Abstract not found

Datatype-generic programming is defining functions that depend on the structure, or “shape”, of datatypes. It has been around for more than 10 years, and a lot of progress has been made, in particular in the lazy functional programming language Haskell. There are more than 10 proposals for generic programming libraries or language extensions for Haskell. To compare and characterize the many generic programming libraries in a typed functional language, we introduce a set of criteria and develop a generic programming benchmark: a set of characteristic examples testing various facets of datatype-generic programming. We have implemented the benchmark for nine existing Haskell generic programming libraries and present the evaluation of the libraries. The comparison is useful for reaching a common standard for generic programming, but also for a programmer who has to choose a particular approach for datatype-generic programming.

Dependent types provide a strong foundation for specifying and verifying rich properties of programs through type-checking. The earliest implementations combined dependency, which allows types to mention program variables; with type-level computation, which facilitates expressive specifications that compute with recursive functions over types. While many recent applications of dependent types omit the latter facility, we argue in this paper that it deserves more attention, even when implemented without dependency. In particular, the ability to use functional programs as specifications enables statically-typed metaprogramming: programs write programs, and static type-checking guarantees that the generating process never produces invalid code. Since our focus is on generic validity properties rather than full correctness verification, it is possible to engineer type inference systems that are very effective in narrow domains. As a demonstration, we present Ur, a programming language designed to facilitate metaprogramming with firstclass records and names. On top of Ur, we implement Ur/Web, a special standard library that enables the development of modern Web applications. Ad-hoc code generation is already in wide use in the popular Web application frameworks, and we show how that generation may be tamed using types, without forcing metaprogram authors to write proofs or forcing metaprogram users to write any fancy types.

A constraint programming system combines two essential components: a constraint solver and a search engine. The constraint solver reasons about satisfiability of conjunctions of constraints, and the search engine controls the search for solutions by iteratively exploring a disjunctive search tree defined by the constraint program. In this paper we give a monadic definition of constraint programming where the solver is defined as a monad threaded through the monadic search tree. We are then able to define search and search strategies as first class objects that can themselves be built or extended by composable search transformers. Search transformers give a powerful and unifying approach to viewing search in constraint programming, and the resulting constraint programming system is first class and extremely flexible.

Modular languages support generative type abstraction, ensuring that an abstract type is distinct from its representation, except inside the implementation where the two are synonymous. We show that this well-established feature is in tension with the non-parametric features of newer type systems, such as indexed type families and GADTs. In this paper we solve the problem by using kinds to distinguish between parametric and non-parametric contexts. The result is directly applicable to Haskell, which is rapidly developing support for type-level computation, but the same issues should arise whenever generativity and non-parametric features are combined.

Abstract. This practical tutorial introduces the features available in Haskell for writing parallel and concurrent programs. We first describe how to write semi-explicit parallel programs by using annotations to express opportunities for parallelism and to help control the granularity of parallelism for effective execution on modern operating systems and processors. We then describe the mechanisms provided by Haskell for writing explicitly parallel programs with a focus on the use of software transactional memory to help share information between threads. Finally, we show how nested data parallelism can be used to write deterministically parallel programs which allows programmers to use rich data types in data parallel programs which are automatically transformed into flat data parallel versions for efficient execution on multi-core processors. 1

Dependent types allow a programmer to express invariant properties of functions, such as the relationship between the input and output lengths of a list. Several “lightweight” approaches to dependent types have been proposed for existing systems, such as Haskell’s Generalised Algebraic Data Types or Type Families. Such approaches are lightweight in the sense that they require minimal modifications to existing systems. However, while these extensions are apparently simple, we find that we often run into limitations fairly quickly. In this paper we will explore these limitations, and show that a full dependent type system allows more straightforward implementation of simple invariants without restricting expressivity.

From the dawn of time, all derivatives of the classic Hindley-Milner type system have supported implicit generalisation of local letbindings. Yet, as we will show, for more sophisticated type systems implicit let-generalisation imposes a disproportionate complexity burden. Moreover, it turns out that the feature is very seldom used, so we propose to eliminate it. The payoff is a substantial simplification, both of the specification of the type system, and of its implementation.

We present a novel approach to regular, multi-dimensional arrays in Haskell. The main highlights of our approach are that it (1) is purely functional, (2) supports reuse through shape polymorphism, (3) avoids unnecessary intermediate structures rather than relying on subsequent loop fusion, and (4) supports transparent parallelisation. We show how to embed two forms of shape polymorphism into Haskell’s type system using type classes and type families. In particular, we discuss the generalisation of regular array transformations to arrays of higher rank, and introduce a type-safe specification of array slices. We discuss the runtime performance of our approach for three standard array algorithms. We achieve absolute performance comparable to handwritten C code. At the same time, our implementation scales well up to 8 processor cores. Categories and Subject Descriptors D.3.3 [Programming Languages]: Language Constructs and Features—Concurrent programming structures; Polymorphism; Abstract data types

We present MorphJ: a language for specifying general classes whose members are produced by iterating over members of other classes. We call this technique “class morphing ” or just “morphing”. Morphing extends the notion of genericity so that not only types of methods and fields, but also the structure of a class can vary according to type variables. This adds a disciplined form of meta-programming to mainstream languages and allows expressing common programming patterns in a highly generic way that is otherwise not supported by conventional techniques. For instance, morphing lets us write generic proxies (i.e., classes that can be parameterized with another class and export the same public methods as that class); default implementations (e.g., a generic do-nothing type, configurable for any interface); semantic extensions (e.g., specialized behavior for methods that declare a certain annotation); and more. MorphJ’s hallmark feature is that, despite its emphasis on generality, it allows modular type checking: a MorphJ class can be checked independently of its uses. Thus, the possibility of supplying a type parameter that will lead to invalid code is detected early—an invaluable feature for highly general components that will be statically instantiated by other programmers. We demonstrate the benefits of morphing with several examples, including a MorphJ reimplementation of DSTM2, a software transactional memory library, which reduces 1,484 lines of Java reflection and bytecode engineering library calls to just 586 lines of MorphJ code.