2017/04/10 by Rathijit Sen, Sen, Rathijit, Jian-Qiao Zhu +5
Computer Science · Decision Sciences · #Advanced Data Storage Technologies #Databases (cs.DB) #FOS: Computer and information sciences #Parallel Computing and Optimization Techniques #Performance (cs.PF) #Programming Languages (cs.PL) #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.1704.02996
openalex publication_date 2017/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.