Coded computation techniques provide robustness against straggling workers in distributed
computing. However, most of the existing schemes require exact provisioning of the …

When gradient descent (GD) is scaled to many parallel workers for large-scale machine
learning applications, its per-iteration computation time is limited by straggling workers …

Distributed computing has been a prominent solution to efficiently process massive datasets
in parallel. However, the existence of stragglers is one of the major concerns that slows …

Due to the surge of cloud-assisted AI services, the problem of designing resilient prediction
serving systems that can effectively cope with stragglers and minimize response delays has …

Client devices at the edge are generating increasingly large amounts of rich data suitable for
learning powerful statistical models. However, privacy concerns and heavy communication …

Distributed computing has emerged as a promising solution for accelerating machine
learning training processes on large-scale datasets by leveraging the parallel processing …

A single straggler worker may delay an entire distributed system. The state-of-the-art
strategies for mitigating delays in large-scale distributed matrix multiplication are polynomial …

In this work, we consider a sequence of $ J $ matrix multiplication jobs which needs to be
distributed by a master across multiple worker nodes. For $ i\in\{1, 2,\ldots, J\} $, job-$ i …

Long integer multiplication algorithms are fundamental computational kernels in numerous
applications, ranging from cryptographic systems to neural networks. They serve as …

In this work, we consider a sequence of J matrix multiplication jobs which needs to be
distributed by a master across multiple worker nodes. For i∈{1, 2,..., J}, job-i begins in round …