Managing application complexity in the SAMRAI object-oriented framework. A major challenge facing software libraries for scientific computing is the ability to provide adequate flexibility to meet sophisticated, diverse, and evolving application requirements. Object-oriented design techniques are valuable tools for capturing characteristics of complex applications in a software architecture. In this paper, we describe certain prominent object-oriented features of the SAMRAI software library that have proven to be useful in application development. SAMRAI is used in a variety of applications and has demonstrated a substantial amount of code and design re-use in those applications. This flexibility and extensibility is illustrated with three different application codes. We emphasize two important features of our design. First, we describe the composition of complex numerical algorithms from smaller components which are usable in different applications. Second, we discuss the extension of existing framework components to satisfy new application needs.

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  1. Sætra, Martin L.; Brodtkorb, André R.; Lie, Knut-Andreas: Efficient GPU-implementation of adaptive mesh refinement for the shallow-water equations (2015)
  2. Bhalla, Amneet Pal Singh; Bale, Rahul; Griffith, Boyce E.; Patankar, Neelesh A.: Fully resolved immersed electrohydrodynamics for particle motion, electrolocation, and self-propulsion (2014)
  3. Dickopf, Thomas; Krause, Dorian; Krause, Rolf; Potse, Mark: Design and analysis of a lightweight parallel adaptive scheme for the solution of the monodomain equation (2014)
  4. Bhalla, Amneet Pal Singh; Bale, Rahul; Griffith, Boyce E.; Patankar, Neelesh A.: A unified mathematical framework and an adaptive numerical method for fluid-structure interaction with rigid, deforming, and elastic bodies (2013)
  5. Hittinger, J.A.F.; Banks, J.W.: Block-structured adaptive mesh refinement algorithms for Vlasov simulation (2013)
  6. Griffith, Boyce E.: Immersed boundary model of aortic heart valve dynamics with physiological driving and loading conditions (2012)
  7. Kamkar, S.J.; Wissink, A.M.; Sankaran, V.; Jameson, A.: Feature-driven Cartesian adaptive mesh refinement for vortex-dominated flows (2011)
  8. Thornburg, Jonathan: Adaptive mesh refinement for characteristic grids (2011)
  9. Lee, Pilhwa; Griffith, Boyce E.; Peskin, Charles S.: The immersed boundary method for advection-electrodiffusion with implicit timestepping and local mesh refinement (2010)
  10. Wang, Ping: Modeling material responses by arbitrary Lagrangian Eulerian formulation and adaptive mesh refinement method (2010)
  11. Bordas, Rafel; Carpentieri, Bruno; Fotia, Giorgio; Maggio, Fabio; Nobes, Ross; Pitt-Francis, Joe; Southern, James: Simulation of cardiac electrophysiology on next-generation high-performance computers (2009)
  12. Nourgaliev, R.R.; Liou, M.-S.; Theofanous, T.G.: Numerical prediction of interfacial instabilities: Sharp interface method (SIM) (2008)
  13. Philip, Bobby; Chacón, Luis; Pernice, Michael: Implicit adaptive mesh refinement for 2D reduced resistive magnetohydrodynamics (2008)
  14. Daphalapurkar, Nitin P.; Lu, Hongbing; Coker, Demir; Komanduri, Ranga: Simulation of dynamic crack growth using the generalized interpolation material point (GIMP) method (2007)
  15. Fattebert, J.-L.; Hornung, R.D.; Wissink, A.M.: Finite element approach for density functional theory calculations on locally-refined meshes (2007)
  16. Griffith, Boyce E.; Hornung, Richard D.; McQueen, David M.; Peskin, Charles S.: An adaptive, formally second order accurate version of the immersed boundary method (2007)
  17. Jung, Y.; Chu, K.T.; Torquato, S.: A variational level set approach for surface area minimization of triply-periodic surfaces (2007)
  18. Nordén, Markus; Löf, Henrik; Rantakokko, Jarmo; Holmgren, Sverker: Dynamic data migration for structured AMR solvers (2007)
  19. Nourgaliev, R.R.; Theofanous, T.G.: High-fidelity interface tracking in compressible flows: unlimited anchored adaptive level set (2007)
  20. Gunney, Brian T.N.; Wissink, Andrew M.; Hysom, David A.: Parallel clustering algorithms for structured AMR (2006)

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