In a detailed Intel-sponsored technical article, veteran game programmer and architect Mike Abrash (Quake
) goes in-depth
on how Intel's upcoming Larrabee architecture pipeline deals intelligently with rasterization.
In the article, which is part of Intel's Visual Computing section
on Gamasutra, Abrash explains during the introduction:
"In 'Look at the Larrabee New Instructions (LRBni)', I presented an overview of Larrabee, an upcoming multithreaded, vectorized, many core processor from Intel. It may not be entirely apparent from that article, but the LRBni contains everything you need to do a great job running code that's designed to be vectorized, such as HLSL shaders.
But what about tasks that aren't inherently scalar (in particular, where the iterations aren't serially dependent), and that at the same time can't be efficiently parallelized in any obvious way, and for which the usual algorithms aren't easily vectorized?"
Abrash continues in his explanation:
"In the process of implementing the standard graphics pipeline for Larrabee, we (the Larrabee team at Intel's partner RAD Game Tools - Atman Binstock, Tom Forsyth, Mike Sartain, and me) have gotten considerable insight into that question, because while the pipeline is largely vectorizable, there are a few areas in which the conventional approaches are scalar, and where some degree of serialization is unavoidable.
This article takes a close look at how we applied the Larrabee New Instructions to one of those problem areas - rasterization - in the process, redesigning our implementation so it was far more vectorizable so it took advantage of the strengths of Larrabee's CPU-based architecture.
Needless to say, performance with the Larrabee New Instructions will vary greatly from one application to the next - there's not much to be done with purely scalar code - but diving into Larrabee rasterization in detail gives you a good sense of what it's like to apply the Larrabee New Instructions to at least one sort of non-obvious application."
The in-depth six-page sponsored feature is now available to read on Gamasutra
, with plenty of detail on the challenges the team faced and their solutions.