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arXiv · 1704.02996

ROSA: R Optimizations with Static Analysis

Rathijit Sen·Jianqiao Zhu·Jignesh M. Patel·Somesh Jha
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Abstract

R is a popular language and programming environment for data scientists. It is increasingly co-packaged with both relational and Hadoop-based data platforms and can often be the most dominant computational component in data analytics pipelines. Recent work has highlighted inefficiencies in executing R programs, both in terms of execution time and memory requirements, which in practice limit the size of data that can be analyzed by R. This paper presents ROSA, a static analysis framework to improve the performance and space efficiency of R programs. ROSA analyzes input programs to determine program properties such as reaching definitions, live variables, aliased variables, and types of variables. These inferred properties enable program transformations such as C++ code translation, strength reduction, vectorization, code motion, in addition to interpretive optimizations such as avoiding redundant object copies and performing in-place evaluations. An empirical evaluation shows substantial reductions by ROSA in execution time and memory consumption over both CRAN R and Microsoft R Open.

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Rathijit Sen, Jianqiao Zhu, Jignesh M. Patel, Somesh Jha. 2017-07-03. ROSA: R Optimizations with Static Analysis. https://arxiv.org/abs/1704.02996

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