Analysing and modelling the performance of the HemeLB lattice-Boltzmann simulation environment

Abstract

We investigate the performance of the HemeLB lattice-Boltzmann simulator for cerebrovascular blood flow, aimed at providing timely and clinically relevant assistance to neurosurgeons. HemeLB is optimised for sparse geometries, supports interactive use, and scales well to 32,768 cores for problems with ∼81 million lattice sites. We obtain a maximum performance of 29.5 billion site updates per second, with only an 11% slowdown for highly sparse problems (5% fluid fraction). We present steering and visualisation performance measurements and provide a model which allows users to predict the performance, thereby determining how to run simulations with maximum accuracy within time constraints.

A framework for hybrid parallel flow simulations with a trillion cells in complex geometries

“…Performance results of cell-structured LBM simulations are reported in [27,30,4], while a performance analysis and model is presented by Axner et al [2]. Groen et al [17] present performance results for the cell-structured HemeLB framework on vascular geometries on supercomputers HeCTOR and SuperMUC using up to 32,768 cores.…”

waLBerla: A block-structured high-performance framework for multiphysics simulations

“…It therefore offers a wide range of state-ofthe-art LBM models, together with a variety of utility and usability functionality. In this regard, it is comparable to other LBM frameworks such as OpenLB [6,7], Palabos [8,9], elbe [10,11], LB3D [12,13,14], HemeLB [15], and Sailfish [16]. Other LBM codes focus on HPC implementations of the method targeted at specific hardware architectures or a particular collision operator, like SunwayLB [17] or the LBM benchmark kernel suite [18].…”

An automated multiscale ensemble simulation approach for vascular blood flow

“…It is a MPI parallelised C++ code with world-class scalability for sparse geometries. It can efficiently model flows in sparse cerebral arteries using up to 32,768 cores [22,23] and utilises a weighted domain decomposition approach to minimize the overhead introduced by compute-intensive boundary and in-/outflow conditions [8]. HemeLB allows users to obtain key flow properties such as velocity, pressure and wall shear at predefined intervals of time, using a property-extraction framework.…”