Obtaining a good load balance is a significant challenge in scaling up lattice-Boltzmann simulations of realistic sparse problems to the exascale. Here we analyze the effect of weighted decomposition on the performance of the HemeLB lattice-Boltzmann simulation environment, when applied to sparse domains. Prior to domain decomposition, we assign wall and in/outlet sites with increased weights which reflect their increased computational cost. We combine our weighted decomposition with a second optimization, which is to sort the lattice sites according to a space filling curve. We tested these strategies on a sparse bifurcation and very sparse aneurysm geometry, and find that using weights reduces calculation load imbalance by up to 85%, although the overall communication overhead is higher than some of our runs.

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Road quality assessment is a crucial part in municipalities’ workto maintain their infrastructure, plan upgrades, and manage theirbudgets. Properly maintaining this infrastructure relies heavily onconsistently monitoring its condition and deterioration over time.This can be a challenge, especially in larger towns and cities wherethere is a lot of city property to keep an eye on. We review roadquality assessment methods currently employed, and then describeour novel algorithm aimed at identifying distressed road regionsfrom street view images and pinpointing cracks within them. Wepredict distressed regions by computing Fisher vectors on localSIFT descriptors and classifying them with an SVM trained to distinguishbetween road qualities. We follow this step with a comparisonto a weighed contour map within these distressed regionsto identify exact crack and defect locations, and use the contourweights to predict the crack severity. Promising results are obtainedon our manually annotated dataset, which indicate the viability ofusing this cost-effective system to perform road quality assessmentat a municipal level.