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 …

We address the problem of optimizing sparse tensor algebra in a compiler and show how to
define standard loop transformations---split, collapse, and reorder---on sparse iteration …

We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting
sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators …

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

This paper proposes Capstan: a scalable, parallel-patterns-based, reconfigurable dataflow
accelerator (RDA) for sparse and dense tensor applications. Instead of designing for one …

In this work, we propose a unified abstraction for graph algorithms: the Extended General
Einsums language, or EDGE. The EDGE language expresses graph algorithms in the …

Real world arrays often contain underlying structure, such as sparsity, runs of repeated
values, or symmetry. Specializing for structure yields significant speedups. But automatically …

This paper shows how to generate code that efficiently converts sparse tensors between
disparate storage formats (data layouts) such as CSR, DIA, ELL, and many others. We …

Recently, sparse tensor algebra (SpTA) plays an increasingly important role in machine
learning. However, due to the unstructured sparsity of SpTA, the general-purpose …

We introduce Stardust, a compiler that compiles sparse tensor algebra to reconfigurable
dataflow architectures (RDAs). Stardust introduces new user-provided data representation …