An Introduction to GCC Compiler Intrinsics in Vector Processing

Finally, here are some performance tips.

First, get a recent compiler and use the best code generation options. (Check the info pages that come with your compiler for things like the -mcpu option.)

Second, profile your code. Humans are bad at guessing where the bottlenecks are. Fix the bottlenecks, not other parts.

Third, get the most work you can from each vector operation by using the vector with the narrowest type elements that your data will fit into. Get the most work you can in each time slice by having enough work that you keep your vector hardware busy. Take big bites of data. If your vector hardware can handle a lot of vectors at the same time, use them. However, exceeding the number of vector registers you have available will slow things down. (Check your processor's documentation.)

Fourth, don't re-invent the wheel. Intel, Freescale and ARM all offer libraries and code samples to help you get the most from their processors. These include Intel's Integrated Performance Primitives, Freescale's libmotovec and ARM's OpenMAX.


In summary, GCC offers intrinsics that allow you to get more from your processor without the work of going all the way to assembly. We have covered basic types and some of the vector math functions. When you use intrinsics, make sure you test thoroughly. Test for speed and correctness against a scalar version of your code. Different features of each processor and how well they operate means that this is a wide open field. The more effort you put into it, the more you will get out.


The GCC include files that map intrinsics to compiler built-ins (eg arm_neon.h) and the GCC info pages that explain those built-ins:

Integrated Performance Primitives


Freescale AltiVec Libs for Linux

AltiVec TM Technology Programming Interface Manual

Ian Ollmann's Altivec Tutorial

RealView Compilation Tools Compiler Reference Guide (especially Appendix E)

RealView Compilation Tools Assembler Guide (esp chapter 5)

Intel C++ Intrinsics Reference


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Hi! > Use GCC's "aligned"

Gluttton's picture


> Use GCC's "aligned" attribute to align data sources and destinations on 16-bit
> float anarray[4] __attribute__((aligned(16))) = { 1.2, 3.5, 1.7, 2.8 };

I'm not shure, but it seams to me that instead of "16-bit" should be writen "16-bytes" ( ). Isn't it?

intrisics is i´m follow

ikkela's picture

intrisics is i´m follow

vector Processing

brian ( vector processing)'s picture

Very interesting article here. But i found different Vector Processing Concepts here >> at


ftheile's picture

The pattern for ARM Neon types is not [type]x[elementcount]_t, but [type][elementcount]x_t.

re Correction

G. Koharchik's picture

You might take a look at:

In example 1.1 they use uint32x4_t as a four element vector of 32-bit unsigned integers...


ssam's picture has some tips on helping GCC autovectorise code.

How old is this article?

Anonymous's picture

So it talks about ancient tech like MMX and SSE2, my guess these days you would write about AVX. Also the links at the end often lead to nowhere, and an article from 2005. This makes me wonder when this article was actually written.

re How old is this article?

G. Koharchik's picture

Very perceptive. The article was accepted for publication in July of 2011. That's why the ARM and Freescale links have gone stale. (I'll post an updated set later this week.)

The choice of MMX and SSE2 for X86 was deliberate. For an introductory article, things that are simple and widespread are often the best choices.

I think an AVX article would wonderful. Any volunteers?

no, intrinsics are no replacement for hand-optimized simd asm

holger's picture

so far, i encountered only one case where intrinsics are somewhat useful - when trying to unroll a loop of non-trivial vector code. if you write a test implementation using intrinsics and let gcc unroll that a bit for you, gcc's liveness analysis and resulting register allocation may give you useful hints for writing the final asm function. but i have never seen a case where gcc produces optimal code from intrinsics for a non-trivial function.

and regarding vendor libraries - the functions they provide are of varying quality with regard to optimization, but even in the cases where the code is pretty good, they don't compete on equal grounds. they have to be pretty generic, which means you always have some overhead. optimizations in simd asm often come from specific knowledge regarding variable ranges. data layout, or data reuse. the vendor lib can't do that.

so write your proof-of-concept using intrinsics or vendor libs. and if performance satisfies you, just keep it that way. but if a function still is a major hotspot, you can do better if you go asm (maybe only a bit, more likely a lot)

Recent i see one articles in

mikkela's picture

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Anonymous dude's picture

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