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Dynamic languages such as R are increasingly used to process .large data sets. Here, the R interpreter induces a large memory overhead due to wasteful memory allocation policies. If an application's working set exceeds the available physical memory, the OS starts to swap, resulting in slowdowns of a several orders of magnitude. Thus, memory optimizations for R will be beneficial to many applications.

Existing R optimizations are mostly based on dynamic compilation or native libraries. Both methods are futile when the OS starts to page out memory. So far, only a few, data-type or application specific memory optimizations for R exist. To remedy this situation, we present a low-overhead page sharing approach for R that significantly reduces the interpreter's memory overhead. Concentrating on the most rewarding optimizations avoids the high runtime overhead of existing generic approaches for memory …