document: A few studies within the physiological domain are of special relevance to this work. These include a performance analysis of a blood-flow LB solver using a range of sparse and non-sparse geometries [21] and a performance prediction model for lattice-Boltzmann solvers [22,23]. This performance prediction model can be applied largely to our HemeLB application, although HemeLB uses a different decomposition technique and performs real-time rendering and visualisation tasks during the LB simulations. Mazzeo and Coveney [1] studied the scalability of an earlier version of HemeLB. However, the current performance characteristics of HemeLB are substantially enhanced due to numerous subsequent advances in the code, amongst others: an improved hierarchical, compressed file format; the use of ParMETIS to ensure good load-balance; the coalesced communication patterns to reduce the overhead of rendering; use of compile-time polymorphism to avoid virtual function calls in inner loops.
extractive_keyphrases: coalesced communication patterns hemelb lb simulations parmetis performance prediction model rendering
abstractive_keyphrases: blood-flow lb solver compile-time polymorphism hemelb application lattice-boltzmann solvers