Parallel computing of spatial big data and derivation of asymptotic behavior of statistical partition equation

Zeyu Long

doi:10.1117/12.2671640

16 March 2023 Parallel computing of spatial big data and derivation of asymptotic behavior of statistical partition equation

Zeyu Long

Author Affiliations +

Proceedings Volume 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022); 1259307 (2023) https://doi.org/10.1117/12.2671640
Event: 2nd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 2022, Guangzhou, China

Abstract

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 𝑛→∞ in the partition equation p(n), p(n) tends to a certain value.

Citation Download Citation

Zeyu Long "Parallel computing of spatial big data and derivation of asymptotic behavior of statistical partition equation", Proc. SPIE 12593, Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022), 1259307 (16 March 2023); https://doi.org/10.1117/12.2671640

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