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Distributed Machine Learning via Sufficient Factor Broadcasting | | Pengtao Xie
; Jin Kyu Kim
; Yi Zhou
; Qirong Ho
; Abhimanu Kumar
; Yaoliang Yu
; Eric Xing
; | | Date: |
26 Nov 2015 | | Abstract: | Matrix-parametrized models, including multiclass logistic regression and
sparse coding, are used in machine learning (ML) applications ranging from
computer vision to computational biology. When these models are applied to
large-scale ML problems starting at millions of samples and tens of thousands
of classes, their parameter matrix can grow at an unexpected rate, resulting in
high parameter synchronization costs that greatly slow down distributed
learning. To address this issue, we propose a Sufficient Factor Broadcasting
(SFB) computation model for efficient distributed learning of a large family of
matrix-parameterized models, which share the following property: the parameter
update computed on each data sample is a rank-1 matrix, i.e., the outer product
of two "sufficient factors" (SFs). By broadcasting the SFs among worker
machines and reconstructing the update matrices locally at each worker, SFB
improves communication efficiency --- communication costs are linear in the
parameter matrix’s dimensions, rather than quadratic --- without affecting
computational correctness. We present a theoretical convergence analysis of
SFB, and empirically corroborate its efficiency on four different
matrix-parametrized ML models. | | Source: | arXiv, 1511.8486 | | Services: | Forum | Review | PDF | Favorites | |
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