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

A genetic algorithm and parallel processing technology, which is applied in the field of data stream parallel processing based on the GPU-CUDA platform and genetic algorithm, to achieve the effect of improving the operating experience

Inactive Publication Date: 2013-09-04
LANGCHAO ELECTRONIC INFORMATION IND CO LTD
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Problems solved by technology

[0017] However, there is currently no technology that can quickly and ef...

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

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[0067] The method of the present invention is described in detail below with reference to the accompanying drawings.

[0068] The implementation of the present invention will be described in detail below in conjunction with the accompanying drawings and examples, so as to fully understand and implement the process of how to apply theoretical models and technical means to solve technical problems and achieve technical effects in the present invention.

[0069] First of all, if there is no conflict, the embodiment of the present invention and the combination of various features in the embodiment are within the protection scope of the present invention. In addition, the steps shown in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and, although a logical order is shown in the flow diagrams, in some cases, the sequence may be different. The steps shown or described are performed in the order herein.

[00...

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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...

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Application Information

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IPC IPC(8): G06F9/38G06N3/12
Inventor 卢晓伟周勇韩君张清
Owner LANGCHAO ELECTRONIC INFORMATION IND CO LTD
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