Part 2: Vectorisation using Intrinsics
A more direct, but more difficult, way of vectorising your code is to use vector intrinsics. Intrisics provide data types for vectors (i.e. __m128 a; would declare the variable a to be a vector of 4 floats). They also provide functions that operate directly on vectors (i.e. _mm_add_ps(a,b) would add together the two vectors a and b).
Working directly with intrinsics means that you have taken responsibility for vectorising your code. The advantage is that you can be confident that any compiler will produce roughly similar output, and that you have complete control of what is vectorised and how. This makes it easier to achieve performance portability, as you know that your code should run at a similar speed regardless of the compiler used. However, a major disadvantage is that the intrinsic data types and functions are specific for a particular processor and vectorisation architecture. For example __m128 a is specific to SSE, meaning the code will only run on processors that support SSE. This means that the gain of performance portability comes with a loss of code portability.
In this part of the workshop you will learn about the vector intrinsics available to support SSE and AVX. You will also learn how to write code that uses intrinsics, but remains code portable (i.e. will still run on computers that don’t support SSE or AVX).