| There's some movement in that direction. However, the R core committers are essentially not only volunteers, but they're all (afaik) academic statisticians. One of the people who made strides in this direction is primarily an computational statistician at Iowa (Luke Tierney / compiler package). Building a high performance runtime/jit is wildly out of their scope of expertise. In retrospect, and I think many of them would agree, building and maintaining their own runtime was a giant mistake. Yet here we are. Serious compiler people (Jan Vitek, others) have made strides towards a faster implementation (his in java / fastr IIRC), but it suffers from the same problem as cpython: there are millions of lines of C code in packages or internal functions that have the details of the R interpreter / C interface deeply embedded in them. In fact, there's probably far more "R" code written in C than in R. Undoing this mess is not easy, and probably not possible. Oh, reading Evaluating the Design of the R Language [1] will shed some more light on why it's hard to make R run fast. [1] http://r.cs.purdue.edu/pub/ecoop12.pdf edited to correctly describe Luke as per gbrown |