It's not safe to allow exceptions go through it either. There's no locks in there and no way for it to protect itself. At least, that's how I would try to do it. There are various things you could do to speed it up, for instance, if you are loading copies of a single script in each of the threads, you could compile it to lua bytecode before you launch any of the threads, then put the buffer into shared memory, and have the scripts load the shared byte code without changing. That's most likely an unnecessary optimization though, depending on your application.
Use Stack Overflow for Teams at work to find answers in a private and secure environment. Get your first 10 users free. Sign up. Learn more. First 10 Free. Asked 4 years ago. Active 4 years ago. Viewed times. Does anyone have any ideas, or suggestions for threading? Borodin k 9 9 gold badges 58 58 silver badges bronze badges. So, this question is quite broad right now. You might want to split it up into some simpler, very specific questions that an expert can unambiguously answer. Chris Beck Chris Beck 11k 3 3 gold badges 32 32 silver badges 67 67 bronze badges. But the biggest advantage of free step execution mode is the extra flexibility it brings along.
Both free step and lock step can max out our CPU usage, but free step is superior in that it's capable of delivering a better gaming experience because we can now dynamically adapt our game to the gamers' machines by allowing different subsystems to run at different frequencies. So while it's true that they both can theoretically max out our CPU usage meaning that literally no CPU cycles are wasted, but free step can partition a frame in a way that more time is devoted to those subsystems that we, as the developers, think contribute more to our game's "responsiveness".
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The way I have envisioned it, we would identify our priorities in a list i. Rendering, 2. Physics, 3. AI and such and when the game starts, it would scale to the gamers' machines so those subsystems that have lower priorities would slow down in favor of those that are given higher priorities. We can even let the gamer tweak this priority inside the Options menu, so for instance, if one particular gamer is more interested in a more challenging AI than more accurate physics calculations she can reverse the priorities.
Of course this is easier to say than do, but all things we have today started as ideas one day nonetheless. In case anybody comes up with other interesting ideas or implementations! I'm not much of a blogger as you see! Hey Josh, you have some interesting ideas here. I think you are right, the best place to put most of the burden would be the Scheduler. One thing the Scheduler is lacking in Smoke is "knowledge" about running tasks. It doesn't have priority or information for load balancing.
Right now, the Scheduler needs a large number of small tasks to keep all the worker threads busy because it doesn't try to actively load balance. With TBB we to get some extra balancing with task stealing, but the Scheduler could to a lot more. This way, we are effectively turning that explicit loop in the one-thread-per-subsystem approach into an implicit loop. This approach is preferable if we don't want to make drastic changes to the scheduler, but puts the burden of scheduling on the tasks themselves.
A better approach that I can think of, which doesn't suit Smoke's current scheduler right out of the box, is to put this burden on the scheduler itself. This way a scheduler has an internal timer with events set at different frequencies which we have obtained from the subsystems themselves, i. I haven't given this further thought though.
dawned Multi-Thread Programming Resources for Game Engine
What would happen if the system under too much pressure that can't run the tasks at the predefined intervals? Maybe a dynamic solution that alters the frequencies during the run-time would help us alleviate this problem to some degree But one problem remains.
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The downside of free step approaches in general is that it would introduce more lag into our system. In the worst case, the rendering happens before physics which takes place before AI which in turn runs before input and I'm not even taking other subsystems into considerations. Menu Documentation. Share Tweet Share Send.
Introduction With the advent of multiple cores within a processor the need to create a parallel game engine has become more and more important. Assumptions This paper assumes a good working knowledge of modern computer game development as well as some experience with game engine threading or threading for performance in general. Parallel Execution State The concept of a parallel execution state in an engine is crucial to an efficient multi-threaded runtime.
Execution Modes Execution state management works best when operations are synchronized to a clock, meaning the different systems execute synchronously. Free Step Mode This mode of execution allows systems to operate in the time they need to complete their calculations. Lock Step Mode This mode requires that all systems complete their execution in a single clock.
Data Synchronization It is possible for multiple systems to make changes to the same shared data. There are two such mechanisms that can be used: Time, where the last system to make the change time-wise has the correct value. Priority, where a system with a higher priority will be the one that has the correct value.
This can also be combined with the time mechanism to resolve changes from systems of equal priority. The following diagram illustrates the different sections that make up the engine: Figure 3: Engine High-Level Architecture Notice that the game processing functionality, referred to as a system, is treated as a separate entity from the engine. Two reasons for inter system communication are: To inform another system of a change it has made to shared data e. Framework The framework is responsible for tying in all the different pieces of the engine together. The game loop is also located within the framework and has the following flow: Figure 4: Main Game Loop The first step in the game loop is to process all pending OS window messages as the engine operates in a windowed environment.
Scheduler The scheduler holds the master clock for execution which is set at a pre-determined frequency. The following diagram illustrates the universal scene and object extension of a system: Figure 5: Universal Scene and Object Extension An example of how extensions work is as follows: A universal scene is extended to have graphics, physics, and other properties. The Managers The managers provide global functionality within the engine and are implemented as singletons, meaning there will only be one instantiation made available for each type of manager. The following demonstrates how the task manager could issue tasks onto threads for execution on a quad core system: Figure 6: Task Manager Thread Pool Example Aside from access by the scheduler for issuing of primary tasks, the task manager also has an initialization mode where it will call systems serially from each thread so that the systems can initialize any thread local storage they require for execution.
State Manager State management is part of the messaging mechanism that tracks and distributes change notifications made by a system to other interested systems. Figure 7: Internal UObject Change Notification While you would think that change notifications would have to be distributed serially, it is possible to parallelize this action. Service Manager The service manager provides access to functionality to systems that otherwise would not have such functionality.
Figure 8: Service Manager Example The service manager has another role of providing access to the properties of the different systems to each other. The following is a list of the function groups provided by the environment manager: Variables — variable names and data that are shared across the entire engine. The variables are usually set upon loading a scene or some user settings, and are queried in the engine and or by the different systems.
Execution — information about the execution, such as the end of a scene or end of the program. This can be set or queried for by either the engine or the systems. Platform Manager The platform manager handles all abstraction of OS calls and also provides added functionality beyond just a simple abstraction. Interfaces The interfaces are the means of communication between the framework, the managers, and the systems.
Subject and Observer Interfaces The subject and observer interfaces are used for the registration of the observer with the subject and for passing of change notifications from the subject to the observer. Manager Interfaces The managers, even though they are singletons, are only directly available to the framework which means that the different systems do not have access to them. System Interfaces The systems need to implement interfaces in order for the framework to get access to its components. Change Interfaces There are also some special interfaces that are used for passing data between the systems.
Systems The systems are what provide the game functionality to the engine. Types The engine should have some predefined systems types that go along with them for standard game components.
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System Components A system has several components to it that need to be implemented. System The system component, or system, is responsible for initializing system resources that will remain more or less constant throughout the execution of the engine. Scene The scene component, otherwise known as a system scene, is responsible for handling resources that are pertinent to the existing scene.
Object The object component, alternatively a. Task The task component, referred to as a system task, is responsible for operating on the scene.
Multithreading Problems In Game Design
Initialization Stage Engine execution begins by initializing the managers and the framework. The framework calls the scene loader to load in the scene. The loader determines what systems the scene is using then calls the platform manager to load those modules. The platform manager loads the modules, passes in the manager interfaces, then calls into them to create a new system. The module returns a pointer to the instantiated system which implements the system interface.
The system module will also register any services it provides with the service manager.
Figure Engine Manager and System Initialization 6. Scene Loading Stage Control returns to the loader which loads the scene. The loader creates a universal scene and calls each system interface to instantiate system scenes, extending the functionality of the universal scene. The universal scene checks each system scene for what shared data changes they could possibly make and what shared data changes they would like to receive.
The universal scene then registers the matching system scenes with the state manager so that they will be notified of the changes. The loader creates a universal object for each object in the scene and determines which systems will be extending the universal object. The universal object follows a similar system object registration pattern with the state manager as that of the universal scene. The loader instantiates system objects via the system scene interfaces it previously received and extends the universal objects with the system objects. The scheduler then queries the system scene interfaces for their primary tasks because the scheduler is responsible for issuing the primary tasks to the task manager during execution.
Figure Universal Scene and Object Initialization 6. Execution is then transferred to the scheduler, which waits for the clock time to expire before proceeding. The scheduler, for free step mode, checks which of the system tasks completed execution in the previous clock. All tasks that are done i. The scheduler will now determine which tasks will complete on the current clock and waits for completion of those tasks. For lock step mode, the scheduler issues all tasks and waits for them to complete for each clock step.
Task Execution Execution is transferred to the task manager. The task manager queues up all tasks submitted to it and starts processing each task as threads become available. Task processing is specific to each system. Systems can operate using only one task or they can issue more tasks which get queued in the task manager, thus potentially getting executed in parallel. As tasks execute they will operate on the entire scene or on specific objects and modify their internal data structures.
How to do it Wrong (Dedicated Subsystem Threads)
Any data that is considered as shared, like position and orientation, needs to get propagated to the other systems. The system task does this by having the system scene or system object whichever was changed inform their observer of the change. In this case the observer is actually the change controller located in the state manager. The change controller queues up the change information to be processed later, but change types that the observer is not interested in are simply ignored.
If the task needs any services it goes through the service manager to call into the provided service. Tasks can also call into the environment manager to read environment variables, change the runtime state e. Figure Task Manager and Tasks 6. Distribution Once all tasks targeted for the current clock have completed execution, the main loop calls the state manager to distribute the changes.
The state manager calls each of its change controllers to distribute the changes they have queued up. For free step mode, the observer gets the changed data from the change controller, but for lock step mode the observer queries the subject for the data. The observers that are interested in the changes done by a system object will typically be other system objects that are all attached to the same universal object.
This makes it possible for the change distribution to be broken up into tasks for execution in parallel. About the Author Jeff Andrews is an Application Engineer with Intel working on optimizing code for software developers, currently focused on PC gaming. Appendix A. Appendix B. The following diagram illustrates the relationship: Figure Observer Design Pattern The following is the flow of events: The observer registers itself with the subject that it wants to observe changes for via the change controller.
The change controller is actually an observer. Instead of registering the observer with the subject it registers itself with the subject and keeps its own list of which observers are registered with which subject. The subject inserts the observer actually the change controller in its list of observers that are interested in it; optionally there can also be a change type which identifies what type of changes the observer is interested in — this helps speed up the change notification distribution process.
When the subject makes a change to its data or state it notifies the observer via a callback mechanism and passes information of the types that were changed. The change controller queues up the change notifications and waits for the signal to distribute them. During distribution the change controller calls the actual observers.
The observers query the subject for the changed data or state or get the data from the message. When the observer is no longer interested in the subject or is being destroyed, it deregisters itself from the subject via the change controller.
Appendix D. Tips on Implementing Tasks While task distribution can be implemented in many different ways, it is best to keep the number of worker threads equal to the number of available logical processors of the platform. They are as follows: Reverse Issuing, if the order of primary tasks being issued is fairly static, the tasks can then be alternately issued in reverse order from frame to frame.
The last task to execute in a previous frame will more than likely still have its data in the cache, so by issuing the tasks in reverse order for the next frame it will all but guarantee that the CPU caches will not have to be repopulated with the correct data. Cache Sharing, some multi-core processors have their shared cache split into sections so that two processors may share a cache, while another two share a separate cache.
By issuing sub-tasks from the same system onto processors sharing a cache it will increase the likelihood that the data will already be in the shared cache. For more complete information about compiler optimizations, see our Optimization Notice. I really appreciate this article and I learned a lot from it. Log in to post comments. This architecture is flawed. That invites a lot of concurrency problems. Hi Josh, Thanks for giving this a really good looking over. Here are some tidbits to give you a little more insight into Smoke:.
Hey, I'm responding to Josh's many insightful questions posted earlier. Hello Orion, Informative blog post. I'd be looking forward to hearing from you Orion. Kind Regards, Josh P. Thanks again for the posts and let me know what you think of the blog :. Hey Orion, Thanks for taking the time and writing a follow-up. JNI calls are typically the only place you'll run into this problem ex. Much of jME hides this from you by handling it behind the scenes for you, but there are still several aspects that make direct LWJGL calls instead of sticking them in a stack to be invoked on the next update.
To my knowledge that is the same with jME-Physics as well, but I have almost no experience with version 2 of that. Hope that was more useful than just a ramble. Darkfrog that definetly helps me out in understanding the details of how to put together a multithreaded architecture. So for the most part you create threads to improve the OpenGL's thread responsiveness, you thread off tasks such as resource loading, game state creation and setup, network IO. You mention how you also use threads to parallelize game logic such as AI, I guess that's where I still am very fuzzy on it all.
How do you synchronize all your game logic threads so they provide consistent game updates. For example most game architectures I've seen you need to have a "game loop" where you do things as an atomic operation The whole sequence of tasks must be run uninterrupted :. Another very interesting article that discusses the design of Game Design being able to scale efficiently to multiple-core processors: Multithreading Game Engines for Multicore Machines It basically takes the extreme viewpoint that instead of seperating a game engine into a couple threads game logic, physics, rendering , that you should create pools of threads that would allow it to scale to massively parallel systems think cores haha.
So I guess we'd only be left with making course grain tasks parallel Full Physics updates, Full game logic updates , because having 's of threads is way to expensive to be worthwhile in a real time environment like 3D gaming. I guess maybe the solution is to see what options there are out there for non-premptive threading in Java and see if they would provide the necessary real time performance. Even still I guess that wouldn't really allow multicore scalability because any kind of microthread system is software based and doesn't really apply to the underlying OS threading system.
Well, I think majority of what you're still asking can be addressed by simply explaining the way I would do AI. You can split that out into its own thread granted that thread isn't starved and it won't cause any problems. Then if you have a controller that is updated in your game loop that actually implements the decisions that the AI has made like, walk forward then your AIs thinking process can be separated from the actual actions he's taking. From the other side if things "happen" and you need to respond the game can throw actions to an event queue that can be processed by another thread.
I do this extensively in JGN and although it can be complicated to manage it in a thread-safe manner it adds an awful lot of flexbility as you can go from one thread for everything to everything having its own thread and it doesn't matter to the system. Split things out into their logical places and use queues to push things between them. This keeps you from ever having to wait on another thread as well as giving you the benefit of keeping them in their logical places. Cool, that makes sense darkfrog. Basically synchronize your threads by having a shared memory queue that will pass events back and forth.
Now how do you keep your games internal simulation state synchronized. Would you just run the game logic loop including physics updating, input handling, collision detection, event handling, controller updating , all in a single thread to keep the simulation synchronized. Also threads in java don't guarantee that each thread has equal amount of time to do its work, so there'd have to be synchronized barriers that each thread would be waiting on so that all the game entities are in a consistent state, and the next batch of update threads can then continue. So what was the question again?
I'm pretty simple-minded at heart so I try to keep things as simplistic as possible. I attempt to go for the most elegant solution that makes the most sense. More times than not that tends to be the right way to go. There's a lot of aspects of threading that can add as well as remove complication, such as synchronization of threads, waiting on other threads, etc.
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