In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …

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

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 …

Low-rank Tucker and CP tensor decompositions are powerful tools in data analytics. The
widely used alternating least squares (ALS) method, which solves a sequence of over …

The analysis of high-dimensional sparse data is becoming increasingly popular in many
important domains. However, real-world sparse tensors are challenging to process due to …

Sparse tensor contractions appear commonly in many applications. Efficiently computing a
two sparse tensor product is challenging: It not only inherits the challenges from common …

The Compressed Sparse Fiber (CSF) representation for sparse tensors is a generalization of
the Compressed Sparse Row (CSR) format for sparse matrices. For a tensor with d modes …

Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the most
computationally intensive kernel in sparse tensor decomposition. In this paper, we propose …

Analysis of multi-dimensional data, especially tensor decomposition, which extracts latent
information, is becoming considerably popular. Although multi-dimensional sparse data is …

Sparse tensor contraction sequence has been widely employed in many fields, such as
chemistry and physics. However, how to efficiently implement the sequence faces multiple …