Household power consumption pattern modeling through a single power sensor

Lü, Yang; Xie, Pengli; Bi, Chen; Zhang, Ronghui; Cai, Bowen; Shao, Xiaowei; Wang, Rongben

doi:10.1016/j.renene.2020.03.118

Cited by 6 publications

(3 citation statements)

References 40 publications

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“…Besides the time for loading image data and writing into a file takes up the remaining part. So the total time of parallel rendering should be like this : t total = t load + t composite + t collect + t write (2) With the increasing of processors, the image collection time keeps growing, which effects overall performance in parallel rendering. Considering that t load and t write are far less than t composite or t collect , t total is mainly decided by t composite and t collect .…”

Section: Binary Tree For Avoiding Image Collectionmentioning

confidence: 99%

“…Author details 1 College of Intelligence and Computing, Tianjin University, Tianjin, China. 2 Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, China. 3 National Supercomputer Center in Tianjin, Tianjin, China.…”

Section: Fundingmentioning

confidence: 99%

“…With the development of HPC systems, simulations could generate larger amounts of data than before in aerodynamics, which means more powerful techniques should be adopted for data analyzing. Visualization aimed at aerodynamics has been playing an important role in scientific discovery, especially in massive data discovery [1][2][3][4][5][6]. The increase of data also challenges the traditional methods of scientific visualization.…”

Section: Introductionmentioning

confidence: 99%

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mSwap: a large-scale image-compositing method with optimal m-ary tree

Hou

Wang

et al. 2021

Adv. Aerodyn.

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With the increasing of computing ability, large-scale simulations have been generating massive amounts of data in aerodynamics. Sort-last parallel rendering is the most classical image compositing method for large-scale scientific visualization. However, in the stage of image compositing, the sort-last method may suffer from scalability problem on large-scale processors. Existing image compositing algorithms tend to perform well in certain situations. For instance, Direct Send is well on small and medium scale; Radix-k gets well performance only when the k-value is appropriate and so on. In this paper, we propose a novel method named mSwap for scientific visualization in aerodynamics, which uses the best scale of processors to make sure its performance at the best. mSwap groups the processors that we can use with a (m,k) table, which records the best combination of m (the number of processors in subgroup of each group) and k (the number of processors in each group). Then in each group, using a m-ary tree to composite the image for reducing the communication of processors. Finally, the image is composited between different groups to generate the final image. The performance and scalability of our mSwap method is demonstrated through experiments with thousands of processors.

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Section: Binary Tree For Avoiding Image Collectionmentioning

confidence: 99%