Based on what I've read before, vectorization is a form of parallelization known as SIMD. It allows processors to execute the same instruction (such as addition) on an array simultaneously.
However, I got confused when reading The Relationship between Vectorized and Devectorized Code regarding Julia's and R's vectorization performance. The post claims that devectorized Julia code (via loops) is faster than the vectorized code in both Julia and R, because:
This confuses some people who are not familiar with the internals of R. It is therefore worth noting how one improves the speed of R code. The process of performance improvement is quite simple: one starts with devectorized R code, then replaces it with vectorized R code and then finally implements this vectorized R code in devectorized C code. This last step is unfortunately invisible to many R users, who therefore think of vectorization per se as a mechanism for increasing performance. Vectorization per se does not help make code faster. What makes vectorization in R effective is that it provides a mechanism for moving computations into C, where a hidden layer of devectorization can do its magic.
It claims that R turns vectorized code, written in R, into devectorized code in C. If vectorization is faster (as a form of parallelization), why would R devectorize the code and why is that a plus?