344 research outputs found
Computing Web-scale Topic Models using an Asynchronous Parameter Server
Topic models such as Latent Dirichlet Allocation (LDA) have been widely used
in information retrieval for tasks ranging from smoothing and feedback methods
to tools for exploratory search and discovery. However, classical methods for
inferring topic models do not scale up to the massive size of today's publicly
available Web-scale data sets. The state-of-the-art approaches rely on custom
strategies, implementations and hardware to facilitate their asynchronous,
communication-intensive workloads.
We present APS-LDA, which integrates state-of-the-art topic modeling with
cluster computing frameworks such as Spark using a novel asynchronous parameter
server. Advantages of this integration include convenient usage of existing
data processing pipelines and eliminating the need for disk writes as data can
be kept in memory from start to finish. Our goal is not to outperform highly
customized implementations, but to propose a general high-performance topic
modeling framework that can easily be used in today's data processing
pipelines. We compare APS-LDA to the existing Spark LDA implementations and
show that our system can, on a 480-core cluster, process up to 135 times more
data and 10 times more topics without sacrificing model quality.Comment: To appear in SIGIR 201
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