Method for realizing support vector machine by MPI programming and OpenMP programming

Abstract

The invention relates to a machine learning method based on statistical learning theory. In order to solve the problems on large-scale sorting and the solution optimization in practical realization of an SVM (support vector machine), and realizes control on time price and space price of calculation, the technical scheme adopted by the invention is a method for realizing support vector machine by adoption of MPI programming and OpenMP programming. According to the concept of sorting algorithm of SVM (support vector machine), the method is realizes as follows: serial program codes are complied by C++ and associated statements and functions of OpenMP and MPI based on the serial codes are added to realize parallelization. The method provided by the invention includes the following detailed steps: 1) determining functions of parts of the algorithm; communicating among the algorithm modules by MPI programming to transfer data and realize synchronization; and 2) adding compiling guidance statements by Open MP in the sub-modules of the algorithm, wherein a compiler automatically conducts thread-level parallel realization of the codes in the parallel area included in the compiling guidance statements. The method provided by the invention is mainly applied to the machine learning.

Description

technical field [0001] The invention relates to a machine learning method based on statistical learning theory, specifically, a parallel support vector classifier is realized based on mixed programming of MPI and OpenMP. Background technique [0002] 1. Support Vector Machine (SVM) [0003] Support Vector Machine (Support Vector Machine, SVM) is a machine learning method based on statistical learning theory proposed by Vapnik et al. It maximizes the classification interval to construct the optimal classification hyperplane to improve the generalization ability of the classifier. It solves problems such as nonlinearity, high dimensionality, and local minima efficiently. Compared with the traditional neural network learning method, SVM has the least structural risk, can approximate any function and guarantee the global optimum, and is suitable for the field of small sample, nonlinear kernel high-dimensional modeling. At present, SVM has been widely used in handwritten chara...

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Problems solved by technology

Among them, the SMP architecture has the characteristics of low communication delay, and can adopt multithreading mechanism (such as Pthreads) and compilation guidance (such as OpenMP) parallel programming. Its implementation is relatively simple, but the disadvantage is that the scalability is poor; the MPP architecture is based on distributed Storage, with good scalability, mainly adopts the message passing mechanism, and the implementation standards include MPI, PVM, etc., but the communication overhead between the processors is large, and programming is relatively difficult

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