Relational Compilation for Performance-Critical Applications Extensible Proof-Producing Translation of Functional Models into Low-Level Code

There are typically two ways to compile and run a purely functional program verified using an interactive theorem prover (ITP): automatically extracting it to a similar language (typically an unverified process, like Coq to OCaml) or manually proving it equivalent to a lower-level reimplementation (like a C program). Traditionally, only the latter produced both excellent performance and end-to-end proofs. This paper shows how to recast program extraction as a proof-search problem to automatically derive correct-by-construction, high-performance code from purely functional programs. We call this idea relational compilation-it extends recent developments with novel solutions to loop-invariant inference and genericity in kinds of side effects. Crucially, relational compilers are incomplete, and unlike traditional compilers, they generate good code not because of a fixed set of clever built-in optimizations but because they allow experts to plug in domain-specific extensions that give them complete control over the compiler's output. We demonstrate the benefits of this approach with Rupicola, a new compiler-construction toolkit designed to extract fast, verified, idiomatic low-level code from annotated functional models. Using case studies and performance benchmarks, we show that it is extensible with minimal effort and that it achieves performance on par with that of handwritten C programs.