An Introduction to GCC Compiler Intrinsics in Vector Processing

Speed is essential in multimedia, graphics and signal processing. Sometimes programmers resort to assembly language to get every last bit of speed out of their machines. GCC offers an intermediate between assembly and standard C that can get you more speed and processor features without having to go all the way to assembly language: compiler intrinsics. This article discusses GCC's compiler intrinsics, emphasizing vector processing on three platforms: X86 (using MMX, SSE and SSE2); Motorola, now Freescale (using Altivec); and ARM Cortex-A (using Neon). We conclude with some debugging tips and references.

Download the sample code for this article here: http://www.linuxjournal.com/files/linuxjournal.com/code/11108.tar

So, What Are Compiler Intrinsics?

Compiler intrinsics (sometimes called "builtins") are like the library functions you're used to, except they're built in to the compiler. They may be faster than regular library functions (the compiler knows more about them so it can optimize better) or handle a smaller input range than the library functions. Intrinsics also expose processor-specific functionality so you can use them as an intermediate between standard C and assembly language. This gives you the ability to get to assembly-like functionality, but still let the compiler handle details like type checking, register allocation, instruction scheduling and call stack maintenance. Some builtins are portable, others are not--they are processor-specific. You can find the lists of the portable and target specific intrinsics in the GCC info pages and the include files (more about that below). This article focuses on the intrinsics useful for vector processing.

Vectors and Scalars

In this article, a vector is an ordered collection of numbers, like an array. If all the elements of a vector are measures of the same thing, it's said to be a uniform vector. Non-uniform vectors have elements that represent different things, and their elements have to be processed differently. In software, vectors have their own types and operations. A scalar is a single value, a vector of size one. Code that uses vector types and operations is said to be vector code. Code that uses only scalar types and operations is said to be scalar code.

Vector Processing Concepts

Vector processing is in the category of Single Instruction, Multiple Data (SIMD). In SIMD, the same operation happens to all the data (the values in the vector) at the same time. Each value in the vector is computed independently. Vector operations include logic and math. Math within a single vector is called horizontal math. Math between two vectors is called vertical math.

Instead of writing: 10 x 2 = 20, express it vertically as:

                          10
                        x  2
                       ------
                          20

In vertical math, vectors are lines of these values; multiple operations happen at the same time:

        -------------------------------
        |  10   |   10  |  10  |  10  |   vector1
        -------------------------------
        -------------------------------
    x   |  2    |   2   |  2   |  2   |   vector2
        -------------------------------
   --------------------------------------
        -------------------------------
        |  20   |   20  |  20  |  20  |   vector3
        -------------------------------

All 10s are multiplied by all 2s at the same time.

So, to convert Celsius to Fahrenheit using F = (9/5) * C + 32 for a vector of temperatures in Celsius:

       -------------------------------
        |  C0   |   C1  |  C2  |  C3  |   Celsius temperatures vector
        -------------------------------
        -------------------------------
    x   |  9    |   9   |  9   |  9   |   vector2
        -------------------------------
   --------------------------------------
        -------------------------------
        |  p1   |   p2  |  p3  |  p4  |   partial result
        -------------------------------
        -------------------------------
    /   |  5    |   5   |  5   |  5   |   vector3
        -------------------------------
   --------------------------------------
        -------------------------------
        |  p1   |   p2  |  p3  |  p4  |   partial result
        -------------------------------
        -------------------------------
   +    |  32   |   32  |  32  |  32  |   vector4
        -------------------------------
    --------------------------------------

        -------------------------------
        |  F0   |   F1  |  F2  |  F3  |   Fahrenheit temperatures vector
        -------------------------------

Saturation arithmetic is like normal arithmetic except that when the result of the operation that would overflow or underflow an element in the vector, that is clamped at the end of the range and not allowed to wrap around. (For instance, 255 is the largest unsigned character. In saturation arithmetic on unsigned characters, 250 + 10 = 255.) Regular arithmetic would allow the value to wrap around zero and become smaller. For example, saturation arithmetic is useful if you have a pixel that is slightly brighter than maximum brightness. It should be maximum brightness, not wrap around to be dark.