At present, the parallel computing theory based on spatial big data has problems such as difficult algorithms, difficult operations, and complex formulas, based on this, this paper proposes a p-Dot parallel computing model based on the traditional parallel computing model of BSP (Bulk Synchronous Parallel), and then tests the model effect by setting experiments. The results reveal that: (1) All curves are open up and have a minimum value. (2) The dataset with a capacity of 0.25GB is the benchmark dataset. (3) The expansion rate e(w) of the input data capacity of the model under different test procedures has a linear relationship with the expansion rate e(n* ) of the corresponding optimal number of machines. (4) When đââ in the partition equation p(n), p(n) tends to a certain value.
Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services. While many solutions have been proposed for this task, the majority of them are too computationally expensive to run on portable devices with limited hardware resources. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based video super-resolution solutions that can achieve a realtime performance on mobile GPUs. The participants were provided with the REDS dataset and trained their models to do an efficient 4X video upscaling. The runtime of all models was evaluated on the OPPO Find X2 smartphone with the Snapdragon 865 SoC capable of accelerating floatingpoint networks on its Adreno GPU. The proposed solutions are fully compatible with any mobile GPU and can upscale videos to HD resolution at up to 80 FPS while demonstrating high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.
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