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Future Is Sparse: Methods and Tools for Sparse Computations
DescriptionMany real-world computations involve sparse data structures in the form of sparse matrices, graphs, or sparse tensors. In computational sciences, sparse matrices are commonly used for numerically solving partial differential equations. Likewise, a large number of approaches for deep learning using graph representations, namely GNNs, have been proposed. More generic, multi-dimensional sparse tensors are currently at the heart of data-driven fields such as AI and deep learning. Getting high performance on such sparse data structures is well-known to be challenging and is still an open research problem. This workshop will gather a group of experts who are researching various aspects of this topic and aim to present the attendees with an overview of the state of the art research activities. More importantly, the workshop will provide a forum for interactions between the SC participants, so that new ideas can be generated to push forward the state of the art.
