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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Laros, James H.; Rizzi, Francesco N.; Sargsyan, Khachik S.; Dahlgren, Kathryn; Mycek, Paul; Safta, Cosmin S.; Le Maitre, Olivier; Knio, Omar; Debusschere, Bert D.

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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TYPE Other Report YEAR 2016
DOIOSTIScopus

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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Laros, James H.; Rizzi, Francesco N.; Sargsyan, Khachik S.; Dahlgren, Kathryn; Mycek, Paul; Safta, Cosmin S.; Le Maitre, Olivier; Knio, Omar; Debusschere, Bert D.

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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TYPE Conference Poster YEAR 2016
DOIOSTIScopus

Partial differential equations preconditioner resilient to soft and hard faults

Proceedings - IEEE International Conference on Cluster Computing, ICCC

Rizzi, Francesco N.; Laros, James H.; Sargsyan, Khachik S.; Mycek, Paul; Safta, Cosmin S.; Le Maitre, Olivier; Knio, Omar; Debusschere, Bert D.

We present a domain-decomposition-based pre-conditioner for the solution of partial differential equations (PDEs) that is resilient to both soft and hard faults. The algorithm is based on the following steps: first, the computational domain is split into overlapping subdomains, second, the target PDE is solved on each subdomain for sampled values of the local current boundary conditions, third, the subdomain solution samples are collected and fed into a regression step to build maps between the subdomains' boundary conditions, finally, the intersection of these maps yields the updated state at the subdomain boundaries. This reformulation allows us to recast the problem as a set of independent tasks. The implementation relies on an asynchronous server-client framework, where one or more reliable servers hold the data, while the clients ask for tasks and execute them. This framework provides resiliency to hard faults such that if a client crashes, it stops asking for work, and the servers simply distribute the work among all the other clients alive. Erroneous subdomain solves (e.g. due to soft faults) appear as corrupted data, which is either rejected if that causes a task to fail, or is seamlessly filtered out during the regression stage through a suitable noise model. Three different types of faults are modeled: hard faults modeling nodes (or clients) crashing, soft faults occurring during the communication of the tasks between server and clients, and soft faults occurring during task execution. We demonstrate the resiliency of the approach for a 2D elliptic PDE, and explore the effect of the faults at various failure rates.

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TYPE Conference Poster YEAR 2015
DOIOSTIScopus

Calibration and Forward Uncertainty Propagation for Large-eddy Simulations of Engineering Flows

Templeton, Jeremy A.; Blaylock, Myra L.; Domino, Stefan P.; Hewson, John C.; Kumar, Pritvi R.; Ling, Julia L.; Najm, H.N.; Ruiz, Anthony R.; Safta, Cosmin S.; Sargsyan, Khachik S.; Stewart, Alessia; Wagner, Gregory

The objective of this work is to investigate the efficacy of using calibration strategies from Uncertainty Quantification (UQ) to determine model coefficients for LES. As the target methods are for engineering LES, uncertainty from numerical aspects of the model must also be quantified. 15 The ultimate goal of this research thread is to generate a cost versus accuracy curve for LES such that the cost could be minimized given an accuracy prescribed by an engineering need. Realization of this goal would enable LES to serve as a predictive simulation tool within the engineering design process.

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TYPE SAND Report YEAR 2015
DOIOSTI
Results 151–175 of 272
Results 151–175 of 272