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Compilers for dynamic languages often rely on intermediate representations with explicit type annotations to facilitate writing program transformations. This paper documents the design of a new typed intermediate representation for a just-in-time compiler for the R programming language called FIŘ. Type annotations, in FIŘ, capture properties such as sharing, the potential for effects, and compiler speculations. In this extended abstract, we focus on the sharing properties that may be used to optimize away some copies of values. 

This paper presents a comparative analysis of three implementations of the R bytecode compiler: the official R implementation, a Java-based compiler, and a Rust-based compiler. The R compiler, written in R itself, poses challenges in terms of performance and maintainability. We evaluate designs of the compilers, their trade-offs, and performance characteristics. The Rust version outperforms the Java version, which itself outperforms the R version. 

In order to generate efficient code, dynamic language compilers often need information, such as dynamic types, not readily available in the program source. Leveraging a mixture of static and dynamic information, these compilers speculate on the missing information. Within one compilation unit, they specialize the generated code to the previously observed behaviors, betting that past is prologue. When speculation fails, the execution must jump back to unoptimized code. In this paper, we propose an approach to further the specialization, by disentangling classes of behaviors into separate optimization units. With contextual dispatch, functions are versioned and each version is compiled under different assumptions. When a function is invoked, the implementation dispatches to a version optimized under assumptions matching the dynamic context of the call. As a proof-of-concept, we describe a compiler for the R language which uses this approach. Our implementation is, on average, 1.7× faster than the GNU R reference implementation. We evaluate contextual dispatch on a set of benchmarks and measure additional speedup, on top of traditional speculation with deoptimization techniques. In this setting contextual dispatch improves the performance of 18 out of 46 programs in our benchmark suite.