Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Avoiding communication in primal and dual block coordinate descent
  methods   Primal and dual block coordinate descent methods are iterative methods for
solving regularized and unregularized optimization problems. Distributed-memory
parallel implementations of these methods have become popular in analyzing
large machine learning datasets. However, existing implementations communicate
at every iteration which, on modern data center and supercomputing
architectures, often dominates the cost of floating-point computation. Recent
results on communication-avoiding Krylov subspace methods suggest that large
speedups are possible by re-organizing iterative algorithms to avoid
communication. We show how applying similar algorithmic transformations can
lead to primal and dual block coordinate descent methods that only communicate
every $s$ iterations--where $s$ is a tuning parameter--instead of every
iteration for the \textit{regularized least-squares problem}. We show that the
communication-avoiding variants reduce the number of synchronizations by a
factor of $s$ on distributed-memory parallel machines without altering the
convergence rate and attains strong scaling speedups of up to $6.1\times$ on a
Cray XC30 supercomputer.

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The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
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The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: funct-an