Circumventing Storage Limitations in Variational Data Assimilation Studies
SIAM Journal on Scientific Computing
Evaluating derivatives: principles and techniques of algorithmic differentiation
Evaluating derivatives: principles and techniques of algorithmic differentiation
Sourcebook of parallel computing
An automatic differentiation platform: odyssée
Future Generation Computer Systems
Automatic differentiation of explicit Runge-Kutta methods for optimal control
Computational Optimization and Applications
Adjoint concepts for the optimal control of Burgers equation
Computational Optimization and Applications
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Towards the construction of a standard adjoint GEOS-Chem model
SpringSim '09 Proceedings of the 2009 Spring Simulation Multiconference
An automatic differentiation platform: Odyssée
Future Generation Computer Systems
New Algorithms for Optimal Online Checkpointing
SIAM Journal on Scientific Computing
SIAM Journal on Control and Optimization
Optimal checkpointing for time-stepping procedures in ADOL-C
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Sensitivity analysis of limit cycle oscillations
Journal of Computational Physics
Structural and Multidisciplinary Optimization
Computing the sparsity pattern of Hessians using automatic differentiation
ACM Transactions on Mathematical Software (TOMS)
Output-based mesh adaptation for high order Navier-Stokes simulations on deformable domains
Journal of Computational Physics
Discrete adjoints of PETSc through dco/c++ and adjoint MPI
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
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In its basic form, the reverse mode of computational differentiation yields the gradient of a scalar-valued function at a cost that is a small multiple of the computational work needed to evaluate the function itself. However, the corresponding memory requirement is proportional to the run-time of the evaluation program. Therefore, the practical applicability of the reverse mode in its original formulation is limited despite the availability of ever larger memory systems. This observation leads to the development of checkpointing schedules to reduce the storage requirements. This article presents the function revolve, which generates checkpointing schedules that are provably optimal with regard to a primary and a secondary criterion. This routine is intended to be used as an explicit “controller” for running a time-dependent applications program.