Data flow parallel processing method based on GPU-CUDA platform and genetic algorithm

Abstract

The invention provides a data flow parallel processing method based on a GPU-CUDA platform and a genetic algorithm. The data flow parallel processing method comprises the following steps: dynamically mining frequent item sets of newest data, and starting the searching process from a group of initial populations, wherein each individual in the populations can be a possible frequent pattern; adopting a sliding window mode according to the characteristics of a data flow to perform streaming data mining, and adopting a nested child window model based on a sliding window in terms of features of frequent item set mining; performing frequent item set mining by adopting a GPU-CUDA parallel processing technology according to the characteristics that the data flow is large in data amount and requires real-time processing; and finally obtaining the frequent item sets of data in the current sliding window by comprehensively processing the frequent item sets of nested child windows in the sliding window. Compared with the prior art, by means of the data flow parallel processing method, the frequent item sets of the flow data are processed through the strong floating-point calculation capability of a GPU and a CUDA accelerating technology for programming on the GPU, modeling can be performed by adopting a parallel mode of the genetic algorithm, and user operation experience is improved.

Description

technical field [0001] The invention relates to the field of computer applications, in particular to a data stream parallel processing method based on a GPU-CUDA platform and a genetic algorithm. Background technique [0002] A data stream is actually a continuously moving procession of elements consisting of collections of related data. Let t represent any time stamp, and at represent the data arriving at that time stamp. Stream data can be expressed as {..., at?1, at, at+1,...}. Different from the traditional application model, the stream data model has the following 4 points in common: (1) The data arrives in real time; (2) The order of data arrival is independent and not controlled by the application system; (3) The data scale is huge and its maximum value cannot be predicted; (4) Once the data is processed, unless it is specially saved, Otherwise, it cannot be retrieved and processed again, or it is expensive to retrieve the data again. [0003] Sliding window (slidin...

AI Technical Summary

Problems solved by technology

[0017] However, there is currently no technology that can quickly and efficiently obtain frequent itemsets of streaming data to improve user experience

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