25 citations · 27 across the 3 of their papers we have counts for
7 papers
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization
Nuwan Ferdinand, Haider Al-Lawati, Stark C. Draper +1
Distributed optimization is vital in solving large-scale machine learning problems. A widely-shared feature of distributed optimization techniques is the requirement that all nodes…
Hierarchical Coded Matrix Multiplication
Shahrzad Kiani, Nuwan Ferdinand, Stark C. Draper
In distributed computing systems slow working nodes, known as stragglers, can greatly extend finishing times. Coded computing is a technique that enables straggler-resistant comput…
Cuboid Partitioning for Hierarchical Coded Matrix Multiplication
Shahrzad Kiani, Nuwan Ferdinand, Stark C. Draper
Coded matrix multiplication is a technique to enable straggler-resistant multiplication of large matrices in distributed computing systems. In this paper, we first present a concep…
Hierarchical Coded Matrix Multiplication
Shahrzad Kiani, Nuwan Ferdinand, Stark C. Draper
Slow working nodes, known as stragglers, can greatly reduce the speed of distributed computation. Coded matrix multiplication is a recently introduced technique that enables stragg…
Anytime Stochastic Gradient Descent: A Time to Hear from all the Workers
Nuwan Ferdinand, Stark Draper
In this paper, we focus on approaches to parallelizing stochastic gradient descent (SGD) wherein data is farmed out to a set of workers, the results of which, after a number of upd…
Exploitation of Stragglers in Coded Computation
Shahrzad Kiani, Nuwan Ferdinand, Stark C. Draper
In cloud computing systems slow processing nodes, often referred to as "stragglers", can significantly extend the computation time. Recent results have shown that error correction…