This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them. Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.
One major concern with both the function parallel models is that they have an upper limit to how many cores they can support. This is the limit of how many parallel tasks it is possible to find in the engine. The number of meaningful tasks is decreased by the fact that threading very small tasks will yield negligible results. The synchronous function parallel model imposes an additional limit – the parallel tasks should have very little dependencies on each other. For example it is not sensible to run a physics task in parallel with a rendering task if the rendering needs the object coordinates from the physics task.
The expected performance of the model can be directly seen from the length of the longest path of execution in the game loop. The length of this path of execution is directly tied to the amount of parallelism in the loop. As this model generally allows the least amount of parallelism of the three models, the same can be expected from the model's performance.
As the synchronous function parallel model assumes there are very few connections between tasks that run in parallel, existing components do not require many changes. For example if running the physics component update is a task that can be run concurrently with the sound mixer, neither component needs special support to operate.
Gabb and Lake propose an alternative function parallel model. The difference is that this model doesn't contain a game loop. Instead, the tasks that drive the game forward update at their own pace, using the latest information available. For example the rendering task might not wait for the physics task to finish, but would just use the latest completed physics update. By using this method it is possible to efficiently parallelize tasks that are interdependent. Figure 2 shows an example of the asynchronous function parallel model.
Figure 2. The asynchronous function parallel model enables interdependent tasks to run in parallel. The rendering task does not wait for the physics task to finish, but uses the latest complete physics update.
As with the synchronous model, the scalability of the asynchronous function parallel model is limited by how many tasks it is possible to find from the engine. Fortunately the communication between threads by only using the latest information available effectively reduces the need for the threads to be truly independent. Thus we can easily have a physics task run concurrently with a rendering task – the rendering task would use a previous physics update to get the coordinates for each object. Based on this, the asynchronous model can support a larger amount of tasks, and therefore a larger amount of processor cores, than the synchronous model.