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Deep and shallow convection calculations occupy significant times inatmosphere models. These calculations also present significant load imbalancesdue to varying cloud covers over different regions of the grid. In this work,we accelerate these calculations on Intel{\textregistered} XeonPhi{\texttrademark} Coprocessor Systems. By employing dynamic scheduling inOpenMP, we demonstrate large reductions in load imbalance and about 10%increase in speedups. By careful categorization of data as private,firstprivate and shared, we minimize data copying overheads for thecoprocessors. We identify regions of false sharing among threads and eliminatethem by loop rearrangements. We also employ proportional partitioning ofindependent column computations across both the CPU and coprocessor cores basedon the performance ratio of the computations on the heterogeneous resources.These techniques along with various vectorization strategies resulted in about30% improvement in convection calculations.

Deep and Shallow convections in Atmosphere Models on Intel Xeon Phi Coprocessor Systems