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Big data processing capacity testing method based on vertex reordering and priority caching

A technology for big data processing and capability testing, applied in special data processing applications, other database retrieval, other database indexes, etc., and can solve problems such as limited performance improvement, non-adjacent logic, and long memory access paths.

Inactive Publication Date: 2020-11-03
NAT UNIV OF DEFENSE TECH
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AI Technical Summary

Problems solved by technology

This ordered global memory access method is more image 3 The unordered random global memory access shown has made great progress, and the performance has been significantly improved. However, this method is still a global memory access method. The storage location of the first hit vertex may be the farthest from the physical distribution of vi. They are not logically adjacent, resulting in a long memory access path and limited performance improvement

Method used

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  • Big data processing capacity testing method based on vertex reordering and priority caching
  • Big data processing capacity testing method based on vertex reordering and priority caching
  • Big data processing capacity testing method based on vertex reordering and priority caching

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Embodiment Construction

[0100] Such as Figure 5 Shown, the present invention comprises the following steps:

[0101] The first step, graph generation. Use the Kronecker graph generator to generate a random graph structure G=(V,E), V is a set of vertices, which contains N V vertices, N V is a positive integer; E is a set of edges; E contains N E side, N E is a positive integer; N V and N E All are positive integers.

[0102] The second step is to construct an adjacency matrix A that stores the graph G. A ij = 0 means there is no edge between vertex i and vertex j, A ij =1 means that there is an edge between vertex i and vertex j, both i and j are positive integers and 0≤i≤N V -1, 0≤j≤N V -1;

[0103] The third step is to sort the vertices of V based on the degree of vertices and cache the key vertices first. The specific method is as follows:

[0104] 3.1. Traverse each vertex in V, record the degree of each vertex, and get the vertex degree set D, the i-th element in D deg(v i ) repres...

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Abstract

The invention discloses a big data processing capacity testing method based on vertex reordering and priority caching, and aims to reduce the number of memory access times, minimize a communication path, avoid invalid memory access and improve the big data processing capacity testing speed of a super computer. The technical scheme is that the method comprises the following five steps: generating agraph G, constructing an adjacent matrix A for storing the graph G, sorting vertexes of the graph G, preferentially caching key vertexes, carrying out BFS search on the graph G by utilizing a sortedvertex set and the preferentially cached key vertexes, and calculating an evaluation value of graph test performance. By adopting the method, the hit rate of the edge relationship between the nodes can be improved, the invalid memory access frequency is reduced, unnecessary memory access is avoided to the greatest extent, the traversal of the graph is accelerated, the big data processing capability test speed of the super computer is improved, and the power consumption can be reduced.

Description

technical field [0001] The invention relates to a method for testing the large data processing capability of a supercomputer, in particular to a method for testing the large data processing capability based on vertex reordering and priority caching. Background technique [0002] Graph structure is one of the most important data structures in big data applications, and has been widely used in many fields, such as social media, bioinformatics, astrophysics, artificial intelligence, data mining, etc. The common feature of these applications is the large amount of data and complex structure, which can often reach trillions of vertices and edges, resulting in higher requirements for data storage and computing power. Supercomputers are mainly used for numerical calculations. Most HPC benchmarks use computing power as a measure, such as HPL used by Top 500; in the era of big data where data-intensive applications are widely emerging, Graph 500 is an important supplement to Top500. ...

Claims

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Application Information

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IPC IPC(8): G06F16/901G06F16/903
CPCG06F16/9024G06F16/90348Y02D10/00
Inventor 甘新标龚春叶杨博陈旭光肖调杰谭雯
Owner NAT UNIV OF DEFENSE TECH
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