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# Arepo wiki









Arepo is a massively parallel code for gravitational nbody systems and



magnetohydrodynamics, both on Newtonian as well as cosmological background.



It is a flexible code that can be applied to a variety of different types of



simulations, offering a number of sophisticated simulation algorithms. A



description of the numerical algorithms employed by the code is given in the



public release paper.



For a more detailed discussion about these algorithms, the original code paper



and subsequent publications are the best resource. This documentation only



addresses the question how to use the different numerical algorithms.






Arepo was written by Volker Springel (vspringel@mpagarching.mpg.de) with further



development by Rüdiger Pakmor (rpakmor@mpagarching.mpg.de) and contributions by



many other authors (www.arepocode.org/people).



The public version of the code was compiled by Rainer Weinberger



(rainer.weinberger@cfa.harvard.edu).



Arepo is a massively parallel code for gravitational Nbody systems



and magnetohydrodynamics, both on Newtonian as well as cosmological



backgrounds. It is a flexible code that can be applied to a variety



of different types of problems, offering a number of sophisticated



simulation algorithms. A description of the numerical algorithms



employed by the code is given in the public release code paper. For a



more in depth discussion about these algorithms, the original code



paper and subsequent publications are the best resource. This



documentation only addresses the question how to use the different



numerical algorithms.






Arepo was written by Volker Springel (vspringel@mpagarching.mpg.de)



with further development by Rüdiger Pakmor



(rpakmor@mpagarching.mpg.de) and contributions by many other authors



(www.arepocode.org/people). The public version of the code was



compiled by Rainer Weinberger (rainer.weinberger@cfa.harvard.edu).






Overview



========






The Arepo code was initially developed to combine the advantages of



finitevolume hydrodynamics schemes with the Lagrangian invariance of



smoothed particle hydrodynamics (SPH) schemes. To this end, Arepo makes use of an



unstructured Voronoimesh which is, in its standard setting, moving with



the fluid in an quasiLagrangian fashion. The fluxes between cells are computed



using a finitevolume approach, and further spatial adaptivity is



provided by the possibility to add and remove cells from the



mesh according to defined criteria. In addition to gas, Arepo allows for a



number of additional particle types which interact only gravitationally, as



well as for forces from external gravitational potentials.






Arepo is optimized for, but not limited to, cosmological simulations of galaxy



formation and consequently for simulations with very high dynamic ranges in



space and time. Therefore, Arepo employs an adaptive timestepping for each



individual cell and particle as well as a dynamic load and memory



balancing scheme. In its current version, Arepo is fully MPI parallel, and tested



to run with >10,000 MPI tasks. The exact performance is, however, highly problem and



machine dependent.



finitevolume hydrodynamics with the Lagrangian convenience of



smoothed particle hydrodynamics (SPH). To this end, Arepo makes use of



an unstructured Voronoimesh which is, in the standard mode of



operating the code, moving with the fluid in a quasiLagrangian



fashion. The fluxes between cells are computed using a finitevolume



approach, and additional spatial adaptivity is provided by the



possibility to add and remove cells from the mesh according to



userdefined criteria. In addition to gas dynamics, Arepo allows for



additional collisionless particle types which interact only



gravitationally. Besides selfgravity, forces from external



gravitational potentials can also be included.






Arepo is optimized for, but not limited to, cosmological simulations



of galaxy formation, and consequently can used for simulations with



high dynamic range in space and time. This is in particular supported



by Arepo's ability to employ adaptive local timestepping for each



individual cell or particle, as well as a dynamic load and memory



balancing domain decomposition. The current version of Arepo is fully



MPI parallel, and has been tested in runs with >10,000 MPI tasks. The



exact performance is, however, highly problem and machinedependent.









Disclaimer



==========






It is important to note that the performance and accuracy of the code is a



sensitive function of some of the code parameters. We also stress that Arepo



comes without any warranty, and without any guarantee that it produces correct



results. If in doubt about something, reading (and potentially improving) the



source code is always the best strategy to understand what is going on!



It is important to note that the performance and accuracy of the code



is a sensitive function of some of the code parameters. We also stress



that Arepo comes without any warranty, and without any guarantee that



it produces correct results. If in doubt about something, reading (and



potentially improving) the source code is always the best strategy to



understand what is going on!






**Please also note the following:**






The numerical parameter values used in the examples contained in the code



distribution do not represent a specific recommendation by the authors! In



particular, we do not endorse these parameter settings in any way as standard



values, nor do we claim that they will provide fully converged results for the



example problems, or for other initial conditions. We think that it is extremely



difficult to make general statements about what accuracy is sufficient for



certain scientific goals, especially when one desires to achieve it with the



smallest possible computational effort. For this reason we refrain from making



The numerical parameter values used in the examples contained in the



code distribution do not represent a specific recommendation by the



authors! In particular, we do not endorse these parameter settings in



any way as standard values, nor do we claim that they will provide



fully converged results for the example problems, or for other initial



conditions. We think that it is extremely difficult to make general



statements about what accuracy is sufficient for certain scientific



goals, especially when one desires to achieve it with the smallest



possible computational effort. For this reason we refrain from making



such recommendations. We encourage every simulator to find out for



herself/himself what integration settings are needed to achieve sufficient



accuracy for the system under study. We strongly recommend to make convergence



and resolution studies to establish the range of validity and the uncertainty of



any numerical result obtained with Arepo. 


herself/himself what integration settings are needed to achieve



sufficient accuracy for the system under study. We strongly recommend



to make convergence and resolution studies to establish the range of



validity and the uncertainty of any numerical result obtained with



Arepo. 


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