Verified Compilers for a Multi-Language World

Though there has been remarkable progress on formally verified compilers in recent years, most of these compilers suffer from a serious limitation: they are proved correct under the assumption that they will only be used to compile whole programs. This is an unrealistic assumption since most software systems today are comprised of components written in different languages - both typed and untyped - compiled by different compilers to a common target, as well as low-level libraries that may be handwritten in the target language. We are pursuing a new methodology for building verified compilers for today\u27s world of multi-language software. The project has two central themes, both of which stem from a view of compiler correctness as a language interoperability problem. First, to specify correctness of component compilation, we require that if a source component s compiles to target component t, then t linked with some arbitrary target code t\u27 should behave the same as s interoperating with t\u27. The latter demands a formal semantics of interoperability between the source and target languages. Second, to enable safe interoperability between components compiled from languages as different as ML, Rust, Python, and C, we plan to design a gradually type-safe target language based on LLVM that supports safe interoperability between more precisely typed, less precisely typed, and type-unsafe components. Our approach opens up a new avenue for exploring sensible language interoperability while also tackling compiler correctness

Fundamental Approaches to Software Engineering

computer software maintenance; computer software selection and evaluation; formal logic; formal methods; formal specification; programming languages; semantics; software engineering; specifications; verificatio

New techniques for adaptive program optimization

Adaptive optimization technology is a key ingredient in modern runtime systems. This technology aims at improving performance by making optimization decisions on the basis of a program’s observed behavior. Application virtual machines indeed face different and perhaps more compelling issues compared to traditional static optimizers, as dynamic language features can force the deferral of most effective optimizations until run time. In this thesis, we present novel ideas to improve adaptive optimization, focusing on two main problems: collecting fine-grained program profiles with low overhead to guide feedback-directed optimization, and supporting continuous optimization and deoptimization by diverting execution across dynamically generated code versions. We present two profiling techniques: the first works at inter-procedural level to collect calling context information for hot code portions, while the second captures cyclic-path profiles within a function’s boundaries. Both techniques rely on efficient and elegant data structures, advancing the state of the art of the theory and practice of the performance profiling literature. We then focus our attention on supporting continuous optimization through on-stack replacement (OSR) mechanisms. We devise a new OSR framework encoded entirely at intermediate-representation level, which extends the best OSR practices with the ability to perform OSR at nearly any program location. Our techniques pave the road to aggressive optimizations and debugging techniques that were not supported by previous approaches. The main technical challenge is how to automatically generate compensation code to fix the program’s state across an OSR transition between different code versions. We present a conceptual framework for OSR, distilling its essence to a core calculus with an operational semantics. Using bisimulation techniques, we describe how OSR can be correctly supported in the presence of common compiler optimizations, providing the first soundness results in this context. We implement our ideas in production systems such as Jikes RVM and the LLVM compiler toolchain, and evaluate their performance against a variety of prominent benchmarks. We investigate the end-to-end utility of our techniques in a series of case studies: we illustrate two possible applications of multi-iteration path profiling, and show how our OSR techniques advance the state of the art for MATLAB code optimization and for source-level debugging of optimized code. Part of the results of this thesis have been published in PLDI, OOPSLA, CGO, and Software Practice and Experience