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Multithreaded Game Engine Architectures

September 6, 2006 Article Start Page 1 of 4 Next

Even though multicore processors have been available for the PC for well over a year, and the Xbox 360 has already sold millions, there is still a lack of knowledge regarding the development of game engines for multicore platforms. This article will attempt to provide a view to game engine parallelism on an architecture level.

As shown by Gabb and Lake[1], instead of looking at multithreading on the level of individual components, we can find better performance by looking for ways to add parallelism to the whole game loop. We will be looking at three different threading supported architecture models for the basic game loop, and comparing them with regards to qualities such as scalability, and expected performance.

There are two main ways to break down a program to concurrent parts: function parallelism, which divides the program to concurrent tasks, and data parallelism, which tries to find some set of data for which to perform the same tasks in parallel. Of the three compared models, two will be function parallel, and one data parallel.

Synchronous function parallel model

One way to include parallelism to a game loop is to find parallel tasks from an existing loop. To reduce the need for communication between parallel tasks, the tasks should preferably be truly independent of each other. An example of this could be doing a physics task while calculating an animation. Figure 1 shows a game loop parallelized using this technique.

Figure 1. A game loop parallelized using the synchronous function parallel model. The animation and the physics tasks can be executed in parallel.

Costa[2] presents a way to automate the scaling of this kind of an architecture. The idea is to divide the functionality to small tasks, build a graph of which tasks precede which task (such as the graph in Figure 1), then supply this task-dependency graph to a framework. The framework in turn will schedule the proper tasks to be run, minding the amount of available processor cores. The alternative to using such a framework is to rewrite parts of your code for each core amount you plan to support.

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