Scalability of partial differential equations preconditioner resilient to soft and hard faults

Karla Morris; Francesco Rizzi; Khachik Sargsyan; Kathryn Dahlgren; Paul Mycek; Cosmin Safta; Olivier Le Maître; Omar Knio; Bert Debusschere

doi:10.1007/978-3-319-41321-1_24

Scalability of partial differential equations preconditioner resilient to soft and hard faults

Karla Morris
, Francesco Rizzi
, Khachik Sargsyan
, Kathryn Dahlgren
, Paul Mycek
, Cosmin Safta
, Olivier Le Maître
, Omar Knio
, Bert Debusschere

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

We present a resilient domain-decomposition preconditioner for partial differential equations (PDEs). The algorithm reformulates the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to both soft and hard faults. We discuss an implementation based on a server-client model where all state information is held by the servers, while clients are designed solely as computational units. Servers are assumed to be “sandboxed”, while no assumption is made on the reliability of the clients. We explore the scalability of the algorithm up to ∼12k cores, build an SST/macro skeleton to extrapolate to∼50k cores, and show the resilience under simulated hard and soft faults for a 2D linear Poisson equation.

langue originale	Anglais
titre	High Performance Computing - 31st International Conference, ISC High Performance 2016, Proceedings
rédacteurs en chef	Jack Dongarra, Julian M. Kunkel, Pavan Balaji
Editeur	Springer Verlag
Pages	469-485
Nombre de pages	17
ISBN (imprimé)	9783319413204
Les DOIs	https://doi.org/10.1007/978-3-319-41321-1_24
état	Publié - 1 janv. 2016
Evénement	31st International Conference on High Performance Computing, ISC High Performance 2016 - Frankfurt, Allemagne Durée: 19 juin 2016 → 23 juin 2016

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9697
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	31st International Conference on High Performance Computing, ISC High Performance 2016
Pays/Territoire	Allemagne
La ville	Frankfurt
période	19/06/16 → 23/06/16

Accès au document

10.1007/978-3-319-41321-1_24

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Contient cette citation

Morris, K., Rizzi, F., Sargsyan, K., Dahlgren, K., Mycek, P., Safta, C., Le Maître, O., Knio, O., & Debusschere, B. (2016). Scalability of partial differential equations preconditioner resilient to soft and hard faults. Dans J. Dongarra, J. M. Kunkel, & P. Balaji (eds.), High Performance Computing - 31st International Conference, ISC High Performance 2016, Proceedings (p. 469-485). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 9697). Springer Verlag. https://doi.org/10.1007/978-3-319-41321-1_24

Morris, Karla ; Rizzi, Francesco ; Sargsyan, Khachik et al. / Scalability of partial differential equations preconditioner resilient to soft and hard faults. High Performance Computing - 31st International Conference, ISC High Performance 2016, Proceedings. Editeur / Jack Dongarra ; Julian M. Kunkel ; Pavan Balaji. Springer Verlag, 2016. p. 469-485 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "We present a resilient domain-decomposition preconditioner for partial differential equations (PDEs). The algorithm reformulates the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to both soft and hard faults. We discuss an implementation based on a server-client model where all state information is held by the servers, while clients are designed solely as computational units. Servers are assumed to be “sandboxed”, while no assumption is made on the reliability of the clients. We explore the scalability of the algorithm up to ∼12k cores, build an SST/macro skeleton to extrapolate to∼50k cores, and show the resilience under simulated hard and soft faults for a 2D linear Poisson equation.",

author = "Karla Morris and Francesco Rizzi and Khachik Sargsyan and Kathryn Dahlgren and Paul Mycek and Cosmin Safta and \{Le Ma{\^i}tre\}, Olivier and Omar Knio and Bert Debusschere",

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Morris, K, Rizzi, F, Sargsyan, K, Dahlgren, K, Mycek, P, Safta, C, Le Maître, O, Knio, O & Debusschere, B 2016, Scalability of partial differential equations preconditioner resilient to soft and hard faults. Dans J Dongarra, JM Kunkel & P Balaji (eds), High Performance Computing - 31st International Conference, ISC High Performance 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 9697, Springer Verlag, p. 469-485, 31st International Conference on High Performance Computing, ISC High Performance 2016, Frankfurt, Allemagne, 19/06/16. https://doi.org/10.1007/978-3-319-41321-1_24

Scalability of partial differential equations preconditioner resilient to soft and hard faults. / Morris, Karla; Rizzi, Francesco; Sargsyan, Khachik et al.
High Performance Computing - 31st International Conference, ISC High Performance 2016, Proceedings. Ed. / Jack Dongarra; Julian M. Kunkel; Pavan Balaji. Springer Verlag, 2016. p. 469-485 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 9697).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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AU - Morris, Karla

AU - Rizzi, Francesco

AU - Sargsyan, Khachik

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AU - Safta, Cosmin

AU - Le Maître, Olivier

AU - Knio, Omar

AU - Debusschere, Bert

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N2 - We present a resilient domain-decomposition preconditioner for partial differential equations (PDEs). The algorithm reformulates the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to both soft and hard faults. We discuss an implementation based on a server-client model where all state information is held by the servers, while clients are designed solely as computational units. Servers are assumed to be “sandboxed”, while no assumption is made on the reliability of the clients. We explore the scalability of the algorithm up to ∼12k cores, build an SST/macro skeleton to extrapolate to∼50k cores, and show the resilience under simulated hard and soft faults for a 2D linear Poisson equation.

AB - We present a resilient domain-decomposition preconditioner for partial differential equations (PDEs). The algorithm reformulates the PDE as a sampling problem, followed by a solution update through data manipulation that is resilient to both soft and hard faults. We discuss an implementation based on a server-client model where all state information is held by the servers, while clients are designed solely as computational units. Servers are assumed to be “sandboxed”, while no assumption is made on the reliability of the clients. We explore the scalability of the algorithm up to ∼12k cores, build an SST/macro skeleton to extrapolate to∼50k cores, and show the resilience under simulated hard and soft faults for a 2D linear Poisson equation.

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Morris K, Rizzi F, Sargsyan K, Dahlgren K, Mycek P, Safta C et al. Scalability of partial differential equations preconditioner resilient to soft and hard faults. Dans Dongarra J, Kunkel JM, Balaji P, rédacteurs en chef, High Performance Computing - 31st International Conference, ISC High Performance 2016, Proceedings. Springer Verlag. 2016. p. 469-485. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-41321-1_24

Scalability of partial differential equations preconditioner resilient to soft and hard faults

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