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Graph edge partition models have recently become an appealing alternative to graph vertex partition models for distributed computing due to their flexibility in balancing loads and their performance in reducing communication cost [6, 16]. In this paper, we propose a simple yet effective graph edge partitioning algorithm. In practice, our algorithm provides good partition quality (and better than similar state-of-the-art edge partition approaches, at least for power-law graphs) while maintaining low partition overhead. In theory, previous work [6] showed that an approximation guarantee of O ( d max √ log n log k ) apply to the graphs with m =Ω( k 2 ) edges ( k is the number of partitions). We further rigorously proved that this approximation guarantee hold for all graphs. We show how our edge partition model can be applied to parallel computing. We draw our example from GPU program locality enhancement and demonstrate that the graph edge partition model does not only apply to distributed computing with many computer nodes, but also to parallel computing in a single computer node with a many-core processor.

Efficiently overseeing adherence to traffic regulations is an arduous task faced by authorities in light of population growth and increased vehicular activity on roads. The conventional approach to managing road users who break laws involves time-intensive manual processes that interfere with smooth transportation operations. This paper proposes a promising tactic for automating the production of E-challans through incorporation of Automatic Number Plate Recognition (ANPR) technology. By installing mounted cameras with ANPR capabilities alongside CCTV equipment which utilizes image processing together with optical character recognition technology (OCR), swift automated identification of infringing drivers can be achieved through read-outs from their vehicle registration plates. Ultimately, this method seeks to lessen reliance on human resources while enhancing the effectiveness of law enforcement activities

Graph edge partition models have recently become an appealing alternative to graph vertex partition models for distributed computing due to both their flexibility in balancing loads and their performance in reducing communication cost. In this paper, we propose a simple yet effective graph edge partitioning algorithm. In practice, our algorithm provides good partition quality while maintaining low partition overhead. It also outperforms similar state-of-the-art edge partition approaches, especially for power-law graphs. In theory, previous work showed that an approximation guarantee of O(d max √(log n log k)) apply to the graphs with m=Ω(k 2 ) edges (n is the number of vertices, and k is the number of partitions). We further rigorously proved that this approximation guarantee hold for all graphs. We also demonstrate the applicability of the proposed edge partition algorithm in real parallel computing systems. We draw our example from GPU program locality enhancement and demonstrate that the graph edge partition model does not only apply to distributed computing with many computer nodes, but also to parallel computing in a single computer node with a many-core processor.