Irregular applications such as big graph analysis, material simulations, molecular dynamics
simulations, and finite element analysis have performance problems due to their use of …

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 …

This paper shows how to build a sparse tensor algebra compiler that is agnostic to tensor
formats (data layouts). We develop an interface that describes formats in terms of their …

While loop reordering and fusion can make big impacts on the constant-factor performance
of dense tensor programs, the effects on sparse tensor programs are asymptotic, often …

This paper introduces three new compiler transformations for representing and transforming
sparse matrix computations and their data representations. In cooperation with run-time …

Many applications, from social network graph analytics to control flow analysis, compute on
sparse data that evolves over the course of program execution. Such data can be …

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 …

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

Many scientific applications compute using sparse data and store that data in a variety of
sparse formats because each format has unique space and performance benefits …

Sparse tensor algebra computations have become important in many real-world
applications like machine learning, scientific simulations, and data mining. Hence …