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

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

Abstract The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives
that provide automatically tuned computational kernels on sparse matrices, for use by solver …

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 …

This dissertation presents an automated system to generate highly efficient, platform-
adapted implementations of sparse matrix kernels. We show that conventional …

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

Large-scale data analytics using machine learning (ML) underpins many modern data-
driven applications. ML systems provide means of specifying and executing these ML …

Many important applications, such as those using sparse data structures, have memory
reference patterns that are unknown at compile-time. Prior work has developed run-time …

This article presents a code generator for sparse tensor contraction computations. It
leverages a mathematical representation of loop nest computations in the sparse polyhedral …

We present an approach for synthesizing transformations to enhance locality in imperfectly-
nested loops. The key idea is to embed the iteration space of every statement in a loop nest …