Event-based parareal: A data-flow based implementation of parareal

Berry, L. A.; Elwasif, Wael; Reynolds-Barredo, J. M.; Samaddar, Debasmita; Sánchez, Raúl; Newman, D. E.

doi:10.1016/j.jcp.2012.05.016

Cited by 30 publications

(33 citation statements)

References 12 publications

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“…It is finally worthwhile mentioning that all BETA simulations performed for this work have been carried out using the event-based, parareal module [13] recently added to the Integrated Plasma Simulator (IPS) framework, currently under development at ORNL by the U.S. fusion community [14,15]. IPS is a framework that allows, among other things, the fast implementation of parareal in an efficient, user-friendly way.…”

Section: Numerical Aspects Of Betamentioning

confidence: 99%

Mechanisms for the convergence of time-parallelized, parareal turbulent plasma simulations

Reynolds-Barredo

Newman

Sánchez

et al. 2012

Journal of Computational Physics

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Section: Numerical Aspects Of Betamentioning

confidence: 99%

Mechanisms for the convergence of time-parallelized, parareal turbulent plasma simulations

Reynolds-Barredo

Newman

Sánchez

et al. 2012

Journal of Computational Physics

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“…The IPS framework written in Python [33,34,35] launches an event-based parareal application using MPI. In the traditional technique, for every parareal iteration k, all processors need to undergo 'mpi-wait' until computations for every time slice is completed for a given k. This significantly increases wall-clock time.…”

Section: Event-based Parareal With Ips Frameworkmentioning

confidence: 99%

“…The framework allows the use of 'event-based parareal' [35] which significantly improves performance and is discussed in more detail in sections 2.3 and 3.3. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Temporal parallelization of edge plasma simulations using the parareal algorithm and the SOLPS code

Samaddar

Coster

Bonnin

et al. 2017

Computer Physics Communications

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“…The mean of the solution at equilibrium is higher in the c = −1 case than in the c = −0.1 case. (12) in Section IV's algorithm applied to the Kuramoto-Sivashinsky equation (13) at c = −1.…”

Section: Validation On the Ks Equationmentioning

confidence: 99%

Towards scalable parallel-in-time turbulent flow simulations

et al. 2013

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We present a reformulation of unsteady turbulent flow simulations. The initial condition is relaxed and information is allowed to propagate both forward and backward in time. Simulations of chaotic dynamical systems with this reformulation can be proven to be well-conditioned time domain boundary value problems. The reformulation can enable scalable parallel-in-time simulation of turbulent flows. I. NEED FOR SPACE-TIME PARALLELISMThe use of computational fluid dynamics (CFD) in science and engineering can be categorized into Analysis and Design. A CFD Analysis performs a simulation on a set of manually picked parameter values. The flow field is then inspected to gain understanding of the flow physics. Scientific and engineering decisions are then made based on understanding of the flow field. Analysis based on high fidelity turbulent flow simulations, particular Large Eddy Simulations, is a rapidly growing practice in complex engineering applications 12 .CFD based Design goes beyond just performing individual simulations, towards sensitivity analysis, optimization, control, uncertainty quantification and data based inference. Design is enabled by Analysis capabilities, but often requires more rapid turnaround. For example, an engineer designer or an optimization software needs to perform a series of simulations, modifying the geometry based on previous simulation results. Each simulation must complete within at most a few hours in an industrial design environment. Most current practices of design use steady state CFD solvers, employing RANS (Reynolds Averaged NavierStokes) models for turbulent flows. Design using high fidelity, unsteady turbulent flow simulations has been investigated in academia 3 . Despite their great potential, high fidelity design is infeasible in an industrial setting because each simulation typically takes days to weeks. FIG. 1:Exponential increase of high performance computing power, primarily sustained by increased parallelism in the past decade. Data originate from top500.org. GFLOPS, TFLOPS, PFLOPS and EFLOPS represent 10 9 , 10 12 , 10 15 and 10 18 FLoating point Operations Per Second, respectively.The inability of performing high fidelity turbulent flow simulations in short turnaround time is a barrier to the game-changing technology of high fidelity CFD-based design. Nevertheless, development in High Performance Computing (HPC), as shown in Figure 1, promises to delivery in about ten years computing hardware a thousand times faster than those available today. This will be achieved through extreme scale a) Corresponding

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Event-based parareal: A data-flow based implementation of parareal

Cited by 30 publications

References 12 publications

Mechanisms for the convergence of time-parallelized, parareal turbulent plasma simulations

Mechanisms for the convergence of time-parallelized, parareal turbulent plasma simulations

Temporal parallelization of edge plasma simulations using the parareal algorithm and the SOLPS code

Towards scalable parallel-in-time turbulent flow simulations

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