Profiling R on a Contemporary Processor

R is a popular data analysis language, but there is scant experimental data characterizing the run-time profile of R programs. This paper addresses this limitation by system-atically cataloging where time is spent when running R pro-grams. Our evaluation using four different workloads shows that when analyzing large datasets, R programs a) spend more than 85 % of their time in processor stalls, which leads to slower execution times, b) trigger the garbage collector frequently, which leads to higher memory stalls, and c) cre-ate a large number of unnecessary temporary objects that causes R to swap to disk quickly even for datasets that are far smaller than the available main memory. Addressing these issues should allow R programs to run faster than they do today, and allow R to be used for analyzing even larger datasets. As outlined in this paper, the results presented in this paper motivate a number of future research inves-tigations in the database, architecture, and programming language communities. All data and code that is used in this paper (which includes the R programs, and changes to the R source code for instrumentation) can be found at