Generalized tensor algebra is a prime candidate for acceleration via customized ASICs.
Modern tensors feature a wide range of data sparsity, with the density of non-zero elements …

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 …

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 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 …

Sparse linear algebra includes the fundamental and important operations in various large-
scale scientific computing and real-world applications. There exists performance bottleneck …

This paper proposes a new storage format for sparse tensors, called Hierarchical
COOrdinate (HiCOO; pronounced:“haiku”). It derives from coordinate (COO) format, arguably …

Big data analysis has become a crucial part of new emerging technologies such as the
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …

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 …

Many applications in data science and scientific computing involve large-scale datasets that
are expensive to store and manipulate. However, these datasets possess inherent …