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Texas A&M University
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FDN achieves state-of-the-art forecasting accuracy while providing interpretable insights into spatiotemporal data, all with reduced computational demands.
A single roofline model is insufficient for predicting SpMM performance across diverse sparsity structures; data layout and blocking strategies must be evaluated in the context of matrix structure.