Phobos: A front-end approach to extensible compilers (long version)

This paper describes a practical approach for implementing certain types of domain-specific languages with extensible compilers. Given a compiler with one or more front-end languages, we introduce the idea of a "generic" front-end that allows the syntactic and semantic specification of domain-specific languages. Phobos, our generic front-end, offers modular language specification, allowing the programmer to define new syntax and semantics incrementally

Enhancing R with Advanced Compilation Tools and Methods

I describe an approach to compiling common idioms in R code directly to native machine code and illustrate it with several examples. Not only can this yield significant performance gains, but it allows us to use new approaches to computing in R. Importantly, the compilation requires no changes to R itself, but is done entirely via R packages. This allows others to experiment with different compilation strategies and even to define new domain-specific languages within R. We use the Low-Level Virtual Machine (LLVM) compiler toolkit to create the native code and perform sophisticated optimizations on the code. By adopting this widely used software within R, we leverage its ability to generate code for different platforms such as CPUs and GPUs, and will continue to benefit from its ongoing development. This approach potentially allows us to develop high-level R code that is also fast, that can be compiled to work with different data representations and sources, and that could even be run outside of R. The approach aims to both provide a compiler for a limited subset of the R language and also to enable R programmers to write other compilers. This is another approach to help us write high-level descriptions of what we want to compute, not how.Comment: Published in at http://dx.doi.org/10.1214/13-STS462 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Heap Abstractions for Static Analysis

Heap data is potentially unbounded and seemingly arbitrary. As a consequence, unlike stack and static memory, heap memory cannot be abstracted directly in terms of a fixed set of source variable names appearing in the program being analysed. This makes it an interesting topic of study and there is an abundance of literature employing heap abstractions. Although most studies have addressed similar concerns, their formulations and formalisms often seem dissimilar and some times even unrelated. Thus, the insights gained in one description of heap abstraction may not directly carry over to some other description. This survey is a result of our quest for a unifying theme in the existing descriptions of heap abstractions. In particular, our interest lies in the abstractions and not in the algorithms that construct them. In our search of a unified theme, we view a heap abstraction as consisting of two features: a heap model to represent the heap memory and a summarization technique for bounding the heap representation. We classify the models as storeless, store based, and hybrid. We describe various summarization techniques based on k-limiting, allocation sites, patterns, variables, other generic instrumentation predicates, and higher-order logics. This approach allows us to compare the insights of a large number of seemingly dissimilar heap abstractions and also paves way for creating new abstractions by mix-and-match of models and summarization techniques.Comment: 49 pages, 20 figure