2,657 research outputs found
Two Techniques for Compiling Lazy Pattern Matching
Projet PARAIn ML~style pattern matching, pattern size is not constrained and ambiguous patterns are allowed. This generality leads to a clear and concise programming style but is challenging in the context of lazy evaluation. A first challenge concerns language designers: in lazy ML, the evaluation order of expressions follows actual data dependencies. That is, only the computations that are needed to produce the final result are performed. Once given a proper (that is, non-ambiguous) semantics, pattern matching should be compiled in a similar spirit: any value matching a given pattern should be recognized by performing only the minimal number of elementary tests needed to do so. This challenge was first met by A.~Laville. A second challenge concerns compiler designers. As it stands, LavillƩs compilation algorithm cannot be incorporated in an actual lazy ML compiler for efficiency and completeness reasons. As a matter of fact, LavillƩs original algorithm did not fully treat the case of integers in patterns and can lead to explosions both in compilation time and generated code size. This paper provides a complete solution to that second challenge. In particular, the well-known (and size-efficient) pattern matching compilation technique using backtracking automata is here introduced for the first time into the world of lazy pattern matching
Functional programming languages for verification tools: experiences with ML and Haskell
We compare Haskell with ML as programming languages for verification tools, based on our experience developing TRUTH in Haskell and the Edinburgh Concurrency Workbench (CWB) in ML. We discuss not only technical language features but also the "worlds" of the languages, for example, the availability of tools and libraries
Common Subexpression Elimination in a Lazy Functional Language
Common subexpression elimination is a well-known compiler optimisation that saves time by avoiding the repetition of the same computation. To our knowledge it has not yet been applied to lazy functional programming languages, although there are several advantages. First, the referential transparency of these languages makes the identification of common subexpressions very simple. Second, more common subexpressions can be recognised because they can be of arbitrary type whereas standard common subexpression elimination only shares primitive values. However, because lazy functional languages decouple program structure from data space allocation and control flow, analysing its effects and deciding under which conditions the elimination of a common subexpression is beneficial proves to be quite difficult. We developed and implemented the transformation for the language Haskell by extending the Glasgow Haskell compiler and measured its effectiveness on real-world programs
Functional Big-step Semantics
When doing an interactive proof about a piece of software, it is important that the underlying programming languageās semantics does not make the proof unnecessarily difficult or unwieldy. Both smallstep and big-step semantics are commonly used, and the latter is typically given by an inductively defined relation. In this paper, we consider an alternative: using a recursive function akin to an interpreter for the language. The advantages include a better induction theorem, less duplication, accessibility to ordinary functional programmers, and the ease of doing symbolic simulation in proofs via rewriting. We believe that this style of semantics is well suited for compiler verification, including proofs of divergence preservation. We do not claim the invention of this style of semantics: our contribution here is to clarify its value, and to explain how it supports several language features that might appear to require a relational or small-step approach. We illustrate the technique on a simple imperative language with C-like for-loops and a break statement, and compare it to a variety of other approaches. We also provide ML and lambda-calculus based examples to illustrate its generality
Logic programming in the context of multiparadigm programming: the Oz experience
Oz is a multiparadigm language that supports logic programming as one of its
major paradigms. A multiparadigm language is designed to support different
programming paradigms (logic, functional, constraint, object-oriented,
sequential, concurrent, etc.) with equal ease. This article has two goals: to
give a tutorial of logic programming in Oz and to show how logic programming
fits naturally into the wider context of multiparadigm programming. Our
experience shows that there are two classes of problems, which we call
algorithmic and search problems, for which logic programming can help formulate
practical solutions. Algorithmic problems have known efficient algorithms.
Search problems do not have known efficient algorithms but can be solved with
search. The Oz support for logic programming targets these two problem classes
specifically, using the concepts needed for each. This is in contrast to the
Prolog approach, which targets both classes with one set of concepts, which
results in less than optimal support for each class. To explain the essential
difference between algorithmic and search programs, we define the Oz execution
model. This model subsumes both concurrent logic programming
(committed-choice-style) and search-based logic programming (Prolog-style).
Instead of Horn clause syntax, Oz has a simple, fully compositional,
higher-order syntax that accommodates the abilities of the language. We
conclude with lessons learned from this work, a brief history of Oz, and many
entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic
Programming
Relay: A New IR for Machine Learning Frameworks
Machine learning powers diverse services in industry including search,
translation, recommendation systems, and security. The scale and importance of
these models require that they be efficient, expressive, and portable across an
array of heterogeneous hardware devices. These constraints are often at odds;
in order to better accommodate them we propose a new high-level intermediate
representation (IR) called Relay. Relay is being designed as a
purely-functional, statically-typed language with the goal of balancing
efficient compilation, expressiveness, and portability. We discuss the goals of
Relay and highlight its important design constraints. Our prototype is part of
the open source NNVM compiler framework, which powers Amazon's deep learning
framework MxNet
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