Distributed computing has become a common approach for large-scale computation tasks
due to benefits such as high reliability, scalability, computation speed, and cost …

We consider a large-scale matrix multiplication problem where the computation is carried
out using a distributed system with a master node and multiple worker nodes, where each …

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 …

Data centers are widely used for big data analytics, which often involve data-parallel jobs,
including query and web service. Meanwhile, cluster frameworks are rapidly developed for …

Faced with saturation of Moore's law and increasing size and dimension of data, system
designers have increasingly resorted to parallel and distributed computing to reduce …

We provide novel coded computation strategies for distributed matrix-matrix products that
outperform the recent “Polynomial code” constructions in recovery threshold, ie, the required …

Distributed model training is vulnerable to byzantine system failures and adversarial
compute nodes, ie, nodes that use malicious updates to corrupt the global model stored at a …

In large-scale distributed computing clusters, such as Amazon EC2, there are several types
of “system noise” that can result in major degradation of performance: system failures …

This paper develops coding techniques to reduce the running time of distributed learning
tasks. It characterizes the fundamental tradeoff to compute gradients in terms of three …

In recent years, electroencephalogram (EEG) e-motion recognition has been becoming an
emerging field in artificial intelligence area, which can reflect the relation between emotional …