Generic Haskell is an extension of Haskell that supports the construction of generic programs. During the development of several applications, such as an XML editor and compressor, we encountered a number of limitations with the existing (Classic) Generic Haskell language, as implemented by the current Generic Haskell compiler. Specifically,

A polytypic function is a function that can be instantiated on many data types to obtain data type specific functionality. Examples of polytypic functions are the functions that can be derived in Haskell, such as show , read , and ` '. More advanced examples are functions for digital searching, pattern matching, unification, rewriting, and structure editing. For each of these problems, we not only have to define polytypic functionality, but also a type-indexed data type: a data type that is constructed in a generic way from an argument data type. For example, in the case of digital searching we have to define a search tree type by induction on the structure of the type of search keys. This paper shows how to define type-indexed data types, discusses several examples of type-indexed data types, and shows how to specialize type-indexed data types. The approach has been implemented in Generic Haskell, a generic programming extension of the functional language Haskell.

Abstract We show how higher kinded generic programming can be represented faithfully within a dependently typed programming system. This development has been implemented using the Oleg system. The present work can be seen as evidence for our thesis that extensions of type systems can be done by programming within a dependently typed language, using data as codes for types. 1.

Generic Haskell is an extension of Haskell that supports the construction of generic programs. These lecture notes discuss three advanced generic programming applications: generic dictionaries, compressing XML documents, and the zipper: a data structure used to represent a tree together with a subtree that is the focus of attention, where that focus may move left, right, up or down the tree. When describing and implementing these examples, we will encounter some advanced features of Generic Haskell, such as type-indexed data types, dependencies between and generic abstractions of generic functions, adjusting a generic function using a default case, and generic functions with a special case for a particular constructor.

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.

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Abstract. The last decade has seen a number of approaches to datatype-generic programming: PolyP, Functorial ML, ‘Scrap Your Boilerplate’, Generic Haskell, ‘Generics for the Masses’, etc. The approaches vary in sophistication and target audience: some propose full-blown programming languages, some suggest libraries, some can be seen as categorical programming methods. In these lecture notes we compare the various approaches to datatype-generic programming in Haskell. We introduce each approach by means of example, and we evaluate it along different dimensions (expressivity, ease of use, etc). 1

Contents SKELL? 5 1.1 Generic programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 SKELL overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Installation 6 2.1 System requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Installing the binary distribution (Linux, Mac OS X) . . . . . . . . . . . . . . 6 2.3 Installing the binary distribution (Windows) . . . . . . . . . . . . . . . . . . . 7 2.4 Building from source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Running gh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.6 Command line flags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.7 General overview of compilation . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.8 Compiling and running the generated code . . . . . . . . . . . . . . . . . . . 9 SKELL: The Language 10 3.1 Special Parentheses . . . . . . . . . . . . . . . . . . . .

Generic functions are defined by induction on the structural representation of types. As a consequence, by defining just a single generic operation, one acquires this operation over any particular type. An instance on a specific type is generated by interpretation of the type's structure. A direct translation leads to extremely ine#cient code that involves many conversions between types and their structural representations. In this

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.