Fault tolerance is a major concern in distributed computational settings. In the classic master-
worker setting, a server (the master) needs to perform some heavy computation which it may …

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 …

We extend coded distributed computing over finite fields to allow the number of workers to
be larger than the field size. We give codes that work for fully general matrix multiplication …

A rook polynomial in combinatorial mathematics is a generating polynomial of the number of
possible arrangements of non-attacking rooks on a checkerboard-like board, where no two …

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 …

Matrix multiplication is a fundamental building block in many machine learning models. As
the input matrices may be too large to be multiplied on a single server, it is common to split …

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 …