Coded Distributed Computing for Inverse Problems

By Yaoqing Yang, Pulkit Grover, Soummya Kar. 2017

68 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

DRACO: Byzantine-resilient Distributed Training via Redundant Gradients (2018, background): “, 2016), more recent work has employed coding theory to speed up distributed machine learning systems (Lee et al., 2017; Li et al., 2015; Dutta et al., 2016; 2017; Reisizadeh et al., 2017; Yang et al., 2017).”
“Short-Dot”: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products (2017, background): “Following the conference publication [1], there have been several interesting works in the coded computing community (see [21] for survey) on coded linear algebra [22]–[37], coded convolutions [47], [48], gradient coding [38]–[42], coded Fast Fourier Transforms [55], [56], performance analysis [45],”
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning (2018, background): “A more recent line of work that address the straggler problem is based on coding-theoryinspired techniques Tandon et al. (2017); Lee et al. (2016); Dutta et al. (2016); Karakus et al. (2017a,b); Yang et al. (2017); Halbawi et al. (2017); Reisizadeh et al. (2017).”
Rateless codes for near-perfect load balancing in distributed matrix-vector multiplication (2018, background): “There are also works dealing with coded gradient descent [24, 43, 56], coded convolution [15], coded Fourier Transform [64], Page Rank [62], and coded distributed optimization [36] – in general any distributed linear computation can essentially be expressed as a matrix multiplication/addition operat”