SUNDIALS was implemented with the goal of providing robust time integrators and nonlinear solvers that can easily be incorporated into existing simulation codes. The primary design goals were to require minimal information from the user, allow users to easily supply their own data structures underneath the solvers, and allow for easy incorporation of user-supplied linear solvers and preconditioners. The main numerical operations performed in these codes are operations on data vectors, and the codes have been written in terms of interfaces to these vector operations. The result of this design is that users can relatively easily provide their own data structures to the solvers by telling the solver about their structures and providing the required operations on them. The codes also come with default vector structures with pre-defined operation implementations for both serial and distributed memory parallel environments in case a user prefers not to supply their own structures. In addition, all parallelism is contained within specific vector operations (norms, dot products, etc.) No other operations within the solvers require knowledge of parallelism. Thus, using a solver in parallel consists of using a parallel vector implementation, either the one provided with SUNDIALS, or the user’s own parallel vector structure, underneath the solver. Hence, we do not make a distinction between parallel and serial versions of the codes.

References in zbMATH (referenced in 145 articles , 1 standard article )

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  1. Constantinescu, Emil M.: Generalizing global error estimation for ordinary differential equations by using coupled time-stepping methods (2018)
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  3. Herajy, Mostafa; Liu, Fei; Heiner, Monika: Efficient modelling of yeast cell cycles based on multisite phosphorylation using coloured hybrid Petri nets with marking-dependent arc weights (2018)
  4. Lasagna, Davide: Sensitivity analysis of chaotic systems using unstable periodic orbits (2018)
  5. Oliver Laslett, Jonathon Waters, Hans Fangohr, Ondrej Hovorka: Magpy: A C++ accelerated Python package for simulating magnetic nanoparticle stochastic dynamics (2018) arXiv
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  8. Einkemmer, Lukas; Tokman, Mayya; Loffeld, John: On the performance of exponential integrators for problems in magnetohydrodynamics (2017)
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  11. Melicher, Valdemar; Haber, Tom; Vanroose, Wim: Fast derivatives of likelihood functionals for ODE based models using adjoint-state method (2017)
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