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CPU+GPU group nuclear supercomputer system and SIFT feature matching parallel computing method

A parallel computing and feature matching technology, applied in the field of space remote sensing photography, can solve the problems of large amount of data, large amount of calculation and high computational complexity in SIFT feature matching processing, and achieve the effect of strong judgment ability, improved real-time performance and increased speed.

Inactive Publication Date: 2013-10-09
ZHENGZHOU NORMAL UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to provide a SIFT feature matching software system oriented to CPU+GPU architecture, aiming to solve the problems of large data volume, high computational complexity and large amount of computation faced by SIFT feature matching processing

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  • CPU+GPU group nuclear supercomputer system and SIFT feature matching parallel computing method

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Embodiment Construction

[0028] Necessary technical solutions:

[0029] The present invention is achieved in this way, in conjunction with accompanying drawing 1, a kind of CPU+GPU group core supercomputing system, this structure comprises CPU unit, north bridge chip unit, system memory unit, graphic arrangement unit:

[0030] As a subsystem of the main processor, the CPU unit is used to interpret computer instructions and process data in computer software to perform high-performance calculations;

[0031] The north bridge chip unit is connected to the CPU unit through the front-side bus to provide support for the type and main frequency of the CPU, the type and maximum capacity of the memory, ISA / PCI / AGP slot, ECC error correction, etc.;

[0032] The system memory unit is connected to the north bridge chip unit through the memory bus to store data information;

[0033] The graphics adapter unit is connected with the north bridge chip unit through the PCI-Express bus, and is used to control the outpu...

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Abstract

The invention discloses a CPU+GPU group nuclear supercomputer system and an SIFT feature matching parallel computing method. A CPU is used for interpreting instructions of a computer, processing data in computer software and carrying out high-performance computing. A north bridge chip unit is used for providing support for the type and basic frequency of the CPU, the type and maximum capacity of an internal storage, ISA / PCI / AGP slots, ECC error correction and the like. A system storage unit is used for storing information of the data. A graphics adaptor unit is used for controlling the output of computer graphs. A GPU module is installed on the graphics adaptor unit and used for carrying out a large amount of simple parallel computing and drawing the data into graphs. A GPU storage module is used for storing collected data. Through the parallel analysis, much computing is divided and performed in the CPU and the GPU, the respective computing advantage is played, the speed of the SIFT feature matching GPU parallel algorithm is improved by nearly 30 times compared with the speed of a CPU series algorithm, the time of data processing is shortened greatly, the real-time performance is improved, and extracting and matching of remote sensing image feature points are achieved.

Description

technical field [0001] The invention belongs to the field of space remote sensing photography, and in particular relates to a CPU+GPU group core supercomputing system and a SIFT feature matching parallel computing method. Background technique [0002] At present, with the development of photogrammetry and computer vision, image feature matching has increasingly become a basic problem in the fields of photogrammetry and remote sensing, resource analysis, 3-D reconstruction, computer vision and pattern recognition, as well as an important basis for applications such as object recognition and tracking. For multiple remote sensing images of the same scene, there may be many differences between them: different resolutions, grayscale attributes, positions (translation and rotation), scales, nonlinear deformations, and so on. Traditional feature point detection algorithms, such as feature point detection algorithms based on template matching, are not easy to design a large number o...

Claims

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Application Information

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IPC IPC(8): G06F9/30
Inventor 肖汉贾遂民肖波冯娜王永刚
Owner ZHENGZHOU NORMAL UNIV
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