feat(post): Rcpp parallel's post began

content/post/rcpp-parallel/index.md

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+title = "Getting Started with Rcpp Parallel"
+
+date = 2020-03-23T21:00:00
+draft = false
+
+authors = ["Gabriel Teotonio"]
+
+tags = ["C++", "Parallel", "Benchmark"]
+
+summary = ""
+
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+
+My first contact with C/C++ was in Data Structures and Algorithms course in the begging of my undergrad, by the time I learned the 
+classical data structures as list, queue, stack, binary tree, binary search tree, among others. In most part of my projects at 
+university I used to program in R and wasn't aware that the computational power of C++ could be integrated to R and make libraries
+like `Armadillo`available. This reality change in my life when I was presented to `Rcpp` R library, which let us to import functions 
+from C++ to R session. Since then I've been using C++ integrated with R in many problems as my undergraduate thesis, which I developed 
+a K-means algorithm for Statistical Shape Analysis with 3-dimensional data about the cortical surface. Similar to the thesis problem,
+most of my projects integrating both languages needed a good computational performance for calculating operations with big 
+vector/matrices structures. Now I am trying to reach a new level of this integration, by using the `RcppParallel` library to run C++ 
+code in R with a parallel backend.  
+`RcppParallel` is a R package that provides implementation of high level parallel algorithms. The main functions are:  
+
+* `parallelFor`  
+* `parallelReduce`  
+
+Throughout this post we are going to run some examples and understand how this package works and its benefits. You can install the package with the following command  
+
+```r
+install.packages("RcppParallel")
+```
+
+A good reminder before starting to build your parallel functions is that R is single-thread, so you code should not call the R or Rcpp API in any fashion. It can cause crashes and other undefined behavior, as pointed by documentation.  
+