Generic Views on Data Types

Proceedings of the 2010 ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation

Holdermans

Jeuring

et al. 2010

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Datatype-generic programming increases program reliability by reducing code duplication and enhancing reusability and modularity. Several generic programming libraries for Haskell have been developed in the past few years. These libraries have been compared in detail with respect to expressiveness, extensibility, typing issues, etc., but performance comparisons have been brief, limited, and preliminary. It is widely believed that generic programs run slower than hand-written code. In this paper we present an extensive benchmarking suite for generic functions and analyze the potential for automatic code optimization at compilation time. Our benchmark confirms that generic programs, when compiled with the standard optimization flags of the Glasgow Haskell Compiler, are substantially slower than their hand-written counterparts. However, we also find that more advanced optimization capabilities of GHC can be used to further optimize generic functions, sometimes achieving the same efficiency as hand-written code.

Section: Optimization Flagsmentioning

confidence: 99%

Optimizing generics is easy!

Proceedings of the 2010 ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation

Holdermans

Jeuring

et al. 2010

Self Cite

“…So far we have kept these trees balanced, but other choices would be acceptable too. In fact, the balancing choice determines a generic view (Holdermans et al 2006). Different balancings might be more convenient for certain generic functions.…”

Section: Left-and Right-biased Encodingsmentioning

confidence: 99%

“…This abundance is partly caused by the lack of a clearly superior approach; each approach has its strengths and weaknesses, uses different implementation mechanisms, a different generic view (Holdermans et al 2006) (i.e. a different representation of datatypes), or focuses on solving a particular task.…”

Section: Introductionmentioning

confidence: 99%

Hierarchy in Generic Programming Libraries

Lecture Notes in Computer Science

Löh²

2015

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Abstract. Generic programming (GP) is a form of abstraction in programming languages that serves to reduce code duplication by exploiting the regular structure of algebraic datatypes. Several different approaches to GP in Haskell have surfaced, giving rise to the problem of code duplication across GP libraries. Given the original goals of GP, the is a rather unfortunate turn of events. Fortunately, we can convert between the different representations of each approach, which allows us to "borrow" generic functions from different approaches, avoiding the need to reimplement every generic function in every single GP library. In previous work we have shown how existing GP libraries relate to each other. In this paper we go one step further and advocate "hierarchical GP": through proper design of different GP approaches, each library can fit neatly in a hierarchy, greatly minimizing the amount of supporting infrastructure necessary for each approach, and allowing each library to be specific and concise, while eliminating code duplication overall. We introduce a new library for GP in Haskell intended to sit at the top of the "GP hierarchy". This library contains a lot of structural information, and is not intended to be used directly. Instead, it is a good starting point for generating generic representations for other libraries. This approach is also suitable for being the only library with native compiler support; all other approaches can be obtained from this one by simple conversion of representations in plain Haskell code.

“…As such, using a library with an explicit encoding of recursion (i.e. with a fixed-point view (Holdermans et al 2006) on data) suits us best. We can either pick regular (Van Noort et al 2008), a library which supports only regular datatypes, or multirec (Rodriguez Yakushev et al 2009), a generalisation of regular that supports mutually-recursive families of datatypes.…”

Section: Generic Programming For Regular Functorsmentioning

confidence: 99%

Generic representations of tree transformations

Bransen

Proceedings of the 9th ACM SIGPLAN Workshop on Generic Programming

2013

Applications which deal with editing of structured data over multiple iterations, such as structure editors, exercise assistants, or applications which support incremental computation, need to represent transformations between different versions of data. A general notion of "transformation" should be more informative than what is obtained by computing the difference between the old and the new term, as diff algorithms generally consider only insert, copy, and delete operations. Transformations, however, may involve swapping elements, or duplicating them, and a good representation of transformations should not involve unnecessary repetition of data, or lose shared structure between the old and new term.In this paper we take a detailed look at the notion of transformation on strongly-typed structures. Our transformations are datatype-generic, and thus can be applied to a large class of data structures. We focus on representing transformations in a way that maximally captures the common substructure between the old and new term. This is of particular importance to a specific class of applications: that of incremental computations which recompute information only for the parts of the tree that are affected by a transformation.We present three different approaches to the problem, of varying complexity, flexibility, and user-friendliness; different approaches might be better suited for one particular application area or another. We provide practical examples of how to encode transformations in each of the approaches, as well as suggestions on how to apply our solution to larger applications. All approaches use the same underlying generic programming library, and support transformations over families of mutually recursive datatypes.