The performance of sparse matrix computation highly depends on the matching of the matrix format with the underlying structure of the data being computed on. Different sparse matrix formats are suitable for different structures of data. Therefore, the first challenge is identifying the matrix structure before the computation to match it with an appropriate data format. The second challenge is to avoid reading the entire dataset before classifying it. This can be done by identifying the matrix structure through samples and their features. Yet, it is possible that global features cannot be determined from a sampling set and must instead be inferred from local features. To address these challenges, we develop a framework that generates sparse matrix structure classifiers using graph convolutional networks. The framework can also be extended to other matrix structures using user-provided generators. The approach achieves 97% classification accuracy on a set of representative sparse matrix shapes.

翻译：稀疏矩阵计算的性能高度依赖于矩阵格式与被计算数据底层结构的匹配程度。不同的稀疏矩阵格式适用于不同的数据结构。因此，首要挑战是在计算前识别矩阵结构，以便匹配合适的数据格式。第二个挑战是避免在分类前读取整个数据集，这可通过采样及其特征来识别矩阵结构实现。然而，全局特征可能无法从采样集确定，而需从局部特征推断。为应对这些挑战，我们开发了一个利用图卷积网络生成稀疏矩阵结构分类器的框架。该框架还可通过用户提供的生成器扩展到其他矩阵结构。该方法在一组代表性稀疏矩阵形状上实现了97%的分类准确率。