12 citations · 18 across the 7 of their papers we have counts for
7 papers
Exploiting Stragglers in Distributed Computing Systems with Task Grouping
Tharindu Adikari, Haider Al-Lawati, Jason Lam +2
We consider the problem of stragglers in distributed computing systems. Stragglers, which are compute nodes that unpredictably slow down, often increase the completion times of tas…
Hierarchical coded elastic computing
Shahrzad Kiani, Tharindu Adikari, Stark C. Draper
Elasticity is offered by cloud service providers to exploit under-utilized computing resources. The low-cost elastic nodes can leave and join any time during the computation cycle.…
Two-terminal source coding with common sum reconstruction
Tharindu Adikari, Stark Draper
We present the problem of two-terminal source coding with Common Sum Reconstruction (CSR). Consider two terminals, each with access to one of two correlated sources. Both terminals…
Decentralized optimization with non-identical sampling in presence of stragglers
Tharindu Adikari, Stark Draper
We consider decentralized consensus optimization when workers sample data from non-identical distributions and perform variable amounts of work due to slow nodes known as straggler…
Rate distortion comparison of a few gradient quantizers
Tharindu Adikari
This article is in the context of gradient compression. Gradient compression is a popular technique for mitigating the communication bottleneck observed when training large machine…
Compressing gradients by exploiting temporal correlation in momentum-SGD
Tharindu B. Adikari, Stark C. Draper
An increasing bottleneck in decentralized optimization is communication. Bigger models and growing datasets mean that decentralization of computation is important and that the amou…
Efficient learning of neighbor representations for boundary trees and forests
Tharindu Adikari, Stark C. Draper
We introduce a semiparametric approach to neighbor-based classification. We build off the recently proposed Boundary Trees algorithm by Mathy et al.(2015) which enables fast neighb…