The fundamental capabilities of each C++ SIMD class include:

Understanding each of these capabilities and how they interact is crucial to achieving desired results.


The SIMD C++ classes contain vertical operator support for most arithmetic operations, including shifting and saturation.

Computation operations include: +, -, *, /, reciprocal ( rcp and rcp_nr ), square root (sqrt), reciprocal square root ( rsqrt and rsqrt_nr ).

Operations rcp and rsqrt are new approximating instructions with very short latencies that produce results with at least 12 bits of accuracy. Operations rcp_nr and rsqrt_nr use software refining techniques to enhance the accuracy of the approximations, with a minimal impact on performance. (The "nr" stands for Newton-Raphson, a mathematical technique for improving performance using an approximate result.)

Horizontal Data Support

The C++ SIMD classes provide horizontal support for some arithmetic operations. The term "horizontal" indicates computation across the elements of one vector, as opposed to the vertical, element-by-element operations on two different vectors.

The add_horizontal, unpack_low and pack_sat functions are examples of horizontal data support. This support enables certain algorithms that cannot exploit the full potential of SIMD instructions.

Shuffle intrinsics are another example of horizontal data flow. Shuffle intrinsics are not expressed in the C++ classes due to their immediate arguments. However, the C++ class implementation enables you to mix shuffle intrinsics with the other C++ functions. For example:

F32vec4 fveca, fvecb, fvecd;
fveca += fvecb;
fvecd = _mm_shuffle_ps(fveca,fvecb,0);

Typically every instruction with horizontal data flow contains some inefficiency in the implementation. If possible, implement your algorithms without using the horizontal capabilities.

Branch Compression/Elimination

Branching in SIMD architectures can be complicated and expensive, possibly resulting in poor predictability and code expansion. The SIMD C++ classes provide functions to eliminate branches, using logical operations, max and min functions, conditional selects, and compares. Consider the following example:

short a[4], b[4], c[4];
for (i=0; i<4; i++)
c[i] = a[i] > b[i] ? a[i] : b[i];

This operation is independent of the value of i. For each i, the result could be either A or B depending on the actual values. A simple way of removing the branch altogether is to use the select_gt function, as follows:

Is16vec4 a, b, c
c = select_gt(a, b, a, b)

Caching Hints

Streaming SIMD Extensions provide prefetching and streaming hints. Prefetching data can minimize the effects of memory latency. Streaming hints allow you to indicate that certain data should not be cached. This results in higher performance for data that should be cached.