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The Present and Future of the R Programming Language

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As R is becoming increasingly more popular and widely used, two great challenges have emerged: performance and scalability. We aim to attack these problems with a new R engine built on top of a Java virtual machine.  The benefits we get from Java are good integrated support for multi-threading, a modern garbage collector, and a better integration with the cloud and databases. Choosing Java instead of say C++ brings also a number of challenges.  A big challenge is accessing well proven numerical libraries implemented in C/Fortran, such as LAPACK/BLAS, but also the Rmath library and other numerical codes present in R. We will explain the status of our  project, FastR. Currently, on small benchmarks, on these we have seen speedups between 2x and 15x over the latest version of the R interpreter.

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  • Why not build the new R engine on C# and the CLR? It seems like C#'s support for value types would help performance greatly (not to mention async/await and the superior implementation of generics).

  • My thoughts exactly elabs. Why use something inferior and lacking like Java when we could leverage the power of the .Net Framework to do the deed best?

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