Affectation (informatique)

En algorithmique et en programmation informatique, une affectation, aussi appelée assignation par anglicisme, est une structure qui permet d'attribuer une valeur à une variable. Il s'agit d'une structure particulièrement courante en programmation impérative, et dispose souvent pour cette raison d'une notation courte et infixée, comme ou . Dans certains langages, le symbole est considéré comme un opérateur d'affectation, et la structure entière peut alors être utilisée comme une expression. D'autres langages considèrent une affectation comme une instruction et ne permettent pas cet usage. Notation La structure d'affectation est le plus souvent notée ou . D'autres langages utilisent une notation en forme de flèche ou un mot-clé. Certains langages placent l'expression à gauche de l'opérateur, et l'identificateur à droite. Certains langages, comme Lisp ou Tcl, utilisent une notation préfixée. Notes et références Catégorie:Programmation informa

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Theory and Practice of Coroutines with Snapshots

Fengyun Liu, Aleksandar Prokopec

While event-driven programming is a widespread model for asynchronous computing, its inherent control flow fragmentation makes event-driven programs notoriously difficult to understand and maintain. Coroutines are a general control flow construct that can eliminate control flow fragmentation. However, coroutines are still missing in many popular languages. This gap is partly caused by the difficulties of supporting suspendable computations in the language runtime. We introduce first-class, type-safe, stackful coroutines with snapshots, which unify many variants of suspendable computing. Our design relies solely on the static metaprogramming support of the host language, without modifying the language implementation or the runtime. We also develop a formal model for type-safe, stackful and delimited coroutines, and we prove the respective safety properties. We show that the model is sufficiently general to express iterators, single-assignment variables, async-await, actors, event streams, backtracking, symmetric coroutines and continuations. Performance evaluations reveal that the proposed metaprogramming-based approach has a decent performance, with workload-dependent overheads of 1.03-2.11 x compared to equivalent manually written code, and improvements of up to 6 x compared to other approaches.

2018

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StagedSAC: a case study in performance-oriented DSL development

Hassan Chafi, Martin Odersky, Tiark Rompf, Vlad Ureche

Domain-specific languages (DSLs) can bridge the gap between high-level programming and efficient execution. However, implementing compiler tool-chains for performance oriented DSLs requires significant effort. Recent research has produced methodologies and frameworks that promise to reduce this development effort by enabling quick transition from library-only, purely embedded DSLs to optimizing compilation. In this case study we report on our experience implementing a compiler for StagedSAC. StagedSAC is a DSL for arithmetic processing with multidimensional arrays modeled after the stand-alone language SAC (Single Assignment C). The main language feature of both SAC and StagedSAC is a loop construction that enables high-level and concise implementations of array algorithms. At the same time, the functional semantics of the two languages allow for advanced compiler optimizations and parallel code generation. We describe how we were able to quickly evolve from a pure library DSL to a performance-oriented compiler with a good speedup and only minor syntax changes using the technique of Lightweight Modular Staging. We also describe the optimizations we perform to obtain fast code and how we plan to generate parallel code with minimal effort using the Delite framework.

ACM2011

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Optimal target assignment for massive spectroscopic surveys

Denis Gillet, Jean-Paul Richard Kneib, Matin Macktoobian

Robotics have recently contributed to cosmological spectroscopy to automatically obtain the map of the observable universe using robotic fiber positioners. For this purpose, an assignment algorithm is required to assign each robotic fiber positioner to a target associated with a particular observation. The assignment process directly impacts on the coordination of robotic fiber positioners to reach their assigned targets. In this paper, we establish an optimal target assignment scheme which simultaneously provides the fastest coordination accompanied with the minimum of colliding scenarios between robotic fiber positioners. In particular, we propose a cost function by whose minimization both of the cited requirements are taken into account in the course of a target assignment process. The applied simulations manifest the improvement of convergence rates using our optimal approach. We show that our algorithm scales the solution in quadratic time in the case of full observations. Additionally, the convergence time and the percentage of the colliding scenarios are also decreased in both supervisory and hybrid coordination strategies.

2020

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