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The performance of sparse matrix computation highly depends on the matchingof the matrix format with the underlying structure of the data being computedon. Different sparse matrix formats are suitable for different structures ofdata. Therefore, the first challenge is identifying the matrix structure beforethe computation to match it with an appropriate data format. The secondchallenge is to avoid reading the entire dataset before classifying it. Thiscan be done by identifying the matrix structure through samples and theirfeatures. Yet, it is possible that global features cannot be determined from asampling set and must instead be inferred from local features. To address thesechallenges, we develop a framework that generates sparse matrix structureclassifiers using graph convolutional networks. The framework can also beextended to other matrix structures using user-provided generators. Theapproach achieves 97% classification accuracy on a set of representative sparsematrix shapes.

Observe Locally, Classify Globally: Using GNNs to Identify Sparse Matrix Structure