FastR is an implementation of the R Language in Java atop Truffle, a framework for building self-optimizing AST interpreters.

FastR is:

• polyglot

..R is very powerful and flexible, but certain tasks are best solved by using R in combination with other programming languages. ..Interfaces to languages, e.g., Java, Fortran and C/C++, incur a significant overhead, which is caused, to a large degree, by the different execution strategies employed by different languages, e.g., compiled vs. interpreted, and by incompatible internal data representations.

..The Truffle framework addresses these issues at a very fundamental level, and builds the necessary polyglot primitives directly into the runtime. ..Consequently, FastR leverages this infrastructure to allow multiple languages to interact transparently and seamlessly. ..All parts of a polyglot application can be compiled by the same optimizing compiler, and can be executed and debugged simultaneously, with little to no overhead at the language boundary.

• efficient

..R is a highly dynamic language that employs a unique combination of data type immutability, lazy evaluation, argument matching, large amount of built-in functionality, and interaction with C and Fortran code. ..Consequently, applications that spend a lot of time in R code often have performance problems. ..Common solutions are to try to apply primitives to large amounts of data at once and to convert R code to a native language like C.

..FastR makes extensive use of the dynamic optimization features provided by the Truffle framework to remove the abstractions that the R language introduces, and can use the Graal compiler to create optimized machine code on the fly.

• compatible

..The hardest challenge for implementations of the R language is the tradeoff between compatibility and performance. ..If an implementation is very compatible, e.g., by using the traditional internal data layout, it cannot perform optimizations that imply a radically different internal structure. ..If an implementation is very efficient, e.g., by adapting internal data structures to the current requirements, it will find it difficult to implement some parts of the GNUR system that are interfacing with applications and packages.

FastR employs many different solution strategies in order to overcome these problems. It also explores possible solutions at a grander scale, like evolution and emulation of R’s native interfaces.

FastR is available in two forms:

1. As a pre-built binary. N.B. This also includes (Truffle) implementations of Ruby and JavaScript. The pre-built binaries are available for Linux and Mac OS X. There is no Windows version available. The binary release is updated monthly.
2. As a source release on GitHub for developers wishing to contribute to the project and/or study the implementation. N.B. This does not contain Ruby or JavaScript. The source release is updated regularly and always contains the latest tested version.

FastR is intended eventually to be a drop-in replacement for GNU R. Currently, however, the implementation is incomplete. Notable limitations are:

1. Graphics support: FastR supports only grid and grid-based packages, graphics package is not supported. The FastR grid package implementation is purely Java based, see its documentation for more details and limitations.
2. Some packages either do not install, or fail tests due to bugs and limitations in FastR. In particular support for popular packages such as data.table and Rcpp is work in progress.

After downloading and unpacking the binary release, or compiling from source, the bin directory contains the R and Rscript commands and these can be used in a similar way to GNU R.

FastR is primarily aimed at long-running applications. The runtime performance behavior is, like Java, based on runtime profiling and runtime compilation of the hot code paths. Therefore, there is an inevitable warm-up time before peak performance is achieved when evaluating a given expression. In addition, startup is slower than GNU R, due to the overhead from Java class loading and compilation.

Building FastR from source is supported on Mac OS X (El Capitan onwards), and various flavors of Linux. FastR uses a build tool called mx (cf maven) which can be downloaded from here. mx manages software in suites, which are normally one-to-one with a git repository. FastR depends fundamentally on the truffle suite. However, performance also depends on the Graal compiler as without it, FastR operates in interpreted mode only. The conventional way to arrange the Git repos (suites) is as siblings in a parent directory, which we will call FASTR_HOME.

FastR shares some code with GnuR, for example, the default packages and the Blas library. Therefore, a version of GnuR (currently R-3.3.2), is downloaded and built as part of the build. Both GNU R and FastR require access certain tools and packages that must be available prior to the build. These are:

A jvmci-enabled Java JDK which is available from [pre-built binary](http://www.oracle.com/technetwork/oracle-labs/program-languages/downloads/index.html)
Python version 2.7.x
A Fortran compiler and libraries. Typically gfortran 4.8 or later
A C compiler and libraries. Typically gcc or clang
The pcre package, version 8.38 or later
The zlib package, version 1.2.8 or later
The bzip2 package, version 1.0.6 or later
The xz package, version 5.2.2 or later
The curl package, version 7.50.1 or later

If any of these are missing the GNU R build will fail which will cause the FastR build to fail also. If the build fails, more details can be found in log files in the libdownloads/R-{version} directory. Note that your system may have existing installations of these packages, possibly in standard system locations, but older versions. These must either be upgraded or newer versions installed with the package manager on your system. Since different systems use different package managers some of which install packages in directories that are not scanned by default by the C compiler and linker, it may be necessary to inform the build of these locations using the following environment variables:

PKG_INCLUDE_FLAGS_OVERRIDE
PKG_LDFLAGS_OVERRIDE

For example, on Mac OS, the MacPorts installer places headers in /opt/local/include and libraries in /opt/local/lib, in which case, the above variables must be set to these values prior to the build, e.g.:

export PKG_INCLUDE_FLAGS_OVERRIDE=-I/opt/local/include
export PKG_LDFLAGS_OVERRIDE=-L/opt/local/lib

Note that if more than once location must be specified, the values must be quoted, e.g., as in export PKG_LDFLAGS_OVERRIDE="\"-Lpath1 -Lpath2\"".

The environment variable JAVA_HOME must be set to the location of the jvmci-enabled Java JDK.

Use the following sequence of commands to download and build an interpreted version of FastR.

$ mkdir $FASTR_HOME
$ cd $FASTR_HOME
$ git clone http://github.com/graalvm/mx
$ PATH=$PATH:$FASTR_HOME/mx
$ git clone http://github.com/graalvm/fastr
$ cd fastr
$ mx build

The build will clone the Truffle repository and also download various required libraries, including GNU R, which is built first. Any problems with the GNU R configure step likely relate to dependent packages, so review the previous section. For FastR development, GNU R only needs to be built once, but an mx clean will, by default remove it. This can be prevented by setting the GNUR_NOCLEAN environment variable to any value.

It is possible to build FastR in "release mode" which builds and installs the GNU R "recommended" packages and also creates a fastr-release.jar file that contains everything that is needed to run FastR, apart from a Java VM. In particular it captures the package dependencies, e.g., pcre and libgfortran, so that when the file is unpacked on another system it will work regardless of whether the packages are installed on that system. For some systems that depend on FastR, e.g., GraalVM, it is a requirement to build in release mode as they depend on this file. To build in release mode, set the FASTR_RELEASE environment variable to any value. Note that this can be done at any time without doing a complete clean and rebuild. Simply set the variable and execute mx build.

After building, running the FastR console can be done either with bin/R or with mx r or mx R. Using mx makes available some additional options that are of interest to FastR developers. FastR supports the same command line arguments as R, so running an R script is done with bin/R -f <file> or bin/Rscript <file>.

mx supports IDE integration with Eclipse, Netbeans or IntelliJ and creates project metadata with the ideinit command (you can limit metadata creation to one IDE by setting the MX_IDE environment variable to, say, eclipse). After running this command you can import the fastr and truffle projects using the File->Import menu.

Further documentation on FastR, its limitations and additional functionality is here.

We would like to grow the FastR open-source community to provide a free R implementation atop the Truffle/Graal stack. We encourage contributions, and invite interested developers to join in. Prospective contributors need to sign the Oracle Contributor Agreement (OCA). The access point for contributions, issues and questions about FastR is the GitHub repository