Graph data prefetcher and prefetching method

Abstract

According to the graph data prefetcher and the prefetching method provided by the invention, a vertex information storage structure is optimized and stored, vertex numbers of a graph are reordered, and connected vertexes are numbered as adjacent values as far as possible, so that the locality of data access is improved, and the randomness of vertex information access is greatly reduced; Meanwhile, the time locality of data access through a graph neural network algorithm is utilized, the number of times of vertex information access is recorded for prefetching design, and the hit rate of prefetching is greatly increased. When the characteristic values of the two vertexes on the same edge are updated, the edge characteristic values are sequentially stored according to the sequence, the prefetcher is designed based on the updating sequence of the two vertexes on the edge and the locality of the access time, and the hit rate of the prefetcher is effectively improved. The characteristic that the storage structure and the access sequence of the topological relation are consistent and continuous is utilized, the prefetcher is designed by recording the historical address of the topological relation, and the extremely high hit rate is achieved.

Description

technical field [0001] The invention relates to a graph data processing technology, in particular to a graph data prefetcher and a prefetch method. Background technique [0002] Graphs are one of the most classic and commonly used data structures. With the increasing application of machine learning and data mining, the scale of graphs has also become larger and larger. On the other hand, due to the extreme irregularity of large-scale graph data, a large amount of data communication is generated during the calculation process on the traditional system, which leads to the problem of low calculation efficiency. How to effectively process and analyze large-scale graph data is a major research hotspot in academia and industry. In order to effectively deal with the above challenges, many graph computing systems have been proposed for efficient graph data processing. [0003] In different graph accelerators, the bandwidth required by the computing array is often several times or e...

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Problems solved by technology

[0006] The above different prefetchers are suitable for some specific scenarios in most cases, due to the irregular structure of the graph, the above prefetchers are not very good The most important reason for prefetching suitable for graph data is that it does not combine the unique structure and algorithm characteristics of graph data

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