Hardware List

Share calculation output

Performance can be saved by having multiple objects share the result of some calculation; of course, this only works if all of them would generate the same result. Such situations are often easy to spot, but can be tricky to refactor, and so exploiting this would be very implementation dependent. 

Some examples might include finding an object in a Scene, reading data from a file, parsing data (such as XML or JSON), finding something in a big list or deep dictionary of information, calculating pathing for a group of Artificial Intelligence (AI) objects, complex mathematics-like trajectories, raycasting, and so on.

Think about each time an expensive operation is undertaken, and consider whether it is being called from multiple locations but always results in the same output. If this is the case, then it would be wise to restructure things so that the result is calculated once and then distributed to every object that needs it in order to minimize the amount of recalculation...