Accelerating Text Mining Workloads in a MapReduce-based Distributed GPU Environment

• Scientific computations have been using GPU-enabled computers successfully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infrastructure. Since the initial steps of text mining are typically data intensive, and the ease of deployment of algorithms is an important factor in developing advanced applications, we introduce a flexible, distributed, MapReduce-based text mining workflow that performs I/O-bound operations on CPUs with industry-standard tools and then runs compute-bound operations on GPUs which are optimized to ensure coalesced memory access and effective use of shared memory. We have performed extensive tests of our algorithms on a cluster of eight nodes with two NVidia Tesla M2050s attached to each, and we achieve considerable speedups for random projection and self-organizing maps.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

GPU computing

MapReduce

Text mining

Self-organizing maps

Random projection

Library and Information Science

Library and Information Science

Biblioteks- och informationsvetenskap

Publikations- och innehållstyp

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