SMO parallel processing method orientated at multi-core cluster

A parallel processing and cluster technology, applied in concurrent instruction execution, machine execution devices, etc., can solve problems such as large space, high cost, and slow response speed

Inactive Publication Date: 2014-04-09
COMP NETWORK INFORMATION CENT CHINESE ACADEMY OF SCI
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Problems solved by technology

According to the current actual needs of Sogou in terms of classification business, we need to complete the classification of the following sample size: a sample file with 10,000 lines, 2 sample files (2 categories) per day, and the script is set to study sample files for 10 days, that is, 200,000 samples, the size of the kernel matrix is

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  • SMO parallel processing method orientated at multi-core cluster
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  • SMO parallel processing method orientated at multi-core cluster

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[0021] The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments.

[0022] figure 1 It is a flowchart of SMO parallel processing for multi-core clusters in an embodiment of the present invention. Among them, the execution subject is all processes. This embodiment includes the following steps:

[0023] Step S101: Obtain local problem parameters of the data to be classified; obtain global parameters according to the local problem parameters; assign initial values ​​to the local problem parameters according to the global parameters, and assign initial values ​​to the algorithm parameters.

[0024] Initialize all processes, assign initial values ​​to the algorithm parameters, and set the multiplier in the algorithm to a i =0, intermediate variable E i =-y i , Where y i Label each sample, E i Parameters for local issues.

[0025] α i = 0 ⇔ y i f ( x i ) ≥ 1 - - - ...

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Abstract

The invention relates to an SMO parallel processing method orientated at a multi-core cluster. The SMO parallel processing method orientated at the multi-core cluster comprises the steps that an initial value is assigned to a local problem parameter according to a global parameter, and an initial value is assigned to an algorithm parameter; a local first boundary and a local second boundary of the local problem parameter are calculated according to the initial value of the local problem parameter; a global first boundary and a global second boundary are obtained according to local first boundary and the local second boundary; when the difference between the global first boundary and the global second boundary is not smaller than preset accuracy, a first multiplier corresponding to the global first boundary and a second multiplier corresponding to the global second boundary are calculated in an iterative model; the local problem parameter is updated in a multithreading mode after each time of iteration; when the iteration reaches a preset iteration frequency, a local solution of data to be classified is calculated according to a local sample multiplier, a global solution is obtained according to the local solution, and data classification is finished. The SMO parallel processing method orientated at the multi-core cluster resolves the traditional problems of high data classification cost, a high error rate and low response speed.

Description

technical field [0001] The invention relates to data classification, in particular to a multi-core cluster-oriented SMO parallel processing method. Background technique [0002] The problem to be solved by the present invention is to realize the parallelization of text classification, which not only improves the speed of text classification, but also distributes and stores data more reasonably and effectively. The technical problem comes from Sogou Company. Sogou Search is an interactive Chinese search engine, and classification technology is one of its core technologies. Due to the ever-increasing data scale of the modern Internet, the volume of samples that need to be classified is huge. However, the solution of storing these data in the same place and on the same disk is obviously difficult to cope with the changes in the future data world. Therefore, there is an urgent need for a technical solution to realize the distributed storage and parallel classification of these ...

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Application Information

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IPC IPC(8): G06F9/38
Inventor 迟学斌高原王珏单桂华田东刘俊
Owner COMP NETWORK INFORMATION CENT CHINESE ACADEMY OF SCI
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