2016
DOI: 10.1016/j.procs.2016.05.444
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Asynchronous Two-level Checkpointing Scheme for Large-scale Adjoints in the Spectral-Element Solver Nek5000

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Cited by 19 publications
(11 citation statements)
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“…Nonlinear optimal perturbations were calculated with nek5000 using a gradient-based optimisation algorithm that relies on integrating backward in time the adjoint equations to compute the gradient (Pringle & Kerswell 2010). A checkpointing and revolve strategy was adopted in order to limit the memory requirements (Schanen et al 2016). The initial condition is updated at each step of the optimization using the gradient rotation algorithm (Foures et al 2013).…”
Section: Turbulent Kinetic Energy Budgetmentioning
confidence: 99%
“…Nonlinear optimal perturbations were calculated with nek5000 using a gradient-based optimisation algorithm that relies on integrating backward in time the adjoint equations to compute the gradient (Pringle & Kerswell 2010). A checkpointing and revolve strategy was adopted in order to limit the memory requirements (Schanen et al 2016). The initial condition is updated at each step of the optimization using the gradient rotation algorithm (Foures et al 2013).…”
Section: Turbulent Kinetic Energy Budgetmentioning
confidence: 99%
“…Therefore large memory or space (∼10 4 GB in the present case) is required when solving the adjoint. Note that this constraint on memory can be mitigated by implementing a checkpointing scheme at the cost of extra calls of equations (2.4) (Schanen et al 2016). Considering the sign of the viscous term and the time derivative term of this adjoint equation, it should be integrated backwards from t = T to t = 0.…”
Section: Optimal Inflow Perturbationmentioning
confidence: 99%
“…Computed in reverse order of the primal computation, the solution at every time‐step must be recovered for each adjoint time‐step, requiring a careful combination of memory check‐pointing, disk check‐pointing, and recomputation. Nek5000 has recently incorporated a novel adjoint check‐pointing scheme that uses all system resources, memory, disks, and archive, proving that adjoint computations of nonlinear problems are feasible at large scale …”
Section: Contributions Algorithm and Implementationmentioning
confidence: 99%