What is the limit of optimization using SIMD?

I need to optimize some C code, which does lots of physics computations, using SIMD extensions on the SPE of the Cell Processor. Each vector operator can process 4 floats at the same time. So ideally I would expect a 4x speedup in the most optimistic case.

Do you 开发者_Go百科think the use of vector operators could give bigger speedups?

Thanks

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The best optimization occurs in rethinking the algorithm. Eliminate unnecessary steps. Find more a direct way of accomplishing the same result. Compute the solution in a domain more relevant to the problem.

For example, if the vector array is a list of n which are all on the same line, then it is sufficient to transform the end points only and interpolate the intermediate points.

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It CAN give better speeds up than 4 times over straight floating point as the SIMD instructions could be less exact (Not so much as to give too many problems though) and so take fewer cycles to execute. It really depends.

Best plan is to learn as much about the processor you are optimising for as possible. You may find it can give you far better than 4x improvements. You may find out you can't. We can't say though without knowing more about the algorithm you are optimising and what CPU you are targetting.

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On their own, no. But if the process of re-writing your algorithms to support them also happens to improve, say, cache locality or branching behaviour, then you could find unrelated speed-ups. However, this is true of any re-write...

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This is entirely possible.

• You can do more clever instruction-level micro optimizations than a compiler, if you know what you're doing.
• Most SIMD instruction sets offers several powerful operations that don't have any equivalent in normal scalar FPU/ALU code (e.g. PAVG/PMIN etc. in SSE2). Even if these don't fit your problem exactly, you can often combine these instructions for great effect.
• Not sure about Cell, but most SIMD instruction sets have features to optimize memory access, for example to prefetch data into cache. I've had very good results with these.

Now this isn't Cell or PPC at all, but a simple image convolution filter of mine got a 20x speedup (C vs. SSE2) on Atom, which is higher than the level of parallelity (16 pixels at a time).

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It depends on the architecture.. For the moment I assume x86 architecture (aka SSE).

You can get factor four on tight loops easily. Just replace your existing math with SSE instruction and you're done.

You can even get a little more than that because if you use SSE you do the math in registers which are usually not used by the compiler. This frees up the general purpose register for other task such as loop control and address calculation. In short the code that surrounds the SSE instruction will be more compact and execute faster.

And then there is the option to hint the memory controller how you want to access the memory, e.g. if you want to store data in a way that it bypasses the cache or not. For bandwidth hungry algorithms that may give you some more extra speed ontop of that.