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  1. Abstract

    High ozone concentrations have become the major summertime air quality problem in China. Extensive in situ observations are deployed for developing strategies to effectively control the emissions of ozone precursors, that is, nitrogen oxides (NOX = NO + NO2) and volatile organic compounds (VOCs). The modeling analysis of in situ observations often makes uses of the dependence of ozone peak concentration on NOXand VOC emissions, because ozone observations are among the most widely available air quality measurements. To extract more information from regulatory ozone observations, we extend the ozone‐precursor relationship to ozone peak time in this study. We find that the sensitivities of ozone peak time and concentration are complementary for regions with large anthropogenic emissions such as China. The ozone peak time is sensitive to both VOC and NOXemissions, and the sensitivity is nearly linear in the transition regime of ozone production compared to the changing ozone peak concentration sensitivity in this regime, making the diagnostics of ozone peak time particularly valuable. The extended ozone‐precursor relationships can be readily applied to understand the effects on ozone by emission changes of NOXand VOC and to assess potential biases of NOXand VOC emission inventories. These observation constraints based on regulatory ozone observations can complement the other measurement and modeling analysis methods nicely. Furthermore, we suggest that the ozone peak time sensitivity we discussed here to be used as a model evaluation measure before the empirical kinetic modeling approach (EKMA) diagram is applied to understand the effectiveness of emission control on ozone concentrations.

     
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  2. Abstract

    Graphical fluid simulations are CPU‐bound. Parallelizing simulations on hundreds of cores in the computing cloud would make them faster, but requires evenly balancing load across nodes. Good load balancing depends on manual decisions from experts, which are time‐consuming and error prone, or dynamic approaches that estimate and react to future load, which are non‐deterministic and hard to debug.

    This paper proposes Birdshot scheduling, an automatic and purely static load balancing algorithm whose performance is close to expert decisions and reactive algorithms without their difficulty or complexity. Birdshot scheduling's key insight is to leverage the high‐latency, high‐throughput, full bisection bandwidth of cloud computing nodes. Birdshot scheduling splits the simulation domain into many micro‐partitions and statically assigns them to nodes randomly. Analytical results show that randomly assigned micro‐partitions balance load with high probability. The high‐throughput network easily handles the increased data transfers from micro‐partitions, and full bisection bandwidth allows random placement with no performance penalty. Overlapping the communications and computations of different micro‐partitions masks latency.

    Experiments with particle‐level set, SPH, FLIP and explicit Eulerian methods show that Birdshot scheduling speeds up simulations by a factor of 2‐3, and can out‐perform reactive scheduling algorithms. Birdshot scheduling performs within 21% of state‐of‐the‐art dynamic methods that require running a second, parallel simulation. Unlike speculative algorithms, Birdshot scheduling is purely static: it requires no controller, runtime data collection, partition migration or support for these operations from the programmer.

     
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