Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DSAG: A mixed synchronous-asynchronous iterative method for
  straggler-resilient learning   We consider straggler-resilient learning. In many previous works, e.g., in
the coded computing literature, straggling is modeled as random delays that are
independent and identically distributed between workers. However, in many
practical scenarios, a given worker may straggle over an extended period of
time. We propose a latency model that captures this behavior and is
substantiated by traces collected on Microsoft Azure, Amazon Web Services
(AWS), and a small local cluster. Building on this model, we propose DSAG, a
mixed synchronous-asynchronous iterative optimization method, based on the
stochastic average gradient (SAG) method, that combines timely and stale
results. We also propose a dynamic load-balancing strategy to further reduce
the impact of straggling workers. We evaluate DSAG for principal component
analysis, cast as a finite-sum optimization problem, of a large genomics
dataset, and for logistic regression on a cluster composed of 100 workers on
AWS, and find that DSAG is up to about 50% faster than SAG, and more than twice
as fast as coded computing methods, for the particular scenario that we
consider.

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The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
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The categories of this paper are: cmp-lg
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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: ao-sci
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The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con