Sparse tensors are rapidly becoming critical components of modern deep learning
workloads. However, developing high-performance sparse operators can be difficult and …

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

Over the past few years, the explosion in sparse tensor algebra workloads has led to a
corresponding rise in domain-specific accelerators to service them. Due to the irregularity …

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 …

We introduce indexed streams, a formal operational model and intermediate representation
that describes the fused execution of a contraction language that encompasses both sparse …

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 …

Sparse tensor decomposition and completion are common in numerous applications,
ranging from machine learning to computational quantum chemistry. Typically, the main …

From FORTRAN to NumPy, arrays have revolutionized how we express computation.
However, arrays in these, and almost all prominent systems, can only handle dense …

Recent years have seen considerable work on compiling sparse tensor algebra
expressions. This paper addresses a shortcoming in that work, namely how to generate …