The Essence of Dataflow Programming

Uustalu, Tarmo; Vene, Varmo

doi:10.1007/11894100_5

Cited by 24 publications

(30 citation statements)

References 35 publications

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“…-functors and their fmap operations-covers any kind of labelled datastructures that support relabeling; -free completely iterative monads-non-wellfounded leaf-labelled trees with grafting and (a specific flavor of) iteration; -cofree recursive comonads-wellfounded node-labelled trees with upward accumulation and recursion; -non-empty list type-a special case of the previous item; we get effortless efficiency for comonadically structured causal dataflow computation [25].…”

Section: Other Generalizationsmentioning

confidence: 99%

Explicit Binds: Effortless Efficiency with and without Trees

Uustalu

2012

Functional and Logic Programming

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Abstract. We demonstrate a simple and robust program transformation technique that can improve asymptotic time complexity of datamanipulating programs (e.g., produce a linear-time list reversal function from the obvious quadratic one). In the version of the present paper, it applies to monadic inductive datatypes and can be stated in two flavors, through a datatype representation, with an explicit ("frozen") bind constructor and a special associated defining clause for the fold function, and in a functional form (generalized Church numerals), with a special definition of the bind function in terms of the build constructor. The technique explicates, systematizes, combines and scales a number of ideas known from the literature, achieving a new level of generality.

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Section: Other Generalizationsmentioning

confidence: 99%

Explicit Binds: Effortless Efficiency with and without Trees

Uustalu

2012

Functional and Logic Programming

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“…Apart from arrows, other formalisms such as monads, comonads, and applicative functors have been used to model computations over data streams (Bjesse et al, 1998;Uustalu & Vene, 2005;McBride & Paterson, 2008). Central to many of these approaches are the representation of streams and computations about them.…”

Section: Alternative Formalismsmentioning

confidence: 99%

“…However, notably missing are the connections between stream computation and the related laws. For example, Uustalu and Vene (2005) concluded that comonad is a suitable model for dataflow computation, but it lacks the discussion on comonadic laws.…”

Section: Alternative Formalismsmentioning

confidence: 99%

Causal commutative arrows

Liu¹,

Cheng²,

Hudak³

2011

J. Funct. Prog.

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Arrows are a popular form of abstract computation. Being more general than monads, they are more broadly applicable, and, in particular, are a good abstraction for signal processing and dataflow computations. Most notably, arrows form the basis for a domain-specific language called Yampa, which has been used in a variety of concrete applications, including animation, robotics, sound synthesis, control systems, and graphical user interfaces. Our primary interest is in better understanding the class of abstract computations captured by Yampa. Unfortunately, arrows are not concrete enough to do this with precision. To remedy this situation, we introduce the concept of commutative arrows that capture a noninterference property of concurrent computations. We also add an init operator that captures the causal nature of arrow effects, and identify its associated law. To study this class of computations in more detail, we define an extension to arrows called causal commutative arrows (CCA), and study its properties. Our key contribution is the identification of a normal form for CCA called causal commutative normal form (CCNF). By defining a normalization procedure, we have developed an optimization strategy that yields dramatic improvements in performance over conventional implementations of arrows. We have implemented this technique in Haskell, and conducted benchmarks that validate the effectiveness of our approach. When compiled with the Glasgow Haskell Compiler (GHC), the overall methodology can result in significant speedups.

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“…An alternative way to look at streams is modelling them as comonads [13]. However, comonads need extra machinery to be able to express applicative combinators, which come for free given the monadic interface.…”

Section: Closing Thoughtsmentioning

confidence: 99%

Efficient and Compositional Higher-Order Streams

Patai

2011

Functional and Constraint Logic Programming

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Abstract. Stream-based programming has been around for a long time, but it is typically restricted to static data-flow networks. By introducing first-class streams that implement the monad interface, we can describe arbitrary dynamic networks in an elegant and consistent way using only two extra primitives besides the monadic operations. This paper presents an efficient stream implementation and demonstrates the compositionality of the constructs by mapping them to functions over natural numbers.

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The Essence of Dataflow Programming

Cited by 24 publications

References 35 publications

Explicit Binds: Effortless Efficiency with and without Trees

Explicit Binds: Effortless Efficiency with and without Trees

Causal commutative arrows

Efficient and Compositional Higher-Order Streams

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