I'm working on a fluid dynamics Navier-Stokes solver that should run in real time. Hence, performance is important.

Right now, I'm looking at a number of tight loops that each account for a significant fraction of the execution time: there is no single bottleneck. Most of these loops do some floating-point arithmetic, but there's a lot of branching in between.

The floating-point operations are mostly limited to additions, subtractions, multiplications, divisions and comparisons. All this is done using 32-bit floats. My target platform is x86 with at least SSE1 instructions. (I've verified in the assembler output that the compiler indeed generates SSE instructions.)

Most of the floating-point values that I'm working with have a reasonably small upper bound, and precision for near-zero values isn't very important. So the thought occurred to me: maybe switching to fixed-point arithmetic could speed things up? I know the only way to be really sure is to measure it, that might take days, so I'd like to know the odds of success beforehand.

Fixed-point was all the rage back in the days of Doom, but I'm not sure where it stands anno 2010. Considering how much silicon is nowadays pumped into floating-point performance, is there a chance that fixed-point arithmetic will still give me a significant speed boost? Does anyone have any real-world experience that may apply to my situation?