Matrix model based multi-calculation-engine automatic selection method

A computing engine and matrix model technology, applied in the direction of complex mathematical operations, etc., can solve the problems of single matrix computing platform, insufficient ability of matrix scale change, low performance, etc., to reduce matrix operation time and achieve the effect of selectivity

Active Publication Date: 2016-03-16
南京鸿云信息科技有限公司
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Problems solved by technology

[0005] Purpose of the invention: In view of the problems and deficiencies in the above-mentioned prior art, the purpose of the present invention is to provide a method for automatically selecting mult

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  • Matrix model based multi-calculation-engine automatic selection method
  • Matrix model based multi-calculation-engine automatic selection method
  • Matrix model based multi-calculation-engine automatic selection method

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[0020] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these embodiments are only for illustrating the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various aspects of the present invention Modifications in equivalent forms all fall within the scope defined by the appended claims of this application.

[0021] Such as figure 1 As shown, the complete process of the present invention includes five parts: designing matrix model, constructing time modeling, constructing data flow graph of matrix operation, optimizing calculation engine allocation and executing solution generation. The specific implementation manners are respectively described as follows:

[0022] The specific implementation of designing the matrix model is as follows: the matrix model is an abstract matrix...

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Abstract

The invention discloses a matrix model based multi-calculation-engine automatic selection method. The method comprises the following steps of: adopting a matrix as a programming model and R as a programming language, and calling matrix calculation operations of different calculation engines in an R language program; calculating the time for executing different operations in different engines for matrixes of different scales, and performing analysis modeling to obtain a time performance model; modeling transmission time of data of the matrixes of different scales among the engines to obtain a time performance model; performing grammatical analysis on the R program, constructing a data flow diagram of the matrix operations, and applying each matrix operation to one calculation engine through recursive analysis on the data flow diagram according to a principle of shortest total execution time; performing grammatical analysis on the R language program, and adding set engine information into the R language program to obtain a final execution solution. According to the method, the problems of single platform, low performance and matrix scale change coping capacity shortage of existing matrix calculation are solved.

Description

technical field [0001] The invention relates to the field of automatic scheduling, in particular to an automatic selection method of multiple computing engines based on a matrix model. Background technique [0002] Matrix is ​​an important model in machine learning and data mining. Operations such as matrix multiplication, transposition, and decomposition are necessary methods in many machine learning algorithms. Among the commonly used languages ​​for data analysis and calculation, Matlab and R are based on matrix models, and they perform very fast in small-scale matrix operations. In the era of big data, with the expansion of the matrix scale, there is an upper limit for single-computer computing, and it has become difficult to store and calculate the matrix effectively. With the emergence of distributed computing frameworks such as MPI, Hadoop, and Spark, efficient large-scale matrix operations can be realized by dividing and storing matrix data on each machine in the di...

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Application Information

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IPC IPC(8): G06F17/16
CPCG06F17/16
Inventor 黄宜华顾荣刘志强
Owner 南京鸿云信息科技有限公司
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