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

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.