Matrix multiplication is a fundamental building block in various distributed computing
algorithms. In order to multiply large matrices, it is common practice to distribute the …

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 …

In distributed computing/storage/machine learning system, the method of encoding and
decoding combing shift‐and‐addition (SA) and zigzag decoding (ZD) is proposed to solve …

To provide reliability in distributed systems, combination property (CP) is desired, where k
original packets are encoded into n≥ k packets and arbitrary k are sufficient to reconstruct …

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 …

Edge computing is an efficient computing paradigm, which can utilize computing devices at
the edge of network to provide real-time proximity service. Since edge devices lack …

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 …