377 research outputs found
Petuum: A New Platform for Distributed Machine Learning on Big Data
What is a systematic way to efficiently apply a wide spectrum of advanced ML
programs to industrial scale problems, using Big Models (up to 100s of billions
of parameters) on Big Data (up to terabytes or petabytes)? Modern
parallelization strategies employ fine-grained operations and scheduling beyond
the classic bulk-synchronous processing paradigm popularized by MapReduce, or
even specialized graph-based execution that relies on graph representations of
ML programs. The variety of approaches tends to pull systems and algorithms
design in different directions, and it remains difficult to find a universal
platform applicable to a wide range of ML programs at scale. We propose a
general-purpose framework that systematically addresses data- and
model-parallel challenges in large-scale ML, by observing that many ML programs
are fundamentally optimization-centric and admit error-tolerant,
iterative-convergent algorithmic solutions. This presents unique opportunities
for an integrative system design, such as bounded-error network synchronization
and dynamic scheduling based on ML program structure. We demonstrate the
efficacy of these system designs versus well-known implementations of modern ML
algorithms, allowing ML programs to run in much less time and at considerably
larger model sizes, even on modestly-sized compute clusters.Comment: 15 pages, 10 figures, final version in KDD 2015 under the same titl
Distributed Deep Learning for Question Answering
This paper is an empirical study of the distributed deep learning for
question answering subtasks: answer selection and question classification.
Comparison studies of SGD, MSGD, ADADELTA, ADAGRAD, ADAM/ADAMAX, RMSPROP,
DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results
show that the distributed framework based on the message passing interface can
accelerate the convergence speed at a sublinear scale. This paper demonstrates
the importance of distributed training. For example, with 48 workers, a 24x
speedup is achievable for the answer selection task and running time is
decreased from 138.2 hours to 5.81 hours, which will increase the productivity
significantly.Comment: This paper will appear in the Proceeding of The 25th ACM
International Conference on Information and Knowledge Management (CIKM 2016),
Indianapolis, US
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