Graphics processing unit (GPU) card group-oriented graph data division optimization method

An optimization method and graph data technology, applied in the direction of electrical digital data processing, resource allocation, program control design, etc., can solve the problem of not obtaining a speedup ratio, and achieve the effect of improving utilization rate and load balancing

Pending Publication Date: 2021-09-21
北京睿芯高通量科技有限公司
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AI Technical Summary

Problems solved by technology

At present, some special load balancing and memory access optimization technologies have been proposed. These optimization technologies aim at the performance bottleneck problem on the GPU platform,

Method used

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[0036] Next, the technical solutions in the embodiments of the present invention will be apparent from the embodiment of the present invention, and it is clearly described, and it is understood that the described embodiments are merely embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, those of ordinary skill in the art will belong to the scope of the present invention without paying in the premise of creative labor.

[0037] Width first search algorithm usually compressed sparse (Compress Sparse Row, CSR) configuration number stored FIG. The CSR adjacency matrix row compressed for storage, is sequentially stored neighbor vertices, and all the spaces are valid values. The structure contains the idea of ​​adjacency list, but the storage space is smaller, more efficient memory access, the greater the amount of information.

[0038] First, a brief introduction to the present invention is applied dynamic load and the stati

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Abstract

A graphics processing unit (GPU) card group-oriented graph data division optimization method is used for dividing graph data G (V, E), a vertex set of the graph data G (V, E) is V, an edge set of the graph data G (V, E) is E, an edge set of the graph data G (V, E) from a source vertex v to a target vertex w is (v, w) (v belongs to V, w belongs to V), and the method comprises a dynamic load division method and a static load division method. The dynamic load division method and the static load division method are mutually switched through a preset switching mechanism. Compared with coarse-grained load division, load division with edges as granularity enables loads among threads to be more balanced, and the utilization rate of GPU computing resources by the BFS is improved.

Description

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Claims

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Application Information

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Owner 北京睿芯高通量科技有限公司
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