Efficient and Type-Safe Generic Data Storage

AbstractIn this paper we present an elegant method for sequentializing arbitrary data using the generic language extension of the functional programming language Clean. We show how the proposed operations can be used to store values of any concrete data type in several kinds of IO containers (such as files or arrays of characters), and how to manipulate stored data efficiently. Moreover, by extending stored data with encoded type information, data manipulation will be type-safe. Defining these operations generically has the advantage that specific instances for user defined data types can be generated fully automatically. Compared to traditional sequentialization methods (or to common data manipulation, using relational data bases) our operations are an order of magnitude faster

Formal Component-Based Semantics

One of the proposed solutions for improving the scalability of semantics of programming languages is Component-Based Semantics, introduced by Peter D. Mosses. It is expected that this framework can also be used effectively for modular meta theoretic reasoning. This paper presents a formalization of Component-Based Semantics in the theorem prover Coq. It is based on Modular SOS, a variant of SOS, and makes essential use of dependent types, while profiting from type classes. This formalization constitutes a contribution towards modular meta theoretic formalizations in theorem provers. As a small example, a modular proof of determinism of a mini-language is developed.Comment: In Proceedings SOS 2011, arXiv:1108.279

Improved fusion for optimizing generics

Abstract. Generic programming is accepted by the functional programming community as a valuable tool for program development. Several functional languages have adopted the generic scheme of type-indexed values. This scheme works by specialization of a generic function to a concrete type. However, the generated code is extremely inefficient compared to its hand-written counterpart. The performance penalty is so big that the practical usefulness of generic programming is compromised. In this paper we present an optimization algorithm that is able to completely eliminate the overhead introduced by the specialization scheme for a large class of generic functions. The presented technique is based on consumer–producer elimination as exploited by fusion, a standard general purpose optimization method. We show that our algorithm is able to optimize many practical examples of generic functions