High-dimensional data stream canonical correlation parallel computation method and high-dimensional data stream canonical correlation parallel computation device in irregular steam

A typical correlation and parallel computing technology, applied in machine execution devices, concurrent instruction execution, etc., can solve the problems of unrealistic and time-consuming materialized stream data, and achieve the effect of reducing cost and improving real-time performance.

Inactive Publication Date: 2014-10-15
INSPUR BEIJING ELECTRONICS INFORMATION IND
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Problems solved by technology

This is undoubtedly very time-consuming
Since the data flow is continuous and huge in practical applications, it is impractical to materialize all stre...

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  • High-dimensional data stream canonical correlation parallel computation method and high-dimensional data stream canonical correlation parallel computation device in irregular steam
  • High-dimensional data stream canonical correlation parallel computation method and high-dimensional data stream canonical correlation parallel computation device in irregular steam
  • High-dimensional data stream canonical correlation parallel computation method and high-dimensional data stream canonical correlation parallel computation device in irregular steam

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[0038] In order to facilitate the understanding of those skilled in the art, the present invention will be further described below in conjunction with the accompanying drawings, which cannot be used to limit the protection scope of the present invention.

[0039] Since the dimensionality of high-dimensional data streams is usually very high, it is unavoidable to perform frequent calculations of matrix multiplication and transposition equations for high-performance operations. In statistical queries, due to the limited computing power, it is usually solved approximately when the accuracy is guaranteed. Therefore, the solution of sacrificing part of the accuracy in exchange for speed is the key for users to perform statistical continuous queries on high-dimensional data streams in real time. Combining the high-performance computing capabilities of the GPU is a good way.

[0040] For high-dimensional data flow, the present invention proposes a specific and feasible implementation...

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Abstract

Based on a CUDA (Compute Unified Device Architecture) and a processing model of high-dimensional data steam in irregular steam of a GPU (Graphic Processing Unit), the invention provides a high-dimensional data stream canonical correlation parallel computation method in the irregular steam. According to the method, on the processing model of the high-dimensional data steam, a CUDA programming model of the GPU and a sliding window data steam mode are adopted for maintaining covariance matrixes S21 and S22 and respective variance matrixes S11 and S12 of two data steam sample matrixes in an incremental updating mode; then, a synopsis data structure is generated; high-dimensional product matrixes are subjected to sampling in the row direction and the line direction for realizing dimensionality reduction; canonical feature values and canonical feature vectors are subjected to parallel computation according to matrixes obtained through sampling; the cost for generating the canonical correlation coefficient is reduced; and the real-time performance of high-dimensional data stream correlation analysis is obviously improved.

Description

technical field [0001] The invention relates to a processing method and device for high-level data streams, in particular to a parallel computing method and device for typical correlation of high-dimensional data streams in irregular streams. Background technique [0002] Correlation analysis of multi-dimensional data streams has a wide range of applications in stock trend forecasting, high-speed network fault diagnosis, weather forecasting and many other fields that require online trend analysis. For example, in sensor networks this is equivalent to analyzing the correlation or coupling relationship between fields. For example, in the analysis of stock investment, the analysis selects the correlation between the S&P 500 Index and the Nasdaq Composite Index to guide the portfolio investment of stocks. How to use the correlation attributes of two indexes to judge whether two stocks are correlated. Which attribute information plays an important role. [0003] Since the dime...

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Application Information

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IPC IPC(8): G06F9/38
Inventor 卢晓伟张广勇沈铂吴韶华
Owner INSPUR BEIJING ELECTRONICS INFORMATION IND
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