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Séance de cours

Problème de philosophe de salle à manger

Dans cours

CS-206: Parallelism and concurrency

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Description

Cette séance de cours traite du problème du Philosophe de la salle à manger, où les philosophes assis autour d'une table ont besoin de 2 fourchettes à manger. L'instructeur présente un algorithme pour prévenir la famine et maximiser le nombre de philosophes qui mangent simultanément.

Enseignant·e·s (3)

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.

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.

Source officielle

Concepts associés (32)

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

Dîner des philosophes

Le problème du « dîner des philosophes » est un cas d'école classique sur le partage de ressources en informatique système. Il concerne l'ordonnancement des processus et l'allocation des ressources à ces derniers et a été énoncé par Edsger Dijkstra. vignette|Illustration du problème La situation est la suivante: Cinq philosophes (initialement, mais il peut y en avoir beaucoup plus) se trouvent autour d'une table; Chacun des philosophes a devant lui un plat de spaghettis; À gauche de chaque plat de spaghettis se trouve une fourchette.

Task parallelism

Task parallelism (also known as function parallelism and control parallelism) is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing tasks—concurrently performed by processes or threads—across different processors. In contrast to data parallelism which involves running the same task on different components of data, task parallelism is distinguished by running many different tasks at the same time on the same data.

Interblocage

thumb|Exemple d'interblocage : le processus P1 utilise la ressource R2 qui est attendue par le processus P2 qui utilise la ressource R1, attendue par P1. Un interblocage (ou étreinte fatale, deadlock en anglais) est un phénomène qui peut survenir en programmation concurrente. L'interblocage se produit lorsque des processus concurrents s'attendent mutuellement. Un processus peut aussi s'attendre lui-même. Les processus bloqués dans cet état le sont définitivement, il s'agit donc d'une situation catastrophique.

Concurrency control

In information technology and computer science, especially in the fields of computer programming, operating systems, multiprocessors, and databases, concurrency control ensures that correct results for concurrent operations are generated, while getting those results as quickly as possible. Computer systems, both software and hardware, consist of modules, or components. Each component is designed to operate correctly, i.e., to obey or to meet certain consistency rules.

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Séances de cours associées (47)

Bases de la devises: Fil et synchronisation

Couvre les bases de la concurrence, se concentrant sur les threads et la synchronisation, y compris l'assemblage des threads et les défis de l'exécution non déterministe.

Moniteurs : Synchronisation et coopération

Explore les moniteurs comme une construction de synchronisation fournissant l'exclusion mutuelle et la coopération entre les threads, avec des exemples comme le tampon d'un seul endroit.

Opérations atomiques : notions de base et applications

Couvre les bases des opérations atomiques dans la proximité et le parallélisme, y compris les opérations fournies par le matériel et les algorithmes synchronisés.

Modèles de mémoire: Visibilité et sémantique

Explique le modèle Java Memory et le thread démarrent la sémantique pour les programmes simultanés.

Cohérence séquentielle : Concepts de base

Explique le modèle de cohérence séquentiel dans la concordance et le parallélisme, couvrant l'ordre des programmes, l'atomicité et les implications de performance.

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Concepts associés (32)

Parallélisme de donnée

Dîner des philosophes