Method and apparatus for gnn-acceleration for efficient parallel processing of massive datasets

Abstract

Provided is an apparatus for accelerating a graph neural network for efficient parallel processing of massive graph datasets, including a streaming multiprocess (SM) scheduler and a computation unit, wherein the SM scheduler obtains a subgraph and an embedding table per layer, determines a number of SMs to be allocated for processing embeddings of a destination-vertex based on a feature dimension and a maximum number of threads in each of the SMs, and allocates the determined number of SMs to each of all destination-vertices included in the subgraph, and the computation unit obtains, by each of the SMs, embeddings of a destination-vertex allocated to each SM, obtains, by each SM, embeddings of at least one or more neighbor-vertices of the destination-vertex using the subgraph, and performs, by each SM, a user-designated operation using the embeddings of the destination-vertex and the embeddings of the neighbor-vertices.

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