ML (langage)

ML (contraction de Meta Language) est un langage de programmation généraliste fonctionnel. Histoire ML fut initialement développé par Robin Milner et d'autres personnes dans les années 1970 à l'université d'Édimbourg, pour le système de preuves formelles (LCF). R. Milner rencontrait des difficultés avec le système de typage de Lisp qui permettait de « prouver » des assertions fausses. ML est un langage fonctionnel impur : il est possible de programmer en impératif et, en conséquence, les fonctions peuvent être sujettes à des effets secondaires non désirés (dits « de bord »), contrairement à des langages purement fonctionnels comme Haskell. ML a été standardisé en 1983, puis révisé en 1997 ; le langage résultant s'appelle Standard ML (SML), et a été notamment implémenté dans Standard ML of New Jersey (SML/NJ). En parallèle, des équipes françaises ont développé Caml, dont la popularité dans la communauté ML internationale est maintenant très grande. Le typage de ML a été

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Programmation fonctionnelle

La programmation fonctionnelle est un paradigme de programmation de type déclaratif qui considère le calcul en tant qu'évaluation de fonctions mathématiques. Comme le changement d'état et la mutation

Haskell

Haskell est un langage de programmation fonctionnel fondé sur le lambda-calcul et la logique combinatoire. Son nom vient du mathématicien et logicien Haskell Curry. Il a été créé en 1990 par un comité

C Sharp

C# (C sharp en anglais britannique) est un langage de programmation orientée objet, commercialisé par Microsoft depuis 2002 et destiné à développer sur la plateforme Microsoft .NET

Programming language abstractions for mobile code

Stéphane Micheloud

Scala is a general-purpose programming language developed at EPFL. It combines the most important concepts found in object-oriented and functional languages. Scala is a statically typed language; in particular it features an advanced type system and supports local type inference. Furthermore it integrates well with the Java and .net platforms: their libraries are accessible without glue code and the Scala compiler generates code for both execution environments. The Scala programming language has several features that make it desirable as a language for distributed application programming. In particular, it supports first-class functions which are useful in relation with the notions of distributed scope and code mobility. In that context, the missing support for run-time types is one important drawback of the Java run-time environment as a target platform. This thesis focuses on the realisation of a new concept combining essential notions from the functional and distributed programming and implying the extension of the notion of lexical scoping to the distributed context. In short, we claim that the notion of lambda abstraction provides an elegant way for dealing with the dynamic rebinding of local references in a distributed execution environment. The key ideas exposed in this research work have been implemented in our Scala compiler. This helped us to evaluate the used techniques, in particular their impact on the reliability and the performance of distributed programs. So far, most research works related to the present subject have focused on functional programming languages, in particular on the ML language family.

EPFL2009

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Finally, a Polymorphic Linear Algebra Language

Lionel Emile Vincent Parreaux, Amir Shaikhha

Many different data analytics tasks boil down to linear algebra primitives. In practice, for each different type of workload, data scientists use a particular specialised library. In this paper, we present Pilatus, a polymorphic iterative linear algebra language, applicable to various types of data analytics workloads. The design of this domain-specific language (DSL) is inspired by both mathematics and programming languages: its basic constructs are borrowed from abstract algebra, whereas the key technology behind its polymorphic design uses the tagless final approach (a.k.a. polymorphic embedding/object algebras). This design enables us to change the behaviour of arithmetic operations to express matrix algebra, graph algorithms, logical probabilistic programs, and differentiable programs. Crucially, the polymorphic design of Pilatus allows us to use multistage programming and rewrite-based optimisation to recover the performance of specialised code, supporting fixed sized matrices, algebraic optimisations, and fusion.

2019

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Translation Correctness for First-Order Object-Oriented Pattern Matching

Burak Emir, Martin Odersky

Pattern matching makes ML programs more concise and readable, and these qualities are also sought in object-oriented settings. However, objects and classes come with open class hierarchies, extensibility requirements and the need for data abstraction, which all conflict with matching on concrete data types. Extractor-based pattern matching has been proposed to address this conflict. Extractors are user-defined methods that perform the task of value discrimination and deconstruction during pattern matching. In this paper, we give the first formalization of extractor-based matching, using a first-order object-oriented calculus. We give a direct operational semantics and prove it sound. We then present an optimizing translation to a target language without matching, and prove a correctness result stating that an expression is equivalent to its translation.

2007

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CS-452: Foundations of software

The course introduces the foundations on which programs and programming languages are built. It introduces syntax, types and semantics as building blocks that together define the properties of a program part or a language. Students will learn how to apply these concepts in their reasoning.

CS-320: Computer language processing

We teach the fundamental aspects of analyzing and interpreting computer languages, including the techniques to build compilers. You will build a working compiler from an elegant functional language into the new web standard for portable binaries called WebAssembly ( https://webassembly.org/ )

ME-213: Programmation pour ingénieur

Mettre en pratique les bases de la programmation vues au semestre précédent. Développer un logiciel structuré. Méthode de debug d'un logiciel. Introduction à la programmation scientifique. Introduction à l'instrumentation virtuelle.