The future of high-performance computing, specifically the next generation of Exascale computers, will presumably see memory capacity and bandwidth fail to keep pace with data generation. Current strategies proposed to address this bottleneck entail the omission of large fractions of data, as well as the incorporation of in situ compression algorithms to avoid overuse of memory. To ensure that post-processing operations are successful, this must be done in a way that ensures that a sufficiently accurate representation of the solution is stored. Moreover, in situations in which the input/output system becomes a bottleneck in analysis, visualization, etc., the number of passes made over the input data must be minimized. In the interest of addressing this problem, this work focuses on the application of pass-efficient compressive matrix decompositions to high-dimensional simulation data from turbulent particle-laden flows. It also includes the presentation of a novel single-pass matrix decomposition algorithm for computing interpolative decompositions. The methods are extensively described and numerical experiments at Re τ = 180 and St + = 0, 1, 10 are performed. In the unladen channel flow case, compression factors exceeding 400 are achieved while maintaining accuracy with respect to first-and second-order flow statistics. In the particle-laden case, compression factors between 10 and 150 with relative reconstruction errors of O(10 −3 ) are achieved. This result shows that these methods can enable efficient computation of various quantities of interest in both the carrier and disperse phases. These algorithms are easily parallelized and can be incorporated directly into solvers, which will allow for effective in situ compression of large-scale simulation data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.