93 research outputs found
Type Inference Builds a Short Cut to Deforestation
No full textDeforestation optimises a functional program by transforming it into another one that does not create certain intermediate data structures. Short cut deforestation is a deforestation method which is based on a single, local transformation rule. In return, short cut deforestation expects both producer and consumer of the intermediate structure in a certain form. Warm fusion was proposed to automatically transform functions into this form. Unfortunately, it is costly and hard to implement. Starting from the fact that short cut deforestation is based on a parametricity theorem of the second-order typed lambda-calculus, we show how the required form of a list producer can be derived through the use of type inference. Typability for the second-order typed lambda-calculus is undecidable. However, we present a linear-time algorithm that solves a partial type inference problem and that, together with controlled inlining and polymorphic type instantiation, suffices for deforestation. The resulting new short cut deforestation algorithm is efficient and removes more intermediate lists than the original
IFL 2020: Proceedings of the 32nd Symposium on Implementation and Application of Functional Languages
No full textImplementation and Application of Functional Languages: 19th International Workshop, IFL 2007, Freiburg, Germany, September 27-29, 2007. Revised Selected Papers
No full textThis book constitutes the thoroughly refereed post-proceedings of the 19th International Workshop on Implementation and Applications of Functional Languages, IFL 2007, held in Freiburg, Germany in September 2007.
The 15 revised full papers presented went through two rounds of reviewing and improvement and were selected from 33 submissions. The papers address all current theoretical and methodological issues on functional and function-based languages such as type checking, contract checking, compilation, parallelism, development and debugging, data structures, parsing as well as various performance related concepts
Source-Based Trace Exploration
No full textHat is a programmer's tool for generating a trace of a computation of a Haskell 98 program and viewing such a trace in various different ways. Applications include program comprehension and debugging. The trace viewing tools show expressions and equations of a computation, but they hardly refer to the source program. This disregard of the program is odd, because the computation follows from the program and the usually familiar source program can help orientation in a complex computation. Hence I started the development of new trace viewing tools that are based on showing the source program with various changing markings
PPDP '14: Proceedings of the 16th International Symposium on Principles and Practice of Declarative Programming
No full textStructure and Properties of Traces for Functional Programs
No full textThe tracer Hat records in a detailed trace the computation of a program written in the lazy functional language Haskell. The trace can then be viewed in various ways to support program comprehension and debugging. The trace was named the augmented redex trail. Its structure was inspired by standard graph rewriting implementations of functional languages. Here we describe a model of the trace that captures its essential properties and allows formal reasoning. The trace is a graph constructed by graph rewriting but goes beyond simple term graphs. Although the trace is a graph whose structure is independent of any rewriting strategy, we define the trace inductively, thus giving us a powerful method for proving its properties
Deforestation of Functional Programs through Type Inference
No full textDeforestation optimises a functional program by transforming it into another one that does not create certain intermediate data structures. Short cut deforestation is a deforestation method which is based on a single, local transformation rule. In return, short cut deforestation expects both producer and consumer of the intermediate structure in a certain form. Starting from the fact that short cut deforestation is based on a parametricity theorem of the second-order typed lambda-calculus, we show how the required form of a list producer can be derived through the use of type inference. Type inference can also indicates which function definitions need to be inlined. Because only limited inlining across module boundaries is practically feasible, we develop a scheme for splitting a function definition into a worker definition and a wrapper definition. For deforestation we only need to inline the small wrapper definition
Type-Inference Based Short Cut Deforestation (nearly) without Inlining
No full textDeforestation optimises a functional program by transforming it into another one that does not create certain intermediate data structures. In [ICFP'99] we presented a type-inference based deforestation algorithm which performs extensive inlining. However, across module boundaries only limited inlining is practically feasible. Furthermore, inlining is a non-trivial transformation which is therefore best implemented as a separate optimisation pass. To perform short cut deforestation (nearly) without inlining, Gill suggested to split definitions into workers and wrappers and inline only the small wrappers, which transfer the information needed for deforestation. We show that Gill's use of a function build limits deforestation and note that his reasons for using build do not apply to our approach. Hence we develop a more general worker/wrapper scheme without build. We give a type-inference based algorithm which splits definitions into workers and wrappers. Finally, we show that we can deforest more expressions with the worker/wrapper scheme than the algorithm with inlining
One Right Does Make a Wrong
No full textAlgorithmic debugging is a semi-automatic method for locating bugs in programs. An algorithmic debugger asks a user a series of questions about the intended behaviour of the program. Here we present two new methods that reduces the number of questions a user must answer to locate a bug. First, we describe a heuristic based on comparing computations of the same program with different inputs. Besides a computation that exhibits some erroneous behaviour, we use information from computations that produce correct results. The heuristic uses program slices to identify areas of code that are likely to be correct. Secondly, we describe a method of compressing the search tree that guides the questions of an algorithmic debugger. This compression is particularly successful when used in combination with our heuristic. Both heuristic and tree-compression are applicable to algorithmic debugging in general. We have implemented it for locating bugs in Haskell programs
Proving the Correctness of Algorithmic Debugging for Functional Programs
No full textThis paper formally presents a model of tracing for functional programs based on a small-step operational semantics. The model records the computation of a functional program in a graph which can be utilised for various purposes such as algorithmic debugging. The main contribution of this paper is to prove the correctness of algorithmic debugging for functional programs based on the model. Although algorithmic debugging for functional programs is implemented in several tracers such as Hat, the correctness has not been formally proved before. The difficulty of the proof is to find a suitable induction principle and a more general induction hypothesis
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