We consider the problem of massive matrix multiplication, which underlies many data
analytic applications, in a large-scale distributed system comprising a group of worker …

In distributed matrix multiplication, a common scenario is to assign each worker a fraction of
the multiplication task, by partitioning the input matrices into smaller submatrices. In …

Coded computing is a new framework to address fundamental issues in large scale
distributed computing, by injecting structured randomness and redundancy. We first provide …

We introduce two generalizations to the paradigm of using Random Khatri-Rao Product
(RKRP) codes for distributed matrix multiplication. We first introduce a class of codes called …

Coded distributed computation has become common practice for performing gradient
descent on large datasets to mitigate stragglers and other faults. This paper proposes a …

The problem of computing batches of matrix multiplications in distributed computing systems
with stragglers is studied. Unlike existing works in the literature, the matrices in a batch are …

Tensors are a fundamental operation in distributed computing, eg, machine learning, that
are commonly distributed into multiple parallel tasks for large datasets. Stragglers and other …

Matrix multiplication is a fundamental operation in machine learning and is commonly
distributed into multiple parallel tasks for large datasets. Stragglers and other failures can …

A ubiquitous problem in computer science research is the optimization of computation on
large data sets. Such computations are usually too large to be performed on one machine …