Langage dédié

Résumé

Un langage dédié (en anglais, domain-specific language ou DSL) est un langage de programmation dont les spécifications sont conçues pour répondre aux contraintes d’un domaine d'application précis. Il s'oppose conceptuellement aux langages de programmation classiques (ou généralistes) comme Java ou C, qui tendent à traiter un ensemble de domaines. Néanmoins, aucun consensus ne définit précisément ce qu'est un langage dédié. Ce manque de définition précise sur la nature d'un langage dédié rend délicate la tâche d'établir un historique clair sur l'origine du concept. En informatique, les langages dédiés traitent divers domaines informatiques (pilotes informatiques, le calcul scientifique, les bases de données) ou traitent de différents autres domaines où intervient l'informatique (médecine, aéronautique). L'utilisation d'un langage dédié n'est pas propre à l'informatique, il existe des langages dédiés aux domaines de la médecine, de la cui

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https://fr.wikipedia.org/wiki/Langage_d%C3%A9di%C3%A9

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Cours associés (73)

"In silico Neuroscience" introduces students to a synthesis of modern neuroscience and state-of-the-art data management, modelling and computing technologies.

Understanding of the principles and applications of functional programming, the fundamental models of program execution, application of fundamental methods of program composition, meta-programming through the construction of interpreters and advanced programming techniques.

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.

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Séances de cours associées (99)

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A building specific, economic building stock model to evaluate energy efficiency and renewable energy

Claudio Nägeli

LESO-PB, EPFL2015

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Better Loop Fusion for LMS

Véra Salvisberg

This is my master thesis done at PPL in Stanford under the supervision of Prof. Kunle Olukotun. It improved LMS, a framework for embedding DSLs (domain-specific languages) into Scala which features many general optimizations that can be used by any DSLs for free. I implemented a more powerful and cleaner version of the loop fusion optimization from the compiler world. Loop fusion is an important performance optimization for all languages that feature list comprehensions and translate their high-level operations into loop-based representations. It can decrease runtime, memory footprint and code size through two different fusion cases: The simpler one is called horizontal or side-by-side fusion and fuses adjacent loops iterating over the same range, enabling further optimizations. The second one is vertical or pipeline fusion, where a producer and a consumer of data are fused, removing the need for the intermediate data structure.

2014

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EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation

Wei Dai

Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations have made it all the more attractive. At the same time, FHE is notoriously hard to use with a very constrained programming model, a very unusual performance profile, and many cryptographic constraints. Existing compilers for FHE either target simpler but less efficient FHE schemes or only support specific domains where they can rely on expert-provided high-level runtimes to hide complications. This paper presents a new FHE language called Encrypted Vector Arithmetic (EVA), which includes an optimizing compiler that generates correct and secure FHE programs, while hiding all the complexities of the target FHE scheme. Bolstered by our optimizing compiler, programmers can develop efficient general-purpose FHE applications directly in EVA. For example, we have developed image processing applications using EVA, with a very few lines of code. EVA is designed to also work as an intermediate representation that can be a target for compiling higher-level domain-specific languages. To demonstrate this, we have re-targeted CHET, an existing domain-specific compiler for neural network inference, onto EVA. Due to the novel optimizations in EVA, its programs are on average 5.3x faster than those generated by CHET. We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.

ASSOC COMPUTING MACHINERY2020

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