Emanuel’s Blog

Many algorithms require control-flow.

In this blog post, we will look at some very simple algorithms like fill, copy, map and iota, and see how we can vectorize them to get speedups.

The dot-product is one of the most fundamental Linear Algebra operations, and it has many applications.

Parallel processing is used in many places to speed up computation.

Aliasing is one of the biggest obstacles to vectorization and auto vectorization in particular.

There are a lot of examples for reductions: computing the sum, product or min over some list of numbers, the dot product (sum of element-wise products), or even the polynomial hash over a string.

An important topic in SIMD vectorization is alignment of memory accesses, i.e. loads and stores.

I assume that you have already looked at Part 0, Part 1, Part 2, and Part 3.

I assume that you have already looked at Part 0, Part 1, and Part 2.

I present this graphical overview of the C2 AutoVectorizer (SuperWord).