Monadic fold, Monadic build, Monadic Short Cut Fusion

Abstract: Short cut fusion improves the efficiency of modularly constructed programs by eliminating intermediate data structures produced by one program component and immediately consumed by another. We define a combinator which expresses uniform production of data structures in monadic contexts, and is the natural counterpart to the well-known monadic fold which consumes them. Like the monadic fold, our new combinator quantifies over monadic algebras rather than standard ones. Together with the monadic fold, it gives rise to a new short cut fusion rule for eliminating intermediate data structures in monadic contexts. This new rule differs significantly from previous short cut fusion rules, all of which are based on combinators which quantify over standard, rather than monadic, algebras. We give examples illustrating the benefits of quantifying over monadic algebras, prove our new fusion rule correct, and show how it can improve programs. We also consider its coalgebraic dual

Programación estructurada matemáticamente

Los lenguajes de programación modernos poseen características que aumentan considerablemente su poder expresivo. Este poder expresivo permite la internalización de las estructuras matemáticas que sustentan la semántica de los lenguajes. Dichas estructuras proveen abstracciones que facilitan distintos aspectos del desarrollo de software, como ser modularidad, optimización, seguridad y verificación. Nos proponemos investigar las propiedades y aplicaciones de estructuras tales como mónadas, arrows, y functores aplicativos.Eje: Aspectos teóricos de Ciencias de la ComputaciónRed de Universidades con Carreras en Informática (RedUNCI

Free Monads, Intrinsic Scoping, and Higher-Order Preunification

Type checking algorithms and theorem provers rely on unification algorithms. In presence of type families or higher-order logic, higher-order (pre)unification (HOU) is required. Many HOU algorithms are expressed in terms of $\lambda$-calculus and require encodings, such as higher-order abstract syntax, which are sometimes not comfortable to work with for language implementors. To facilitate implementations of languages, proof assistants, and theorem provers, we propose a novel approach based on the second-order abstract syntax of Fiore, data types \`a la carte of Swierstra, and intrinsic scoping of Bird and Patterson. With our approach, an object language is generated freely from a given bifunctor. Then, given an evaluation function and making a few reasonable assumptions on it, we derive a higher-order preunification procedure on terms in the object language. More precisely, we apply a variant of $E$-unification for second-order syntax. Finally, we briefly demonstrate an application of this technique to implement type checking (with type inference) for Martin-L\"of Type Theory, a dependent type theory

ContextWorkflow: A Monadic DSL for Compensable and Interruptible Executions

Context-aware applications, whose behavior reactively depends on the time-varying status of the surrounding environment - such as network connection, battery level, and sensors - are getting more and more pervasive and important. The term "context-awareness" usually suggests prompt reactions to context changes: as the context change signals that the current execution cannot be continued, the application should immediately abort its execution, possibly does some clean-up tasks, and suspend until the context allows it to restart. Interruptions, or asynchronous exceptions, are useful to achieve context-awareness. It is, however, difficult to program with interruptions in a compositional way in most programming languages because their support is too primitive, relying on synchronous exception handling mechanism such as try-catch. We propose a new domain-specific language ContextWorkflow for interruptible programs as a solution to the problem. A basic unit of an interruptible program is a workflow, i.e., a sequence of atomic computations accompanied with compensation actions. The uniqueness of ContextWorkflow is that, during its execution, a workflow keeps watching the context between atomic actions and decides if the computation should be continued, aborted, or suspended. Our contribution of this paper is as follows; (1) the design of a workflow-like language with asynchronous interruption, checkpointing, sub-workflows and suspension; (2) a formal semantics of the core language; (3) a monadic interpreter corresponding to the semantics; and (4) its concrete implementation as an embedded domain-specific language in Scala

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

Interleaving Data and Effects

The study of programming with and reasoning about inductive datatypes such as lists and trees has benefited from the simple categorical principle of initial algebras. In initial algebra semantics, each inductive datatype is represented by an initial f-algebra for an appropriate functor f. The initial algebra principle then supports the straightforward derivation of definitional principles and proof principles for these datatypes. This technique has been expanded to a whole methodology of structured functional programming, often called origami programming.In this article we show how to extend initial algebra semantics from pure inductive datatypes to inductive datatypes interleaved with computational effects. Inductive datatypes interleaved with effects arise naturally in many computational settings. For example, incrementally reading characters from a file generates a list of characters interleaved with input/output actions, and lazily constructed infinite values can be represented by pure data interleaved with the possibility of non-terminating computation. Straightforward application of initial algebra techniques to effectful datatypes leads either to unsound conclusions if we ignore the possibility of effects, or to unnecessarily complicated reasoning because the pure and effectful concerns must be considered simultaneously. We show how pure and effectful concerns can be separated using the abstraction of initial f-and-m-algebras, where the functor f describes the pure part of a datatype and the monad m describes the interleaved effects. Because initial f-and-m-algebras are the analogue for the effectful setting of initial f-algebras, they support the extension of the standard definitional and proof principles to the effectful setting. Initial f-and-m-algebras are originally due to Filinski and Støvring, who studied them in the category Cpo. They were subsequently generalised to arbitrary categories by Atkey, Ghani, Jacobs, and Johann in a FoSSaCS 2012 paper. In this article we aim to introduce the general concept of initial f-and-m-algebras to a general functional programming audience

Complexity bounds for container functors and comonads

The notion of containers, due to Abbott et al., characterises a subset of parametric data types which can be described by a set of shapes and a set of positions for each shape. This includes common data types such as tuples, lists, trees, arrays, and graphs. Various useful categorical structures can be derived for containers that have some additional structure on their shapes and positions. For example, the notion of a directed container (due to Ahman et al.) gives rise to container comonads. Containers, and refinements such as directed containers, provide a useful reasoning tool for data types and an abstraction mechanism for programming, e.g., building libraries parameterised over containers. This paper studies the performance characteristics of traversal schemes over containers modelled by additional functor and comonad structure. A cost model for container transformations is defined from which complexity bounds for the operations of container functors and comonads are derived. This provides a reasoning principle for the performance of programs structured using these idioms, suggesting optimisations which follow from the underling mathematical structure. Due to the abstract interface provided by the syntax of containers and category theory, the complexity bounds and subsequent optimisations they imply are implementation agnostic (machine free). As far as we are aware, this is the first such study of the performance characteristics of containers

Interleaving data and effects

The study of programming with and reasoning about inductive datatypes such as lists and trees has benefited from the simple categorical principle of initial algebras. In initial algebra semantics, each inductive datatype is represented by an initial f-algebra for an appropriate functor f. The initial algebra principle then supports the straightforward derivation of definitional principles and proof principles for these datatypes. This technique has been expanded to a whole methodology of structured functional programming, often called origami programming. In this article we show how to extend initial algebra semantics from pure inductive datatypes to inductive datatypes interleaved with computational effects. Inductive datatypes interleaved with effects arise naturally in many computational settings. For example, incrementally reading characters from a file generates a list of characters interleaved with input/output actions, and lazily constructed infinite values can be represented by pure data interleaved with the possibility of non-terminating computation. Straightforward application of initial algebra techniques to effectful datatypes leads either to unsound conclusions if we ignore the possibility of effects, or to unnecessarily complicated reasoning because the pure and effectful concerns must be considered simultaneously. We show how pure and effectful concerns can be separated using the abstraction of initial f-and-m-algebras, where the functor f describes the pure part of a datatype and the monad m describes the interleaved effects. Because initial f-and-m-algebras are the analogue for the effectful setting of initial f-algebras, they support the extension of the standard definitional and proof principles to the effectful setting. Initial f-and-m-algebras are originally due to Filinski and Støvring, who studied them in the category Cpo. They were subsequently generalised to arbitrary categories by Atkey, Ghani, Jacobs, and Johann in a FoSSaCS 2012 paper. In this article we aim to introduce the general concept of initial f-and-m-algebras to a general functional programming audience