Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

Numerous irregular graph datasets, for example social networks or web graphs, may contain
even trillions of edges. Often, their structure changes over time and they have domain …

Tensor algebra is a powerful tool with applications in machine learning, data analytics,
engineering and the physical sciences. Tensors are often sparse and compound operations …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

Sparse tensors arise in problems in science, engineering, machine learning, and data
analytics. Programs that operate on such tensors can exploit sparsity to reduce storage …

The Jaccard similarity index is an important measure of the overlap of two sets, widely used
in machine learning, computational genomics, information retrieval, and many other areas …

Betweenness centrality (BC) is a crucial graph problem that measures the significance of a
vertex by the number of shortest paths leading through it. We propose Maximal Frontier …

This paper shows how to compile sparse array programming languages. A sparse array
programming language is an array programming language that supports element-wise …

We introduce Mosaic, a sparse tensor algebra compiler that can bind tensor expressions to
external functions of other tensor algebra libraries and compilers. Users can extend Mosaic …

Combinatorial algorithms such as those that arise in graph analysis, modeling of discrete
systems, bioinformatics, and chemistry, are often hard to parallelize. The Combinatorial …