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Séance de cours

Programmation Data-Parallel: Bases et Mandelbrot Set

Dans cours

CS-206: Parallelism and concurrency

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Description

Cette séance de cours introduit la programmation data-parallèle comme une forme de parallélisation distribuant des processus d'exécution à travers les nœuds informatiques. Il couvre le parallèle pour la boucle, l'initialisation des valeurs du tableau, et le rendu de l'ensemble Mandelbrot en utilisant la programmation data-parallel. L'instructeur démontre le calcul de l'ensemble Mandelbrot, les types de charge de travail (uniformes et irréguliers) et l'objectif du programmeur parallèle de données.

Enseignant·e·s (3)

Viktor Kuncak

Viktor Kunčak joined EPFL in 2007, after receiving a PhD degree from MIT. Since then has been leading the Laboratory for Automated Reasoning and Analysis and supervised at least 12 completed PhD theses. His works on languages, algorithms and systems for verification and automated reasoning. He served as an initiator and one of the coordinators of a European network (COST action) in the area of automated reasoning, verification, and synthesis. In 2012 he received a 5-year single-investigator European Research Council (ERC) grant of 1.5M EUR. His invited talks include those at Lambda Days, Scala Days, NFM, LOPSTR, SYNT, ICALP, CSL, RV, VMCAI, and SMT. A paper on test generation he co-authored received an ACM SIGSOFT distinguished paper award at ICSE. A PLDI paper he co-authored was published in the Communications of the ACM as a Research Highlight article. His Google Scholar profile reports an over-approximate H-index of 38. He was an associate editor of ACM Transactions on Programming Languages and Systems (TOPLAS) and served as a co-chair of conferences on Computer-Aided Verification (CAV), Formal Methods in Computer Aided Design (FMCAD), Workshop on Synthesis (SYNT), and Verification, Model Checking, and Abstract Interpretation (VMCAI). At EPFL he teaches courses on functional and parallel programming, compilers, and verification. He has co-taught the MOOC "Parallel Programming" that was visited by over 100'000 learners and completed by thousands of students from all over the world.

Sanidhya Kashyap

Sanidhya Kashyap is a systems researcher and an assistant professor at the EPFL School of Computer and Communication Sciences (IC). His research focuses on designing systems software that are performant and robust for heterogeneous hardware. He is broadly interested in the area of systems with a particular focus on operating systems, databases, concurrency, scheduling, networks, data analytics, and software testing (fuzzing).

Martin Odersky

Martin Odersky heads the programming research group at EPFL. His research interests cover fundamental as well as applied aspects of programming languages. They include semantics, type systems, programming language design, and compiler construction. The main focus if his work lies in the integration of object-oriented and functional programming. His research thesis is that the two paradigms are just two sides of the same coin and should be unified as much as possible. To prove this he has experimented a number of language designs, from Pizza to GJ to Functional Nets. He has also influenced the development of Java as a co-designer of Java generics and as the original author of the current javac reference compiler. His current work concentrates on the Scala programming language, which unifies FP and OOP, while staying completely interoperable with Java and .NET. Martin Odersky got his doctorate from ETHZ, in 1989. He held research positions at the IBM T.J. Watson Research Center from 1989 and at Yale University from 1991. He was then a professor at the University of Karlsruhe from 1993 and at the University of South Australia from 1997. He joined EPFL as full professor in 1999. He is associate editor of the Journal of Functional Programming and member of IFIP WG 2.8. He was conference chair for ICFP 2000, and program chair for ECOOP 2004 as well as ETAPS/CC 2007.

Source officielle

Concepts associés (32)

Parallélisme (informatique)

vignette|upright=1|Un des éléments de Blue Gene L cabinet, un des supercalculateurs massivement parallèles les plus rapides des années 2000. En informatique, le parallélisme consiste à mettre en œuvre des architectures d'électronique numérique permettant de traiter des informations de manière simultanée, ainsi que les algorithmes spécialisés pour celles-ci. Ces techniques ont pour but de réaliser le plus grand nombre d'opérations en un temps le plus petit possible.

Parallélisme de donnée

Le parallélisme par distribution de donnée ou parallélisme de donnée (data parallelism en anglais) est un paradigme de la programmation parallèle. Autrement dit, c'est une manière particulière d'écrire des programmes pour des machines parallèles. Les algorithmes des programmes qui entrent dans cette catégorie cherchent à distribuer les données au sein des processus et à y opérer les mêmes opérations à l'instar des SIMD. Le paradigme opposé est celui du parallélisme de tâche. Catégorie:Programmation concurr

Ensemble de Mandelbrot

En mathématiques, lensemble de Mandelbrot est une fractale définie comme l'ensemble des points c du plan complexe pour lesquels la suite de nombres complexes définie par récurrence par : est bornée. alt=Représentation de l'ensemble de Mandelbrot|vignette|L'ensemble de Mandelbrot (en noir) L'ensemble de Mandelbrot a été découvert par Gaston Julia et Pierre Fatou avant la Première Guerre mondiale. Sa définition et son nom actuel sont dus à Adrien Douady, en hommage aux représentations qu'en a réalisées Benoît Mandelbrot dans les années 1980.

Parallel programming model

In computing, a parallel programming model is an abstraction of parallel computer architecture, with which it is convenient to express algorithms and their composition in programs. The value of a programming model can be judged on its generality: how well a range of different problems can be expressed for a variety of different architectures, and its performance: how efficiently the compiled programs can execute. The implementation of a parallel programming model can take the form of a library invoked from a sequential language, as an extension to an existing language, or as an entirely new language.

Map (parallel pattern)

Map is an idiom in parallel computing where a simple operation is applied to all elements of a sequence, potentially in parallel. It is used to solve embarrassingly parallel problems: those problems that can be decomposed into independent subtasks, requiring no communication/synchronization between the subtasks except a join or barrier at the end. When applying the map pattern, one formulates an elemental function that captures the operation to be performed on a data item that represents a part of the problem, then applies this elemental function in one or more threads of execution, hyperthreads, SIMD lanes or on multiple computers.

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Séances de cours associées (30)

Principes du parallélisme

Couvre les bases du parallélisme, y compris des exemples physiques, le contexte historique, l'ère multicore, et des collections parallèles dans Scala.

Opérations Data-Parallel: Programmation Scala

Couvre les opérations parallèles de données dans la programmation Scala, en mettant l'accent sur les opérations de repliement et l'exploitation agrégée.

Conc-Trees: Programmation parallèle en Scala

Couvre la mise en œuvre de Conc-Trees à Scala pour la programmation parallèle.

Opérations parallélistes de données: Partie 1

Explore les opérations parallèles de données dans Scala, la conversion des collections, la mise en œuvre de la somme et l'examen des opérations de plis.

Opérations de données et cartographie parallèle

Explore la programmation parallèle dans Scala, les opérations fonctionnelles sur les collections, et les fonctions de carte parallèle sur les tableaux et les arbres.

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Concepts associés (32)

Parallélisme (informatique)

Parallélisme de donnée

Ensemble de Mandelbrot

Parallel programming model

Map (parallel pattern)

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