Graph neural network application-oriented task scheduling execution system and method

Abstract

The invention provides a graph neural network application-oriented task scheduling execution system and method, and the method comprises the steps that a to-be-transmitted graph operator cache modulereads a to-be-processed graph operator processing request, and obtains needed input data from a cache according to a graph operator label of the to-be-processed graph operator processing request; thetransmitting unit transmits the to-be-processed graph operator processing request and the input data to a unified structure processing unit based on the static data stream; the unified structure processing unit maps the input data to the input of the corresponding binary operation tree according to the graph operator code of the graph operator label and the graph operator execution frame, and completes the current round of operation to obtain an intermediate result; the label generator generates a new graph operator label according to the label information of the previous round of operation; the unified structure processing unit returns the intermediate result and the new graph operator label to the to-be-transmitted graph operator cache module; and circularly execution is performed untilthe residual repetition frequency value in the graph operator label is 1, and the current intermediate result is written back to the cache.

Description

technical field [0001] The invention relates to the field of graph neural network applications, in particular to a task scheduling execution system and method for graph neural network applications. Background technique [0002] Convolutional neural networks are often used to solve problems such as computer vision, natural language processing, and speech analysis. However, they are usually only applicable to data spaces with Euclidean structure or grid structure, and their application range is limited. In recent years, the research on non-Euclidean graph structure data has been on the rise, and the data in the graph structure can express more complex relationships between elements on a larger scale. Graph convolutional neural networks (GCNs) perform graph convolution on graph-structured data, which has a more powerful ability to express information, so it has received great attention in academia and industry. Graph convolutional neural networks are currently widely used in n...

AI Technical Summary

Problems solved by technology

The hybrid computing mode reflected in the aggregation and combination stages makes it impossible for current conventional general-purpose processors, graph computing or neural network-specific accelerators to run graph neural networks efficiently.

In addition, if a dedicated acceleration structure is simply built for the two stages, problems such as low utilization of computing resources will arise.

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