Vectorization is a parallel computing method that compiles repetitive program instructions into a single vector (combination of multiple datasets), which is then executed simultaneously and maximizes computer speed. Vectorization is an example of single instruction, multiple data (SIMD) processing because it executes a single operation (e.g., addition, division) over a large dataset. Today, vectorization is a critical micro-process and universally present in modern computers.
Vectorization starts with the understanding the downside of scalar programming—the process of operating on a dataset sequentially. For example, a developer writes a for-loop to add two sets of numbers [a,b] to get a result [c]. In early computing, computers did just that, repeating the addition of a and b pairs one after another. Because these processes take place sequentially, the time needed to complete the program can be massive, depending on the datasets’ size.
This excess runtime is no problem for most users for a small set of numbers, but the simple repeated instruction means lost time and energy when working with large datasets. Using applications that automatically detect and convert scalar instructions into vectorized implementations, computers can drastically decrease runtime processing. Most computers today contain automatic vectorization abilities and multi-core CPUs, which mean order of magnitude performance gains.