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Big data concurrent scheduling and acceleration processing method based on many-core cluster

A processing method and big data technology, applied in special data processing applications, file/folder operations, file systems, etc., can solve problems such as application processing difficulties, data cannot be processed in time, etc., and achieve the effect of fast computing speed

Active Publication Date: 2021-07-30
OCEAN UNIV OF CHINA +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

It is more difficult to deal with subsequent applications
In order to solve the problem that a large amount of data generated by the model system cannot be processed in time, this system method was invented to achieve a parallel data processing system capability of tens of millions of orders of magnitude

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  • Big data concurrent scheduling and acceleration processing method based on many-core cluster

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Embodiment Construction

[0041] In order to make the purpose, technical solution and advantages of the application more clear, the technical solution in the embodiment of the application will be described in more detail below in conjunction with the drawings in the embodiment of the application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of the application. The embodiments described below by referring to the figures are exemplary, and are intended to explain the present application, and should not be construed as limiting the present application. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application. Embodiments of the present application will be described in detail below in conjunction with th...

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Abstract

The invention discloses a big data concurrent scheduling and acceleration processing method based on a many-core cluster. The big data concurrent scheduling and acceleration processing method based on the many-core cluster comprises the following steps: acquiring an ocean data file; splitting the ocean data file, so that at least two ocean data sub-files are obtained, wherein the ocean data sub-files have the same resolution ratio and time step length; and performing parallel processing on each ocean data sub-file through a parallel system so as to obtain at least one spatio-temporal data file, and the spatio-temporal data file has different resolution and time step length from the ocean data sub-files. By means of the big data concurrent scheduling and accelerated processing method based on the many-core cluster, the resolution ratio and the time step length of the obtained spatio-temporal data file can be changed according to needs, and the calculation speed is high.

Description

technical field [0001] The invention relates to the technical field of parallel processing, in particular to a large data concurrent scheduling and accelerated processing method based on many-core clusters and a parallel processing system for marine data files. Background technique [0002] Ocean numerical simulation takes the real ocean as the basic physical background, uses high-performance computers as the carrier, and establishes mathematical models according to physical laws to simulate the state of the ocean (including sea temperature, salinity, ocean current, ocean waves, tides, etc.), Parametrically and quantitatively describe the specific conditions of the ocean. With the rapid improvement of computer computing power, ocean numerical simulation has been greatly developed in recent years, and has gradually become an important source of ocean big data. Among them, CESM is the leader among many marine numerical simulation systems, and the amount of data generated by i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/16
CPCG06F16/16
Inventor 王勇周生辉刘圆圆魏志强贾东宁桂琳
Owner OCEAN UNIV OF CHINA