If you don’t want to buy a new expensive multicore laptop, cloud computing is your best bet. Hence you would directly increase the performance of your code. the time you have to wait for your computation to be done. Distributing these computations over several independent computers would directly save over the computation wall-clock time, ie. This bad performance is directly proportional to the number of calls to your model. System.time(replicate( 10, model(rnorm( n)))) # user system elapsed Consider for instance that one has a prior over n data samples so that the likelihood of the data could be something like that: Let us see define a toy example as an illustration. Without any painful or complicated devops tasks. In this post I want to share my experience on how to get a working RStudio on the cloud with your own files and as many CPU as you need. But aren't statistical simulations just different trials of the same things? What if you were using parallel computing to actually work them in parallel and achieve a scalable speedup? What if you were using it on the cloud? It is possible that you still have to wait minutes (or hours for heavy statistical simulation) for your computation to be done. All the usual tricks ( matrix calculations, *apply functions, compiler, Rcpp) may not bring a sufficient speedup. Getting working R code is quite straightforward But getting high performance R code may become a headache. However RStudio remains an unbeatable IDE for data analysis and research on the whole. I have been using R for almost ten years now I like R, I love it. What if cloud computing could save you days without changing your usual workflow? Data analysis with RStudio is great, apart from R famous poor performance.
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