Modular Probabilistic Models via Algebraic Effects

Probabilistic programming languages (PPLs) allow programmers to construct statistical models and then simulate data or perform inference over them. Many PPLs restrict models to a particular instance of simulation or inference, limiting their reusability. In other PPLs, models are not readily composable. Using Haskell as the host language, we present an embedded domain specific language based on algebraic effects, where probabilistic models are modular, first-class, and reusable for both simulation and inference. We also demonstrate how simulation and inference can be expressed naturally as composable program transformations using algebraic effect handlers

Mechanized Reasoning About how Using Functional Programs And Embeddings

Embedding describes the process of encoding a program\u27s syntax and/or semantics in another language---typically a theorem prover in the context of mechanized reasoning. Among different embedding styles, deep embeddings are generally preferred as they enable the most faithful modeling of the original language. However, deep embeddings are also the most complex, and working with them requires additional effort. In light of that, this dissertation aims to draw more attention to alternative styles, namely shallow and mixed embeddings, by studying their use in mechanized reasoning about programs\u27 properties that are related to how . More specifically, I present a simple shallow embedding for reasoning about computation costs of lazy programs, and a class of mixed embeddings that are useful for reasoning about properties of general computation patterns in effectful programs. I show the usefulness of these embedding styles with examples based on real-world applications

Hoogle?: Constants and ?-abstractions in Petri-net-based Synthesis using Symbolic Execution

Type-directed component-based program synthesis is the task of automatically building a function with applications of available components and whose type matches a given goal type. Existing approaches to component-based synthesis, based on classical proof search, cannot deal with large sets of components. Recently, Hoogle+, a component-based synthesizer for Haskell, overcomes this issue by reducing the search problem to a Petri-net reachability problem. However, Hoogle+ cannot synthesize constants nor ?-abstractions, which limits the problems that it can solve. We present Hoogle?, an extension to Hoogle+ that brings constants and ?-abstractions to the search space, in two independent steps. First, we introduce the notion of wildcard component, a component that matches all types. This enables the algorithm to produce incomplete functions, i.e., functions containing occurrences of the wildcard component. Second, we complete those functions, by replacing each occurrence with constants or custom-defined ?-abstractions. We have chosen to find constants by means of an inference algorithm: we present a new unification algorithm based on symbolic execution that uses the input-output examples supplied by the user to compute substitutions for the occurrences of the wildcard. When compared to Hoogle+, Hoogle? can solve more kinds of problems, especially problems that require the generation of constants and ?-abstractions, without performance degradation

Retrofitting parallelism onto OCaml.

OCaml is an industrial-strength, multi-paradigm programming language, widely used in industry and academia. OCaml is also one of the few modern managed system programming languages to lack support for shared memory parallel programming. This paper describes the design, a full-fledged implementation and evaluation of a mostly-concurrent garbage collector (GC) for the multicore extension of the OCaml programming language. Given that we propose to add parallelism to a widely used programming language with millions of lines of existing code, we face the challenge of maintaining backwards compatibility--not just in terms of the language features but also the performance of single-threaded code running with the new GC. To this end, the paper presents a series of novel techniques and demonstrates that the new GC strikes a balance between performance and feature backwards compatibility for sequential programs and scales admirably on modern multicore processors

Extending Equational Monadic Reasoning with Monad Transformers

There is a recent interest for the verification of monadic programs using proof assistants. This line of research raises the question of the integration of monad transformers, a standard technique to combine monads. In this paper, we extend Monae, a Coq library for monadic equational reasoning, with monad transformers and we explain the benefits of this extension. Our starting point is the existing theory of modular monad transformers, which provides a uniform treatment of operations. Using this theory, we simplify the formalization of models in Monae and we propose an approach to support monadic equational reasoning in the presence of monad transformers. We also use Monae to revisit the lifting theorems of modular monad transformers by providing equational proofs and explaining how to patch a known bug using a non-standard use of Coq that combines impredicative polymorphism and parametricity

A Semantic Framework to Debug Parallel Lazy Functional Languages

It is not easy to debug lazy functional programs. The reason is that laziness and higherorder complicates basic debugging strategies. Although there exist several debuggers for sequential lazy languages, dealing with parallel languages is much harder. In this case, it is important to implement debugging platforms for parallel extensions, but it is also important to provide theoretical foundations to simplify the task of understanding the debugging process. In this work, we deal with the debugging process in two parallel languages that extend the lazy language Haskell. In particular, we provide an operational semantics that allows us to reason about our parallel extension of the sequential debugger Hood. In addition, we show how we can use it to analyze the amount of speculative work done by the processes, so that it can be used to optimize their use of resources

Iteration and labelled iteration

AbstractWe analyse the conventional sum-based representation of iteration from the perspective of programmers, and show that the syntax they suggest is fundamentally not a good representation of Java-style iteration with for, while, break, and continue. We present an alternative syntax, which we call “labelled iteration”, where loops are identified using labels.The languages are analysed: we give denotational and operational semantics, adequacy proofs for both languages, and a translation function from sum-based iteration to labelled iteration