Enabling In Situ Pre- and Post-processing for Exascale Hemodynamic Simulations - A Co-design Study with the Sparse Geometry Lattice-Boltzmann Code HemeLB

摘要

Today's fluid simulations deal with complex geometries and numerical data on an extreme scale. As computation approaches the exascale, it will no longer be possible to write and store the full-sized data set. textit{In situ} data analysis and scientific visualisation provide feasible solutions to the analysis of complex large scaled CFD simulations. To bring pre- and post-processing to the exascale we must consider modifications to data structure and memory layout, and address latency and error resiliency. In this respect, a particular challenge is the exascale data processing for the sparse geometry lattice-Boltzmann code HemeLB, intended for hemodynamic simulations. In this paper, we assess the needs and challenges of HemeLB users and sketch a co-design infrastructure and system architecture for pre- and post-processing the simulation data. To enable in situ data visualisation and analysis during a running simulation, post-processing needs to work on a reduced subset of the original data. Particular choices of data structure and visualisation techniques need to be co-designed with the application scientists in order to achieve efficient and interactive data processing and analysis. In this work, we focus on the hierarchical data structure and suitable visualisation techniques which provide possible solutions to interactive in situ data processing at exascale. Architectural challenges and road-maps will be presented as the major focus of this paper. We sketch a software architecture which integrates pre- and post-processing techniques that can provide in situ analysis and ultimately computational steering to HemeLB.