13,653 research outputs found
Asynchronous Parallel Stochastic Gradient Descent - A Numeric Core for Scalable Distributed Machine Learning Algorithms
The implementation of a vast majority of machine learning (ML) algorithms
boils down to solving a numerical optimization problem. In this context,
Stochastic Gradient Descent (SGD) methods have long proven to provide good
results, both in terms of convergence and accuracy. Recently, several
parallelization approaches have been proposed in order to scale SGD to solve
very large ML problems. At their core, most of these approaches are following a
map-reduce scheme. This paper presents a novel parallel updating algorithm for
SGD, which utilizes the asynchronous single-sided communication paradigm.
Compared to existing methods, Asynchronous Parallel Stochastic Gradient Descent
(ASGD) provides faster (or at least equal) convergence, close to linear scaling
and stable accuracy
Slow Learners are Fast
Online learning algorithms have impressive convergence properties when it
comes to risk minimization and convex games on very large problems. However,
they are inherently sequential in their design which prevents them from taking
advantage of modern multi-core architectures. In this paper we prove that
online learning with delayed updates converges well, thereby facilitating
parallel online learning.Comment: Extended version of conference paper - NIPS 200
Balancing the Communication Load of Asynchronously Parallelized Machine Learning Algorithms
Stochastic Gradient Descent (SGD) is the standard numerical method used to
solve the core optimization problem for the vast majority of machine learning
(ML) algorithms. In the context of large scale learning, as utilized by many
Big Data applications, efficient parallelization of SGD is in the focus of
active research. Recently, we were able to show that the asynchronous
communication paradigm can be applied to achieve a fast and scalable
parallelization of SGD. Asynchronous Stochastic Gradient Descent (ASGD)
outperforms other, mostly MapReduce based, parallel algorithms solving large
scale machine learning problems. In this paper, we investigate the impact of
asynchronous communication frequency and message size on the performance of
ASGD applied to large scale ML on HTC cluster and cloud environments. We
introduce a novel algorithm for the automatic balancing of the asynchronous
communication load, which allows to adapt ASGD to changing network bandwidths
and latencies.Comment: arXiv admin note: substantial text overlap with arXiv:1505.0495
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