Multithreading is a concept that exist for many decades in computer science, nevertheless, only in recent years it become a trend in game development with the arrival of multi-core CPUs in our home PCs. In this small article we will discuss how AnKi utilizes the power of multiple processors. Also the source code of an example can be found at the end of the article.
The concept of multiple threads offers many advantages in applications that execute many uniform jobs at the same time, for example a webserver needs to serve multiple hosts at the same time and without a host waiting for a previous request to finish. A game application though used to have a very standard and linear flow. For example, we first update the AI, then the physics, then we update the world, we do visibility determination and lastly we render, then we repeat the same again. Sometimes its difficult to execute some of these distinctive steps in parallel because the data of the previous step will be used by the next. I bet high class development studios have found ways to blend these steps but in AnKi we use a more simple approach. We use multiple threads to run uniform jobs in parallel.
One good example to illustrate how AnKi uses the power of multiple threads is the visibility determination algorithm. One step of visibility determination is the test for every renderable scene node against the camera’s view frustum. If we have N nodes to test and M threads we can roughly assign for testing the first N/M nodes to the first thread, the next N/M nodes to the next thread etc.
“Ambient occlusion is a shading method used in 3D computer graphics which helps add realism to local reflection models by taking into account attenuation of light due to occlusion” [Wikipedia.org]
The proper way to do ambient occlusion is very expensive for today’s hardware and especially without the use of a ray-tracing renderer. For that reason a few new techniques developed that tried to produce the same result using simpler and faster algorithms. One of these approaches is the Screen Space Ambient Occlusion (aka SSAO) that makes the calculations of ambient occlusion in 2D space just like a normal 2D filter. This article will not go deep into what SSAO is, there are many great readings around the web that cover the theory behind SSAO, in this article we will jump into the implementation that AnKi uses.
There are quite a few techniques to produce SSAO, a top level way to group them is from the resources they need to produce the SSAO:
Some use just the depth buffer (Amnesia, Aliens VS Predator)
AnKi used to implement the second technique for quite some time. The results were good but apparently not that good, so for the past couple of weeks I’ve tried to implement the third technique by reading this great article from gamedev. The present article extends the gamedev one by adding a few optimization tips and by presenting the code in GLSL.
Bellow you can see the old and the new SSAO, rendered at the half of the original rendering size with two blurring passes.
C++ Standard Template Library (STL) offers a smart pointer called auto_ptr. auto_ptr is a template class that acts as a wrapper for a single dynamically allocated class instance. Its purpose and its usefulness is to deallocate the memory when this smart pointer gets out of scope. Its a way to do automatic garbage collection in C++ by saving us from the extra code writing. One extra reason to use auto_ptr is that it makes the code less error prone simply because it doesn’t rely on the programmer to do the memory dealocation.
In this short article we will discuss a case where auto_ptr fails to do what its supposed to do….
Ok, the title sounds a little bit pompous and promises allot, many fellow programmers will disagree with the described methodology and others may find it useful. After many changes and rewrites of the AnKi logging system we’ve gained some experience of how a proper logger should be designed and work. The purpose of this article is to pass this knowledge to anyone who is interested.